The Journal of Real Estate Finance and Economics

, Volume 40, Issue 4, pp 497–543

Is the Mean Return of Hotel Real Estate Stocks Apt to Overreact to Past Performance?

Authors

    • Finance, Real Estate, and Law DepartmentCalifornia State Polytechnic University, Pomona
  • Yongheng Deng
    • Institute of Real Estate StudiesNational University of Singapore
Article

DOI: 10.1007/s11146-009-9223-x

Cite this article as:
Zhang, M. & Deng, Y. J Real Estate Finan Econ (2010) 40: 497. doi:10.1007/s11146-009-9223-x
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Abstract

This study examines the return patterns of hotel real estate stocks in the U.S. during the period from 1990 to 2007.We find that the magnitude and persistence of future mean returns of hotel real estate stocks can be predicted based on past returns, past earnings surprise, trading volume, firm size, and holding period. The empirical evidence found from this paper confirms that short-horizon contrarian profits can be partially explained by the lead-lag effects, while in the intermediate-term price momentum profits and long-term contrarian profits can be partially attributed to the firms’ overreaction to past price changes. Our results support the contrarian/overreaction hypothesis, and they are inconsistent with the Fama-French risk-based hypothesis or the underreaction hypothesis. The study also confirms the earning underreaction hypothesis and finds the high volume stocks tend to earn high momentum profits in the intermediate-term. The study finds that the earning momentum effect for hotel stocks is more short-lived and smaller in magnitude than the market average. Price momentum portfolios (or contrarian portfolios) of big hotel firms underperform small hotel firms and the hotel price momentum portfolio (or contrarian portfolios) significantly underperform the overall market over the intermediate-term (or the long-term). These findings imply that the U.S. hotel industry, particularly the big hotel firms, have experienced relatively conservative growth in the sample period. It suggests that a conservative hotel growth strategy accompanied by an internal-oriented financing policy is proper in a period of prosperity.

Keywords

HotelStock return patternOverreact

Introduction

The U.S. lodging (hotel) industry revenue increased to $133.4 billion in 2006, from $122.7 billion in 2005, representing about 1% of the country’s GNP, and generated $26.6 billion in pretax profits (American Hotel & Lodging Association 2007). In 2006, the hotel industry provides 1.87 million1 employment opportunities, accounting for 1.2% of the aggregate U.S. employment. The historical data of the hotel industry indicates a cyclical pattern. Choi et al. (1999) find that the mean duration for contraction is 1.7 years, and 5.7 years for expansion, which implies that investors and developers tend to be over-optimistic. Many studies, for example, Vogel (2001), and Powers and Barrows (2002), report that the hotel industry is more sensitive to the fluctuating market demand than other sectors. Lundberg et al. (1995) point out that the hotel industry, similar to other heavily capitalized industries such as real estate and financial service sector, tends to oversupply in prosperity or when there is other positive information, and encounters heavy losses during the subsequent economic recession because of “too many rooms in the inn”. The problem has been exaggerated when hotel companies were over-leveraged. For example, lodging companies expanded dramatically in the 1980s, and their financial problems were serious from the middle of the 1980s to the beginning of 1990s (Vogel 2001).

Since the stock market serves as the source of capital and stock price reflects the market expectation, if the hotel industry is prone to overinvestment and its stock IPOs are demand-driven by the underling, a logical deduction is the hotel stock return will overreact to its previous price information and demonstrate intermediate-term momentum and long-term reversal patterns.

This study looks directly from the hotel stock market data during the period from 1990 to 2007. In this period, two important factors might change the relationship between stock returns and their past performance of hotel industry. One is the ownership interest of both direct equity investment and especially real estate investment trusts (REITs) in hotel real estate had been growing fast during 1990s. The market capitalization of America’s hotel REITs rose to $19.4 billion in the first quarter of 1998 from only $142.4 million in 1993 when there were only two U.S. lodging REITs, according to Real Estate Weeks.2 This amount accounts for 42.6% of all hotel stocks’ market capitalization. By the end of 1998, there are 16 hotel REITs and 51 non-REITs hotel corporations traded in the capital market (Mooradian and Yang 2001). All the hotel REITs are powerful industry players. According to Mueller and Jan de Beur (1999), 5% of hotel properties were owned by REITs by the end of 1999. Although the hotel REITs IPO wave cools down recently, the REITs still plays an important role as a source of capital for underlying hotel real estate.

Another important factor is that mergers, acquisition, and joint ventures changed the competitive environment of the lodging sector in the U.S., from the end of twentieth century to the beginning of the twenty-first century. For example, Starwood acquired Westin for US$ 1.6 billion in 1997, Starwood bought ITT Sheraton for US$ 13.7 billion in 1998, and Hilton acquired Promus group for US$ 41 billion in 1999 (Vogel 2001). Hotel chains account for a large percentage of the U.S.’s hotel room inventory. In 1999, the number of the rooms of largest 25 hotel chains, such as Bass, Marriott International, Hilton Hotel Corporation, and Starwood Hotels & Resorts, was 2.4 million, or about 70% of the U.S. market (Angelo and Vladimir 2001).

Given the REITs IPO boom together with the hotel real estate industry concentration, it is important to have a better understanding of the underlying hotel real estate industry market characteristics through investigating its stock price behaviors. In particularly, are hotel real estate firms, especially big firms, still relatively more prone to overinvestment than the overall stock market?

This study provides new insights into the relationship between stock returns and past firm performance for the hotel real estate industry in the U.S. based on the Lehmann (1990) and Jegadeesh and Titman (1993, 2001)’s frameworks, this study utilizes the most critical explanatory variables to investigate the determinants of the contrarian or momentum profits of the hotel real estate industry. The study finds that the magnitude and persistence of future average returns of hotel real estate stocks can be predicted based on past returns, past earning surprises, trading volume, firm size, and holding period. The evidence of this paper strongly confirms that short-horizon contrarian profits can be partially explained by the lead-lag effects, while in the intermediate-term price momentum profits and long-term contrarian profits can be partially attributed to the firms’ overreaction to past price. The study also confirms the earning underreaction hypothesis and finds the high volume stocks tend to earn high momentum profits in the intermediate-term.

As expected, the study finds that the earning momentum effect for hotel stocks is more short-lived and smaller in magnitude than for the whole market on average. Possible explanation is that products and services of hotel industry are highly perishable and intangible. Near term financial performance information such as earnings of hotel stocks are more accessible in a timely matter, and therefore be more quickly reflected into the prices than what could be done for other industries.

Contradicts to our expectation, the empirical study also finds that price momentum portfolios (or contrarian portfolios) of big hotel firms underperform small hotel firms and the hotel price momentum portfolio (or contrarian portfolios) significantly underperform the overall market over the intermediate-term (or the long-term). It could be possibly caused by big hotel REITs which are less likely to overinvest because the dividend policy of REITs together with their more limited free cash flow, mitigate the oversupply of the hotel industry, particularly big firms, compared with the overall stock market. Meanwhile, less cash flow means less cash flow momentum in the intermediate-term and reversion in the long-term future according to Yoshida (2008), which also explains a less predictable price pattern. Another possible reason is that learning from the lesson of the 1980’s hotel oversupply and financial jeopardy, the capital market might exercise more caution to check on the management of hotel firms who has the incentive to overbuild or overpay for assets, then reduce the risks of overinvestment, hence reduce the momentum effect of mean returns for hotel stocks.

Furthermore, the study finds the evidence of the segmentation in terms of contrarian or momentum profits between hotel real estate industry and overall. This finding implies that a conservative hotel growth strategy accompanied by an internal-oriented financing policy is appropriate in a period of prosperity.

Literature Review

Large volume of existing studies indicates there are many average stock return patterns which can not be explained by the CAPM and APT. Particularly, many recent studies document patterns of the predictability of average stock returns after the findings of long-term reversal (De Bondt and Thaler 1985, 1987), short-term reversal (Jegadeesh 1990; Lehmann 1990), and intermediate-term momentum (Jegadeesh and Titman 1993) in average stock returns. These researches find that the magnitude and persistence of future excess returns can be predicted based on past performance (returns, earnings, trading volume, analyst coverage, etc.) and firm characteristics (firm size, book-to-market ratio, etc).

For instance, Lehmann (1990) suggests the short-horizon “contrarian trading strategy”—selling the securities that have performed well and buying the securities that have performed poorly will earn positive profits. Campbell et al. (1993), Blume et al. (1994), and Conrad et al. (1994) also report that there is strong evidence of short-term price reversals, particularly for high-transaction securities. Working on the long-horizon data, De Bondt and Thaler (1985, 1987) find stocks with low long-term past returns tend to outperform winners over the subsequent 3–5 years. Poterba and Summers (1988) and Fama and French (1988) also find mean returns reversion in long horizon. In the intermediate time horizon, the empirical puzzle is not return reversal but return continuation. Jegadeesh and Titman (1993) document a horizon with 3–12 months of “momentum” in stock prices—past winners on average continues to outperform past losers. The result is supported by the tests of Rouwenhorst (1998) and Jegadeesh and Titman (2001). Chan et al. (1996) propose the concept of an “earning momentum” strategy to refer to the investment strategy based on past earnings-related information.

Many explanations have been proposed to account for these patterns. As for short-horizon predictability, Lo and Mackinlay (1990) Conrad and Kaul (1998), and Moskowitz and Grinblatt (1999) find lead-lag effects (returns of large stocks lead those of smaller stocks) can explain short-term reversals. Kaul and Nimalendran (1990) and Jegadeesh and Titman (1993) document that short-horizon excess profits may also be caused by bid-ask spread. The source of the intermediate-term momentum strategy excess profits and the interpretation of the evidence are widely debated. The explanatory theories can be classified as two categories—behavior models and risk-based models. Behavior models imply that the holding period momentum profits arise because of an overreaction or underreaction to information that further pushes the prices of winners (losers) up (down) in the subsequent intermediate-term, say 3–12 months. The “overconfidence bias” hypothesis of Daniel et al. (1998), and the “positive feedback trader” model of DeLong et al. (1990) and “overinvestment-financial problem” hypothesis of Zhang (2002) can be listed in the overreaction subset. These overreaction models also predict long-term price reversals as an error correction to the previous intermediate-term price overreaction. In the subset of the underreaction hypotheses, some studies, such as Brown et al. (1988), Bernard and Thomas (1990), and Chan et al. (1996), suggest that investors may underreact to past earnings or price and that a momentum strategy may produce excess profits. The “conservatism bias” hypothesis of Barberis et al. (1998), the “gradual-information-diffusion” model of Hong and Stein (1999) and Hong et al. (2000), fall into this subset. One important implication of the underreaction hypotheses is that the post-holding period returns will be zero whenever the information is fully reflected on the prices. Others (e.g., Conrad and Kaul (1998), Fama and French (1993, 1996)) have suggested a risk-based interpretation of momentum. Risk-based models suggest that the profitability of momentum strategies may simply be the compensation for risks. For example, Fama and French (1996) argue that if the risk premium of the three stock-market factors is considered, the price reversal in the short and long-horizon largely “disappears” in the regression residuals. Although Fama-French risk-based models are certainly a logical possibility, there is little evidence in favor of such a risk theory. For example, Jegadeesh and Titman (2001) examine the risk-adjusted returns and still find a negative long-term reversal profit. Carhart (1997), Moskowitz and Grinblatt (1999) and Cochrane (1999) argue that most of the momentum profits come from short positions in small illiquid stocks, therefore momentum strategies cannot yield exploitable profit after taking into account high transaction cost of small stocks and short-sales.

As for the explanations of long-horizon reversals, many studies, including those of De Bondt et al. (1985, 1987), Chopra et al. (1992), and Jegadeesh and Titman (2001), Zhang (2002) support the concept of market overreaction. Other competing explanations include “microstructure biases” hypothesis of Ball et al. (1995), “upward bias in cumulating single-period returns” hypothesis of Conrad and Kaul (1993), “book-to-market equity effect” hypothesis proposed by Chan et al. (1991), Lakonishok et al. (1994) and Fama and French (1995), and “cash flow reversion” hypothesis of Yoshida (2008).

Data and Methodology

This paper focuses on following five issues. First, the study examines the predictability (contrarian or momentum effect) of hotel stock returns in different horizons—short horizon (1 week to 1 month), intermediate horizon (3 months to 12 months), and long horizon (13 months to 60 months)—based on past return and past trading volume or firm size.

Second, using the “earning momentum/contrarian” strategy the study provides more evidence to evaluate market efficiency and to explain stock return predictability in a special way. As reported by previous studies (Bernard and Thomas (1990), and Chan et al. (1996)), it is natural to look at earnings to try to understand movements in stock prices because the predictability of stock average return is largely due to the component of returns that is related to this earnings-related information. Thus, it is widely believed that earnings are one of the driving forces for return momentum behavior in these studies.

Third, the existing literature have not reached the consensus whether the hotel stocks and the whole stock market has overreaction, underreaction to past information, behave as those in Conrad and Kaul (1998) risk-based hypothesis, or can be explained by Fama-French model. The study builds upon the previous literature to test various theories explaining predictability in stock returns.

Furthermore, the paper studies the impacts of unique characteristics of the hotel industry on hotel stock performance. Historical data before 1990 indicates that hotel industry particularly big hotel firms tended to take more aggressive expansion/overreaction than other industries and small hotel firms. It would be interesting to examine whether intermediate-term price momentum strategies for big capitalization stocks are more profitable than those of small stocks in the period 1990–2007. Another characteristic of the hotel industry is that its products and services are highly perishable and intangible; production, delivery, and consumption take place simultaneously. Thus, hotel firms’ near-term earnings could be more precisely expected by analysts and investors and thus be partially reflected in hotel firm stock prices before the date of next quarter earning disclosure. This property implies that the persistence and magnitude of the hotel earning momentum strategies and market earning momentum strategies will be different.

Last, since the organizational form of REITs differs from that of ordinary stocks, it is interesting to find out whether the bust of REITs since the middle of 1990s in hotel real estate industry implies greater or less momentum (or contrarian) profits.

Hotel real estate stocks in the U.S. comprise all hotel industry firms listed on the NYSE, AMEX, and NASDAQ during the period of January 1990 through December 2007. The sample of market portfolio is constructed from all stocks traded on the NYSE, AMEX, and NASDAQ during the same period. Hotel real estate firms are constituted of ordinary hotel stocks and hotel REITs. The selection of ordinary hotel stocks is based on the U.S. Census Bureau’s 1987 Standard Industrial Classifications (SIC) code system with SIC major industry group code 70 (Hotels, rooming houses, camps, and other lodging places). The selection of hotel REITs is based on the lodging and resorts sector of National Association of REITs (NAREIT) Constituents List.3 Return data, trading volume (number of transactions), and firm sizes (market capitalization) for individual securities are obtained from the Center for Research in Security Prices (CRSP). The study uses net income disclosed every quarter as the proxy of earnings data. Net income data are obtained from COMPUSTAT files. Table 1 reports the average daily returns and the number of stocks from 1990 to 2007 for the samples of price strategies. Table 2 reports the average net income (measure of earnings, in Million US$) and the number of stocks for earning strategies. Both tables illustrate that the mean rate of returns and mean earnings for market portfolio and hotel portfolio are closely related to the macro economic climate. For example, economic recession in the early 1990s, the Asia Financial Crisis in 1998, and the 9-11 terrorism attack in 2001 shocked returns and earnings of the overall stock market and hotel stocks.
Table 1

Summary statistics of daily returns data

Year

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Panel A: Hotel stocks (REITs included)

Number of stocks

49

43

46

52

68

74

85

91

86

75

62

61

52

49

54

54

53

45

Mean

−0.0008

0.0026

0.0029

0.0028

0.0005

0.0013

0.0010

0.0011

−0.0006

0.0005

0.0009

0.0002

0.0003

0.0014

0.0013

0.0003

0.0011

−0.0004

Standard derivation

0.067

0.068

0.089

0.052

0.044

0.046

0.044

0.039

0.044

0.041

0.043

0.042

0.038

0.030

0.023

0.026

0.021

0.026

Panel B: Hotel REITs

Number of stocks

3

3

3

5

11

12

14

15

16

17

17

17

17

19

22

23

23

18

Mean

−0.0029

0.0021

0.0018

0.0033

0.0002

0.0013

0.0014

0.0008

−0.0011

−0.0004

0.0009

0.0004

0.0001

0.0014

0.0011

0.0005

0.0011

−0.0001

Standard derivation

0.039

0.052

0.065

0.043

0.030

0.023

0.018

0.017

0.024

0.022

0.027

0.030

0.030

0.025

0.020

0.019

0.017

0.022

Panel C: Hotel stocks (REITs excluded)

Number of stocks

46

40

43

48

58

64

74

78

71

59

46

45

37

32

36

35

34

28

Mean

−0.0006

0.0026

0.0030

0.0027

0.0005

0.0014

0.0009

0.0012

−0.0005

0.0009

0.0009

0.0002

0.0003

0.0014

0.0014

0.0002

0.0011

−0.0005

Standard derivation

0.069

0.069

0.090

0.053

0.046

0.049

0.047

0.042

0.048

0.046

0.047

0.046

0.041

0.032

0.024

0.030

0.024

0.028

Panel D: Market stocks

Number of stocks

7292

7325

7620

8130

8705

9093

9693

9933

9800

9468

9174

8450

7750

7316

7185

7246

7321

7609

Mean

−0.0003

0.0024

0.0020

0.0018

0.0006

0.0017

0.0012

0.0012

0.0005

0.0016

0.00004

0.0013

0.0000

0.0024

0.0009

0.0003

0.0008

0.0000

Standard derivation

0.055

0.059

0.063

0.055

0.052

0.051

0.048

0.049

0.058

0.056

0.061

0.058

0.055

0.043

0.032

0.030

0.028

0.031

Table 2

Summary statistics of quarterly earning data

Year

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Panel A: Hotel stocks (REITs included)

Number of stocks

44

44

46

55

64

69

77

74

70

69

65

65

64

61

55

51

42

37

Mean

4.9

4.6

5.8

4.3

5.9

5.9

8.4

8.6

14.8

4.7

15.8

6.6

2.6

0.5

6.7

14.0

27.9

27.6

Standard derivation

55.1

43.3

32.3

26.4

27.9

30.6

33.0

50.1

85.1

97.1

141.9

59.4

58.0

42.6

49.8

60.6

80.5

52.1

Panel B: Hotel REITs

Number of stocks

3

3

4

9

12

14

16

16

16

17

16

18

20

22

21

22

20

18

Mean

5.4

7.8

6.7

1.8

0.3

−1.7

5.7

7.8

5.1

12.6

4.0

−2.5

−9.5

−7.1

−0.7

0.3

19.4

25.0

Standard derivation

23.9

10.7

11.3

10.7

4.4

14.9

12.8

10.1

37.5

42.1

50.3

34.6

59.5

42.5

17.7

37.0

48.6

43.0

Panel C: Hotel stocks (REITs excluded)

Number of stocks

42

42

43

47

53

56

62

59

55

53

50

48

45

40

35

30

22

19

Mean

4.8

4.3

5.7

4.7

7.0

7.7

9.6

9.3

16.7

2.3

17.7

8.3

5.0

4.3

11.0

25.5

35.4

29.7

Standard derivation

56.2

44.3

33.1

28.0

30.3

33.1

36.5

56.5

95.8

109.2

161.2

68.3

66.9

41.9

61.7

72.7

100.5

58.7

Panel D: Market stocks

Number of stocks

7776

8322

8684

9961

10595

11682

11833

11758

12306

12447

12164

11581

11031

10657

10348

10066

9376

8451

Mean

10.5

7.5

5.4

8.0

11.6

12.3

14.8

15.8

16.0

18.7

18.7

1.2

1.6

26.7

34.9

42.1

56.6

61.3

Standard derivation

82.8

75.5

151.8

106.2

75.8

107.0

104.6

123.6

199.6

148.2

204.0

495.5

499.7

274.0

278.5

396.1

390.4

382.1

Table 3 reports Fama-French Three-Factor regression statistics and Ljung-Box-Q statistics based on monthly return for an equally-weighted hotel stock portfolio. Risk-adjusted abnormal return (Alpha) is estimated using the Fama-French three-factor regression model:
$$ {\hat \alpha_{\text{i}}} = {\text{E}}\left( {{R_i}} \right) - {R_f} - {\hat \beta_i}\left[ {{\text{E}}\left( {{R_M}} \right) - {R_f}} \right] - {\hat s_i}{\text{E}}\left( {\text{SMB}} \right) - {\hat h_i}{\text{E}}\left( {\text{HML}} \right) $$
Table 3

Fama-French three factor regression and Ljung-Box-Q statistics based on monthly return for hotel stock portfolio

Panel A: Regression statistics summary

FF factors

FF factor loadings

T Stat.

P-Value

Alpha

−0.40

−1.55

0.12

Market (Beta)

1.03

0.48

0.63

SMB

0.70

9.12*

0.00

HML

0.87

9.20*

0.00

Panel B: Ljung-Box Q statistics

Variables

Q statistics

Q(4)

Q(8)

Q(12)

Q(16)

Q(20)

Hotel stock returns

16.33

21.85

24.03

26.33

27.83

(0.00)*

(0.00)*

(0.01)*

(0.03)*

(0.09)*

Residual (risk-adjusted returns)

2.66

5.42

7.70

10.81

15.68

(0.45)

(0.61)

(0.74)

(0.77)

(0.68)

Market factor

6.80

9.65

11.69

14.57

16.26

(0.08)*

(0.21)

(0.39)

(0.48)

(0.64)

SMB factor

6.54

7.88

10.60

16.17

22.06

(0.09)*

(0.34)

(0.48)

(0.37)

(0.28)

HML factor

2.66

5.42

7.70

10.81

15.68

(0.45)

(0.61)

(0.74)

(0.77)

(0.68)

This table reports the regression estimated for the Hotel stock portfolio in the U.S. The dependent variable is the monthly return in excess of the risk-free rate (treasure bill rate). The explanatory variables are the monthly returns from the Fama and French (1993) Research Factor portfolio for size and book-to-market factors and monthly return in excess of the Treasury bill rate on the equally-weighted market portfolio of all the component stocks from the Research Factor portfolio. In Panel A, FF factor loadings are the slope coefficient in Fama-French three-factor model time-series regressions. “T Stat.” is the T statistic. “Market” is the market factor (the value-weighted index minus the risk-free rate), “SMB” is the size factor (small stocks minus big stocks), and “HML” is the book-to-market factor (high minus low book-to-market stocks). “Alpha” is the intercept term or three-factor risk-adjusted abnormal return. The T statistics for market factor test the null that the loading is equal to 1. Panel B reports the Ljung-Box-Q statistics for dependent variable—monthly rate of return of Hotel stock portfolio, Fama-French three factors—Market, SMB, and HML, and residuals (or risk-adjusted rate of return). “Q(4)”, “Q(8)”, “Q(12)”, “Q(16)”, and “Q(20)” are the Ljung-Box-Q tests for up to fourth, eighth, twelfth, sixteenth, and twentieth month order. P-Values are shown in parentheses below the Ljung-Box Q Statistics. Statistics superscripted by * are significant at 10% level

Panel A of Table 3 reports that Alpha is positive but not significantly at the 10% level. Thus we cannot reject the null that the risk-adjusted return of hotel stock portfolio is statistically different from zero. The loadings of the SMB and HML are significantly different from zero. Market factor loading (Beta) is not significantly different from 1. It implies that the hotel stock portfolio behaves similarly to the whole market portfolio on a monthly return basis. Ljung-Box-Q statistics can be used to test whether a group of autocorrelations or cross-autocorrelations is significantly different from zero. Panel B reports the Q-statistics for three factors and the residual term for up to fourth, eighth, twelfth, sixteenth, and twentieth month order. The Q-statistics for the residuals falls above the upper boundary at 10% significance level for all orders, i.e., residuals were neither positively nor negatively auto-correlated.

This study includes critical explanatory variables to investigate the determinants of contrarian or momentum profits of the hotel real estate industry based on the short-term reversal portfolio strategy proposed by Lehmann (1990) and intermediate-term momentum portfolio strategy proposed by Jegadeesh and Titman (1993, 2001). The study employs weekly data in short-term study since much of the short horizon contrarian literature focuses on weekly interval and hence the results of this paper can be easily compared to others. Quarterly returns and earnings data are utilized for the intermediate-term and the long-term because earnings are only available on quarterly basis in COMPUSTAT file. For simplification, the study classifies the portfolios into two general sets of price strategies and earning strategies based on whether using R (past return) or E (past earning news).

For the tractability of the model, all stocks are equally-weighted within a given portfolio. All return data used in this study are the return above the risk-free rate of return (30 days U.S. Treasury Bill rate of return). The study uses the commonly used standard unexpected earnings (SUE) as the measure of earning news, such that
$$ SU{E_{iq}} = \frac{{{e_{iq}} - {e_{iq - 1}}}}{{{\sigma_i}}} $$
where eiq is quarterly earnings (net income) most recently announced as of quarter q for stock i, \( {e_{iq - 1}} \)is earnings one quarter ago, and σi is the standard deviation of unexpected earnings, \( {e_{iq}} - {e_{iq - 1}} \) over the period 1990 to 2007. The SUE model uses the assumptions of random walk and Martingale process.

At the beginning of each period (week for short-term; quarter for intermediate- and long-term) starting from January 1990, all stocks are sorted based on their past compound returns or average standard unexpected earnings (SUE) in the formation period K (previous week for short-term, previous 3 months, 6 months, 9 months, or 12 months for intermediate and long-term) and divided into three equally-weighted portfolios. R1 represents the loser portfolio with the lowest returns in the low 33.3% of the sample pool, R3 represents the winner portfolio with the highest returns in the upper 33.3%, and R2 represents the portfolio between the low 33.3% and the upper 33.3% during the previous K period. In the same manner, E1 represents the portfolio with the most unfavorable earning surprise (SUE) in the low 33.3% of the sample pool, E3 represents the portfolio that have delivered the most favorable earning surprises (SUE) in the upper 33.3%, and E2 represents the portfolio between the low 33.3% and the upper 33.3% during the previous formation period. This study refers to the strategies of long winners (losers) and short losers (winners) based on past returns as “price momentum (contrarian)” strategies R3-R1 (R1-R3), and those based on past earnings surprises as “earning momentum (contrarian)” strategies E3-E1 (E1-E3).

The research indicates J as holding periods where J = 1 week, 2 weeks, or 4 weeks for short-term strategies; J = 3 months, 6 months, 9 months, or 12 months for intermediate-term; and J = 36 months, 48 months, or 60 months for long-term strategies.

As suggested by Lee and Swaminathan (2000), trading volume used in this paper is the number of transactions. Following Jegadeesh and Titman (1993, 2001) this study uses market capitalization as the measure of firm size. Using the mean value as breakpoints, firm sizes and formation period trading volume are divided into two categories. V1 represents the lowest trading volume portfolio, and V2 represents the highest trading volume portfolio. The smaller firms are in size class C1, and the larger firms are in C2. Therefore, V2-V1 (C2-C1) represents the portfolio of long the V2 (C2) and short the V1 (C1) portfolio at the same time.

Taken together, the stocks are grouped together to form a portfolios based on the four explanatory variables (J, K, R/E, V/C). While the most of previous studies employed 2 or 3 explanatory variables in their research model, the study integrates at most four variables into a single portfolio.

To increase the power of the tests, the study constructs special overlapping portfolios as suggested by Jegadeesh and Titman (1993). A momentum (contrarian) portfolio in any particular week (for short-term) or quarter (for intermediate-term and long-term) holds stocks ranked in that portfolio in any of the alive previous K formation period. For example, in the intermediate-term J = 12 K = 3 months analysis, in the fourth quarter in 1995 the winner portfolio comprises equal percentage (25%) of the R3 stocks format on the first day of January 1995, on the first day of April 1995, on the first day of July 1995, and on the first day of October 1995, respectively (which will be held to the last day of December 1995, the last day of March 1996, the last day of June 1996, and the last day of September 1996, respectively).

Because the COMPUSTAT database only offers quarterly earnings data, in short horizon, only price contrarian strategies will be used in the analysis. In the intermediate and long horizon, both return and earning momentum (contrarian) series strategies are investigated.

In the short-term, mean monthly holding period returns are employed for periods following the portfolio formation. In intermediate- and long-term study, annual holding period returns (annualized rate of return on holding period average basis) are computed.

To provide additional evidence on the source of the profits of various portfolio investment strategies, the Fama-French three-factor model (Fama and French 1993) are used. Risks premium due to market factor (Market), book-to-market equity ratio (HML) factor, and size (SMB) factor will be adjusted from the original portfolio returns. Throughout the paper, we use the convention that statistics must have two-tailed P-values less than 0.10 to be termed significant. Thus, a P-value lowers than 0.10 implies a significant statistical difference.

Empirical Results

Short-Term Price Contrarian Strategies

This section discusses the results for the basic contrarian portfolio and two advanced contrarian strategy portfolios based on past trading volume or firm size over short-term. Fama-French-Three-Factor risk-adjusted returns and the lead-lag hypothesis (Lo and Mackinlay 1990) are also discussed.

Basic Price Contrarian Strategy

This subsection gives the general view of the short-term contrarian strategies. Table 4 summarizes mean monthly stock returns of price contrarian strategy portfolios for the hotel stocks and the whole market. The associated T statistics are shown to test whether the returns are reliably different from zero.
Table 4

Mean monthly returns of price contrarian strategy for hotel stock and market portfolio

Portfolio

Hotel portfolio

Market portfolio

J = 1

J = 2

J = 4

J = 1

J = 2

J = 4

Week

Week

Week

Week

Week

Week

R1

0.046

0.024

0.015

0.045

0.027

0.017

(8.37)**

(6.37)**

(5.43)**

(11.10)**

(8.46)**

(6.80)**

R3

−0.018

−0.006

0.000

−0.014

−0.004

0.002

(−4.23)**

(−1.94)**

(0.12)

(−4.36)**

(−1.80)*

(0.86)

R1-R3

0.063

0.029

0.014

0.056

0.030

0.015

(10.90)**

(8.26)**

(6.19)**

(24.70)**

(18.40)**

(12.00)**

R1-R3 represents the combined portfolio of long the R1 portfolio and short the R3 portfolio at the same time. T statistics are shown in parentheses below the returns values. Statistics superscripted by * are significant at 10% level and ** are significant at 5% level for two-tailed T-tests

The table illustrates that the mean return is negative for winners and positive for losers in all holding periods. Both winners and losers experience fast price reversals. The results in the last two rows indicate that the profits of the contrarian portfolios are significantly positive at the 5% level. For instance, buying previous week hotel losers and selling previous week hotel winners, and holding the contrarian portfolio for 1 week will earn 6.3% monthly return. The results are highly consistent with findings in previous studies (e.g. Lehmann (1990), Conrad et al. (1991) and Jegadeesh (1990)). The results show that holding the contrarian portfolios for 1 week will earn the highest contrarian returns, however, the contrarian profits drop fast in the 2-week and 4-week holding periods. It is worthy to note that although returns of a hotel stock contrarian portfolio are slightly higher than those of a market contrarian portfolio in the first week, there is no big difference between them over the 2-week and 3-week holding periods.

Volume-Based Price Contrarian Strategy

This subsection introduces the trading volume as an explanatory variable and examines its impacts on the predictability of contrarian portfolio. Table 5 reports monthly returns of hotel and market portfolios formed on the basis of a two-way analysis between price contrarian and past trading volume. Table values represent the mean monthly returns over the next holding period J weeks (J = 1, 2 or 4).
Table 5

Mean monthly returns of price contrarian strategy based on past return and past trading volume for hotel stock and market portfolio

Portfolio

J = 1 Week

J = 2 Week

J = 4 Week

R1

R3

R1-R3

R1

R3

R1-R3

R1

R3

R1-R3

Panel A: Hotel portfolio

V1

0.059

−0.023

0.077

0.032

−0.012

0.044

0.016

−0.003

0.018

(8.09)**

(−3.69)**

(8.99)**

(7.00)**

(−3.03)

(7.97)**

(4.78)**

(−1.21)

(4.94)**

V2

0.034

−0.013

0.046

0.019

−0.003

0.020

0.015

0.002

0.013

(4.43)**

(−2.35)**

(5.98)**

(3.68)**

(−0.65)

(4.29)**

(3.88)**

(0.60)

(3.69)**

V2-V1

−0.025

0.009

−0.034

−0.013

0.010

−0.023

−0.001

0.005

−0.006

(−2.68)**

(1.28)

(−3.01)**

(−2.35)**

(1.98)**

(−3.24)**

(−0.29)

(1.49)

(−1.11)

Panel B: Market portfolio

V1

0.053

−0.026

0.075

0.030

−0.011

0.040

0.019

−0.001

0.019

(17.60)**

(−10.60)**

(37.90)

(12.43)**

(−5.28)**

(30.30)**

(9.35)**

(−0.63)

(20.60)**

V2

0.038

−0.005

0.042

0.025

0.000

0.024

0.016

0.004

0.012

(7.61)**

(−1.23)**

(15.70)

(6.31)**

(0.04)

(11.90)**

(5.25)**

(1.65)*

(7.76)**

V2-V1

−0.015

0.022

−0.037

−0.006

0.011

−0.017

−0.003

0.005

−0.008

(−5.93)**

(9.59)**

(−19.17)**

(−2.93)**

(6.80)**

(−11.89)**

(−2.24)**

(4.22)**

(−8.17)**

Contrarian portfolio R1-R3 represents the combined portfolio of long losers R1 and short winners R3. V2-V1 represents the combined portfolio of long high volume V2 and short low volume portfolio V1. T statistics are shown in parentheses below the returns values. Statistics superscripted by * are significant at 10% level and ** are significant at 5% level for two-tailed T-tests

Several important results are found for both hotel and market portfolio. First, both low volume losers and winners experience faster reversals than their high volume counterparts. Therefore, the contrarian portfolios of the low volume stocks significantly outperform that of the high volume stocks. This could be illustrated by negative values in the cells crossed by column (R3-R1) and row (V2-V1). This finding is not consistent with previous studies such as Campbell et al. (1993) and Conrad et al. (1994). For example, Campbell et al. claim, “Price changes accompanied by high volume will tend to be reversed; this will be less true of price changes on days with low volume”. Second, although the low volume portfolios do better in a contrarian portfolio (R1-R3), both high and low trading volume contrarian portfolios can earn significant positive profits. Also, similar with the general contrarian strategy, the contrarian profits of volume-based price contrarian strategy decrease when holding period becomes longer. Finally, in general there is no big difference in magnitude of contrarian profits between hotel stocks and market portfolio. Although the difference between big volume and small volume contrarian portfolio (V2-V1 × R3-R1) in J = 4 weeks case for hotel stocks is not statistically significant from zero, it could be partially explained by a much larger variance of hotel stocks compared with the market portfolio.

These evidences suggest that the magnitude and persistence of mean stock return can be predicted based on trading volume as stated by Conrad et al. (1991) and Conrad et al. (1994). “Traders can learn valuable information about stocks by observing both past price and past volume information.” Low trading volume is associated with smaller stocks, and hence we contribute the high contrarian profits of low volume stocks to the liquidity hypothesis suggested by Lee and Swaminathan (2000); firms with relatively low trading volume are less liquid in short horizon, and thus command a higher expected return.

Size-Based Price Contrarian Strategy

This subsection examines the impact of firm size on the predictability of the contrarian portfolios. Table 6 reports returns of hotel and market portfolios formed on the basis of past return and firm size. Several key results are found. First, small firms tend to experience faster price reversals. Therefore, small losers C1R1 earn the highest return and small winners C1R3 earn the lowest. As a consequence, the contrarian portfolio (R1-R3) of small firms significantly outperforms that of large firms over all holding periods for both market and hotel portfolio. This evidence illustrates that firm size can predict contrarian profits in a short horizon. Second, significant positive profits in the contrarian portfolio are found for small firms as well as for large firms. Third, the contrarian returns decay quickly in 2-week and 4-week holding periods.
Table 6

Mean monthly returns of price contrarian strategy based on past return and firm size for hotel stock and market portfolio

Portfolio

J = 1 Week

J = 2 Week

J = 4 Week

R1

R3

R1-R3

R1

R3

R1-R3

R1

R3

R1-R3

Panel A: Hotel portfolio

C1

0.063

−0.028

0.088

0.034

−0.015

0.049

0.020

−0.005

0.025

(8.27)**

(−4.10)**

(9.07)**

(6.49)**

(−3.37)**

(7.84)**

(5.35)**

(−1.48)

(5.95)**

C2

0.023

−0.007

0.029

0.014

0.001

0.012

0.008

0.004

0.004

(4.05)**

(−1.50)

(5.71)**

(3.34)**

(0.32)

(3.22)**

(2.75)**

(1.64)*

(1.55)

C2-C1

−0.039

0.021

−0.061

−0.020

0.017

−0.037

−0.012

0.009

−0.021

(−4.78)**

(2.85)**

(−5.54)**

(−3.63)**

(3.31)**

(−4.93)**

(−3.10)**

(2.69)**

(−4.12)**

Panel B: Market portfolio

C1

0.062

−0.024

0.082

0.036

−0.009

0.044

0.022

0.000

0.023

(15.60)**

(−7.15)**

(33.70)**

(11.02)**

(−3.42)**

(27.30)**

(8.21)**

(−0.09)

(18.50)**

C2

0.021

−0.003

0.024

0.015

0.001

0.013

0.010

0.003

0.006

(4.65)**

(−1.02)

(9.59)**

(4.33)**

(0.23)

(7.27)**

(4.15)**

(1.88)**

(4.70)**

C2-C1

−0.039

0.020

−0.061

−0.022

0.009

−0.031

−0.012

0.004

−0.016

(−16.30)**

(8.55)**

(−28.43)**

(−11.80)**

(6.05)**

(−22.57)**

(−9.31)**

(3.21)**

(−18.04)**

Contrarian portfolio R1-R3 represents the combined portfolio of long losers R1 and short winners R3. C2-C1 represents the combined portfolio of long big size C2 and short small size C1. T statistics are shown in parentheses below the returns values. Statistics superscripted by * are significant at 10% level and ** are significant at 5% level for two-tailed T-tests

A possible explanation for the high contrarian profits of small stocks is that small stocks are hard to trade in the market thus needs a higher liquidity risk premium. Furthermore, due to their thin trading volumes, smaller stocks cannot be transacted at their prevailing market prices, and therefore are expensive to trade in several weeks interval. Therefore, it may not be possible to execute active trading strategies with small stocks although theoretically they offer higher profits.

Risk-Adjusted Returns of Contrarian Strategy

To provide additional evidence on the source of the Contrarian profits, the Fama-French three-factor model (Fama and French 1993) are utilized. Tables 7 and 8 summarize the returns and risk-adjusted returns for the basic and two advanced contrarian strategy portfolios based on past trading volume or firm size for both the hotel and general market. The formation period is 1 week.
Table 7

Risk-adjusted and non-risk-adjusted mean monthly returns for hotel price contrarian (R1-R3) strategy portfolios

Portfolio

Not risk-adjusted

Risk-adjusted

J = 1 Week

J = 2 Week

J = 4 Week

J = 1 Week

J = 2 Week

J = 4 Week

R1-R3

R1-R3

R1-R3

R1-R3

R1-R3

R1-R3

V1

0.075

0.040

0.019

0.070

0.037

0.018

(37.90)**

(30.30)**

(20.60)**

(38.60)**

(30.30)**

(20.40)**

V2

0.042

0.024

0.012

0.037

0.022

0.011

(15.70)**

(11.90)**

(7.76)**

(15.80)**

(11.90)**

(7.70)**

C1

0.082

0.044

0.023

0.075

0.041

0.020

(33.70)**

(27.30)**

(18.50)**

(34.20)**

(27.30)**

(18.10)**

C2

0.024

0.013

0.006

0.021

0.012

0.006

(9.59)**

(7.27)**

(4.70)**

(9.59)**

(7.02)**

(4.62)**

All

0.056

0.030

0.015

0.052

0.028

0.014

(24.70)**

(18.40)**

(12.00)**

(25.10)**

(18.50)**

(11.90)**

This table reports the mean monthly returns and Fama-French three-factor risk adjusted mean monthly abnormal returns for short-term hotel price contrarian strategy portfolio based on past trading volume, and firm size. Contrarian portfolio R1-R3 represents the combined portfolio of long losers R1 and short winners R3. “All” portfolio is the basic momentum portfolio not classified by firm size and trading volume. T statistics are shown in parentheses below the returns values. Statistics superscripted by * are significant at 10% level and ** are significant at 5% level for two-tailed T-tests

Table 8

Risk-adjusted and non-risk-adjusted mean monthly returns for market price contrarian (R1-R3) strategy portfolios

Portfolio

Not risk-adjusted

Risk-adjusted

J = 1 Week

J = 2 Week

J = 4 Week

J = 1 Week

J = 2 Week

J = 4 Week

R1-R3

R1-R3

R1-R3

R1-R3

R1-R3

R1-R3

V1

0.077

0.044

0.018

0.073

0.040

0.017

(8.99)**

(7.97)**

(4.94)**

(9.08)**

(7.87)**

(4.81)**

V2

0.046

0.020

0.013

0.042

0.017

0.010

(5.98)**

(4.29)**

(3.69)**

(5.93)**

(3.85)**

(3.16)**

C1

0.088

0.049

0.025

0.082

0.044

0.022

(9.07)**

(7.84)**

(5.95)**

(9.12)**

(7.58)**

(5.54)**

C2

0.029

0.012

0.004

0.026

0.011

0.003

(5.71)**

(3.22)**

(1.55)

(5.54)**

(3.17)**

(1.21)

All

0.063

0.029

0.014

0.058

0.027

0.013

(10.90)**

(8.26)**

(6.19)**

(10.80)**

(7.95)**

(5.85)**

This table reports the mean monthly returns and Fama-French three-factor risk adjusted mean monthly abnormal returns for short-term market price contrarian strategy portfolio based on past trading volume, and firm size. Contrarian portfolio R1-R3 represents the combined portfolio of long losers R1 and short winners R3. “All” portfolio is the basic momentum portfolio not classified by firm size and trading volume. T statistics are shown in parentheses below the returns values. Statistics superscripted by * are significant at 10% level and ** are significant at 5% level for two-tailed T-tests

If the profitability of contrarian strategies can be well explained by the three-factor model (Fama and French 1993), the estimated intercept coefficients of these regressions, which are interpreted as the risk-adjusted return of the portfolio relative to the three-factor model, will not be statistically different from zero. The results in Tables 7 and 8 indicate that risk-adjusted contrarian returns are slightly decreased but still significantly positive over all holding periods. This evidence is not compatible with the Fama and French’s hypothesis. Thus, something other than the market, size, and BE/ME factor largely explain the profits of contrarian portfolios.

Lead-Lag Hypothesis

The empirical results that the price contrarian strategy for small firms could earn higher contrarian profit than that for big firms suggest that short-horizon excess profits are possibly partially due to lead-lag effects—returns of large stocks lead those of smaller stocks (Lo and Mackinlay 1990).

Table 9 presents the Ljung-Box-Q statistics of autocorrelation or cross-autocorrelation of weekly mean excess returns for the hotel portfolio and the market portfolio. The first point to note is that the Q statistics in risk-adjusted return data are generally smaller than those of non-risk-adjusted return. This implies that the autocorrelations or cross-autocorrelations in non-risk-adjusted returns are partially explained by the autocorrelations of Fama-French three factors. But the fact that Q statistics of risk-adjusted returns are still significant at a 5% level except in “Small-Lead-Big” category, suggests that other factors also influence the autocorrelations and cross-autocorrelations. Second, the Q statistics of “Big-Lead-Small” (cross autocorrelations between previous return of big firms with lag period return of small ones) are statistically significant in risk-adjusted returns. This evidence implies that previous big firm returns have a significant impact on future returns of small firms. The third finding is that the “Big-Lead-Small” effect is relatively weaker for the hotel portfolio than for the market portfolio.
Table 9

Ljung-Box-Q statistics for autocorrelation and cross-autocorrelation of weekly mean returns for hotel stock and market portfolio

Autocorrelation or Cross-autocorrelation of

Not risk-adjusted

Risk-adjusted

Q(4)

Q(8)

Q(12)

Q(4)

Q(8)

Q(12)

Panel A: Hotel portfolio

Small

36.41

39.48

40.37

10.77

14.85

15.46

(0.00)**

(0.00)**

(0.00)**

(0.01)**

(0.04)**

(0.16)

Big

14.95

21.06

21.92

4.05

5.28

11.43

(0.00)**

(0.00)**

(0.02)**

(0.26)

(0.63)

(0.41)

Small-Lead-Big

4.63

11.08

5.75

4.73

11.67

6.38

(0.20)

(0.14)

(0.89)

(0.19)

(0.11)

(0.85)

Big-Lead-Small

79.86

87.13

92.99

12.05

14.18

16.93

(0.00)**

(0.00)**

(0.01)**

(0.05)**

(0.10)*

(0.00)**

Panel B: Market portfolio

Small

165.29

179.50

187.37

93.15

95.16

105.29

(0.00)**

(0.00)**

(0.00)**

(0.00)**

(0.00)**

(0.00)**

Big

14.51

25.99

39.71

12.94

20.33

27.00

(0.00)**

(0.00)**

(0.00)**

(0.00)**

(0.00)**

(0.00)**

Small-Lead-Big

7.76

18.06

35.00

3.44

9.73

12.88

(0.05)*

(0.08)*

(0.00)**

(0.33)

(0.55)

(0.30)

Big-Lead-Small

145.04

162.29

174.33

28.68

29.93

36.43

(0.00)**

(0.00)**

(0.00)**

(0.00)**

(0.00)**

(0.00)**

This table reports the Statistics summary of the Ljung-Box-Q test for hotel stock portfolio (presented in Panel A) and market portfolio (presented in Panel B) in the U.S. “Small” represents small firms. “Big” represents big firms. Return data are in weekly frequency. “Small-Lead-Big” examines the cross autocorrelation between past return of small firms with lag period return of big firms; and “Big-Lead-Small” examines the cross autocorrelation between past return of big firms with lag period return of small firms. “Risk-Adjusted” represents that the returns of small and big firms are risk-adjusted by Fama-French three-factor model. “Q(4)”, “Q(8)”, and “Q(12)”, are the Ljung-Box-Q tests for up to fourth, eighth, twelfth order autocorrelation. P-Values are shown in parentheses below the Ljung-Box Q Statistics. Statistics superscripted by * are significant at 10% level and ** are significant at 5% level for two-tailed T-tests

Intermediate- and Long-Term Momentum and Contrarian Strategies

At the intermediate-term ranging from 3–12 months, the empirical puzzle is not return reversal but return continuation. Some finance studies also find the long-term price reversal up to 5 years after the events. Therefore, this study extends the holding period to 5 years.

Basic Price and Earning Strategies

Table 10 presents mean annual returns for basic price momentum strategy portfolios based on different formation periods. In the past two decades, momentum strategies have become more popular among institutional investors. One might expect that the frequent trading activities of these institutions would eliminate the momentum effect in intermediate-term and the reversal effect in long-term, at least for the large stocks that they can trade at low transaction cost. However, consistent with previous studies, Table 10 reveals that in the recent period from 1990 to 2007, the results for both the general market portfolio and hotel stocks confirm price momentum in holding period from 3–12 months and price reversal in the long-term from 3–5 years. Furthermore, Table 10 also presents the difference between hotel stocks and the overall market portfolio.
Table 10

Mean annual returns of intermediate- and long-term price momentum strategy for hotel stock and market portfolios

Portfolio

Hotel portfolio

Market portfolio

 

 

J = 3

J = 6

J = 9

J = 12

J = 36

J = 48

J = 60

J = 3

J = 6

J = 9

J = 12

J = 36

J = 48

J = 60

K

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

3 Mon

R1

0.122

0.078

0.084

0.075

0.130

0.133

0.145

0.120

0.105

0.116

0.137

0.174

0.164

0.159

(1.39)

(1.34)

(1.74)*

(1.90)*

(3.85)*

(4.20)*

(4.03)*

(1.49)

(1.95)*

(2.75)*

(3.68)*

(9.14)*

(10.34)*

(10.25)*

R3

0.053

0.091

0.106

0.123

0.117

0.130

0.115

0.142

0.153

0.171

0.177

0.139

0.130

0.133

(0.80)

(1.89)*

(2.43)*

(3.29)*

(4.59)*

(3.79)*

(3.11)*

(2.47)*

(3.60)*

(4.64)*

(5.12)*

(8.87)*

(10.17)*

(10.32)*

R3-R1

−0.064

0.012

0.021

0.048

−0.018

−0.005

−0.048

0.020

0.046

0.053

0.040

−0.050

−0.055

−0.050

(−0.83)

(0.26)

(0.60)

(1.47)

(−0.59)

(−0.10)

(−1.00)

(0.45)

(1.63)*

(2.30)*

(1.76)*

(−3.24)*

(−3.31)*

(−2.67)*

6 Mon

R1

0.110

0.082

0.091

0.093

0.135

0.126

0.115

0.100

0.099

0.122

0.155

0.185

0.172

0.166

(1.14)

(1.42)

(1.79)*

(2.33)*

(3.48)*

(3.46)*

(3.47)*

(1.20)

(1.87)*

(2.77)*

(3.72)*

(9.22)*

(9.88)*

(10.03)*

R3

0.058

0.110

0.129

0.146

0.146

0.134

0.148

0.171

0.187

0.192

0.176

0.130

0.124

0.131

(1.00)

(2.43)*

(3.63)*

(4.20)*

(5.17)*

(4.19)*

(4.31)*

(2.97)*

(4.36)*

(5.16)*

(5.35)*

(8.35)*

(9.69)*

(9.69)*

R3-R1

−0.048

0.027

0.037

0.053

0.014

0.017

0.045

0.066

0.083

0.068

0.021

−0.080

−0.082

−0.072

(−0.58)

(0.52)

(0.86)

(1.53)

(0.34)

(0.39)

(0.95)

(1.28)

(2.83)*

(2.45)*

(0.78)

(−4.32)*

(−4.07)*

(−3.09)*

9 Mon

R1

0.145

0.115

0.103

0.101

0.123

0.115

0.115

0.118

0.121

0.147

0.181

0.198

0.180

0.174

(1.45)

(1.82)*

(1.97)*

(2.39)*

(3.46)*

(3.12)*

(3.56)*

(1.38)

(2.20)*

(3.09)*

(3.95)*

(9.52)*

(10.76)*

(10.59)*

R3

0.097

0.138

0.141

0.147

0.140

0.144

0.145

0.199

0.202

0.184

0.167

0.121

0.123

0.130

(1.73)*

(3.29)*

(3.93)*

(4.43)*

(4.98)*

(4.26)*

(3.94)*

(3.67)*

(4.75)*

(5.21)*

(5.67)*

(7.90)*

(9.49)*

(8.47)*

R3-R1

−0.043

0.022

0.037

0.046

0.021

0.048

0.041

0.074

0.076

0.036

−0.014

−0.117

−0.100

−0.095

(−0.51)

(0.44)

(0.84)

(1.23)

(0.58)

(0.99)

(1.05)

(1.35)

(2.15)*

(1.13)

(−0.43)

(−5.45)*

(−4.95)*

(−3.57)*

12 Mon

R1

0.166

0.099

0.089

0.105

0.125

0.137

0.112

0.144

0.146

0.176

0.206

0.203

0.184

0.180

(1.51)

(1.58)

(1.79)*

(2.45)*

(3.32)*

(3.11)*

(2.99)*

(1.63)*

(2.51)*

(3.43)*

(4.30)*

(9.59)*

(10.50)*

(10.46)*

R3

0.136

0.159

0.166

0.168

0.155

0.156

0.177

0.194

0.176

0.161

0.150

0.116

0.118

0.123

(2.28)

(3.81)*

(4.33)*

(5.21)*

(5.32)*

(4.52)*

(4.04)*

(3.55)*

(4.30)*

(4.95)*

(5.29)*

(7.44)*

(9.61)*

(9.31)*

R3-R1

−0.026

0.058

0.075

0.063

0.038

0.053

0.090

0.046

0.028

−0.014

−0.056

−0.135

−0.120

−0.132

(−0.31)

(1.12)

(1.68)*

(1.59)

(0.95)

(1.10)

(2.00)*

(0.79)

(0.73)

(−0.40)

(−1.71)*

(−6.20)*

(−5.54)*

(−4.89)*

T statistics are shown in parentheses. Statistics superscripted by * are significant at 10% level for two-tailed T-tests

Table 11 presents mean annual returns for basic earning momentum strategy portfolios based on different formation periods. It indicates that, the news reflected in the past earnings announcement continues to leave its traces in the next several years holding period following the formation. Interestingly, the mean returns of the earning momentum portfolio, that is the spread in returns between stocks with delivered favorable surprises (E3) and those with unfavorable surprise (E1) is positive up to 5 years for both hotel stocks and market portfolio. However, this result is less significant for the hotel stocks, particularly in the long horizon.
Table 11

Mean annual returns of intermediate- and long-term earning momentum strategy for hotel stock and market portfolio

Portfolio

Hotel portfolio

Market portfolio

 

 

J = 3

J = 6

J = 9

J = 12

J = 36

J = 48

J = 60

J = 3

J = 6

J = 9

J = 12

J = 36

J = 48

J = 60

K

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

3 Mon

E1

0.084

0.097

0.116

0.096

0.097

0.094

0.077

−0.001

0.038

0.066

0.080

0.119

0.118

0.114

(1.35)

(2.22)*

(3.03)*

(2.96)*

(3.59)*

(3.54)*

(3.17)*

(−0.03)

(1.08)

(2.31)*

(3.18)*

(8.59)*

(9.70)*

(10.99)*

E3

0.154

0.128

0.120

0.135

0.105

0.088

0.071

0.218

0.198

0.186

0.188

0.149

0.140

0.135

(2.13)*

(2.69)*

(3.15)*

(4.11)*

(3.51)*

(3.22)*

(2.45)*

(3.72)*

(4.96)*

(5.75)*

(6.46)*

(9.96)*

(11.09)*

(12.44)*

E3-E1

0.062

0.034

0.009

0.037

0.011

−0.003

−0.002

0.220

0.157

0.119

0.108

0.036

0.030

0.032

(0.98)

(0.82)

(0.23)

(1.09)

(0.37)

(−0.11)

(−0.07)

(16.96)*

(15.70)*

(12.21)*

(13.04)*

(8.46)*

(7.06)*

(6.83)*

6 Mon

E1

0.139

0.128

0.092

0.071

0.064

0.054

0.043

−0.005

0.037

0.056

0.075

0.120

0.118

0.113

(2.42)*

(2.40)*

(2.32)*

(2.26)*

(2.47)*

(2.17)*

(1.81)*

(−0.09)

(1.05)

(1.97)*

(2.89)*

(8.40)*

(9.40)*

(10.28)*

E3

0.206

0.187

0.186

0.191

0.128

0.110

0.088

0.271

0.227

0.215

0.204

0.151

0.142

0.135

(3.01)*

(3.65)*

(5.03)*

(5.05)*

(4.15)*

(3.76)*

(2.67)*

(4.77)*

(5.99)*

(6.86)*

(7.30)*

(10.38)*

(11.21)*

(12.58)*

E3-E1

0.057

0.063

0.092

0.119

0.071

0.065

0.056

0.277

0.187

0.157

0.128

0.039

0.033

0.033

(0.94)

(1.07)

(2.12)*

(2.93)*

(2.50)*

(2.45)*

(1.73)*

(20.37)*

(18.04)*

(17.12)*

(16.92)*

(7.70)*

(6.86)*

(6.54)*

9 Mon

E1

0.057

0.051

0.053

0.057

0.089

0.092

0.088

−0.003

0.021

0.050

0.073

0.121

0.119

0.114

(1.07)

(1.10)

(1.41)

(1.76)*

(2.78)*

(2.76)*

(2.89)*

(−0.06)

(0.60)

(1.67)*

(2.71)*

(8.20)*

(9.18)*

(9.91)*

E3

0.172

0.187

0.167

0.178

0.112

0.095

0.071

0.277

0.245

0.221

0.207

0.150

0.142

0.138

(2.48)*

(3.87)*

(4.26)*

(4.70)*

(4.12)*

(3.76)*

(2.83)*

(4.94)*

(6.50)*

(6.99)*

(7.36)*

(10.56)*

(11.54)*

(12.63)*

E3-E1

0.115

0.133

0.112

0.120

0.030

0.004

−0.018

0.282

0.222

0.170

0.133

0.036

0.032

0.036

(2.07)*

(3.01)*

(3.15)*

(3.42)*

(1.12)

(0.14)

(−0.57)

(18.77)*

(19.78)*

(17.88)*

(16.22)*

(6.41)*

(5.50)*

(5.86)*

12 Mon

E1

0.006

0.004

0.001

0.024

0.070

0.079

0.079

−0.048

0.007

0.045

0.073

0.121

0.122

0.114

(0.10)

(0.07)

(0.03)

(0.69)

(2.52)*

(2.79)*

(2.91)*

(−0.87)

(0.18)

(1.38)

(2.42)*

(7.81)*

(8.73)*

(9.52)*

E3

0.283

0.216

0.209

0.183

0.096

0.085

0.053

0.324

0.261

0.230

0.213

0.148

0.142

0.138

(3.69)*

(4.51)*

(5.18)*

(5.38)*

(3.97)*

(3.49)*

(2.54)*

(5.81)*

(6.85)*

(7.17)*

(7.41)*

(10.31)*

(11.09)*

(12.58)*

E3-E1

0.274

0.212

0.208

0.163

0.028

0.016

−0.025

0.385

0.253

0.183

0.139

0.034

0.029

0.037

(4.28)*

(4.90)*

(5.21)*

(5.29)*

(1.45)

(0.64)

(−0.89)

(21.70)*

(20.30)*

(16.27)*

(14.36)*

(5.03)*

(3.95)*

(5.62)*

T statistics are shown in parentheses. Statistics superscripted by * are significant at 10% level for two-tailed T-tests

The empirical results suggest that first, sorting on past return give rise to large profits in intermediate-term momentum and long-term contrarian portfolios for the whole market. Sorting on past earning surprise, earning momentum strategies are profitable for both the hotel stocks and the general market portfolio. This evidence strongly supports that average stock returns in different horizons can be predicted by past returns for the market portfolio and by past earnings for both the market and hotel stock portfolio. One interesting finding is that the mean returns for all holding periods are positive for both the winners and losers. This is because of the long bull market overlapping with the sample period during 1990 to 2007. (See Table 1)

Second, we find that two pieces of publicly available information—stocks’ prior return and prior earning surprise, help to predict future returns. Each of the momentum strategies is individually successful, and one effect is not subsumed by the other.

Third, the market price momentum portfolio experiences price revision and have significant negative momentum profits in 3-year to 5-year holding periods after portfolio formation. The evidence is important because it refutes the common presumption that price momentum is simply a market underreaction (Chan et al. 1996). Instead, the finding tends to support the overreaction hypothesis—at least a portion of the momentum profits is better characterized as an overreaction. However, this evidence is weak for the hotel portfolio. There are some possible explanations for this finding. For example, Cochrane (1999) attributes the intermediate-term momentum to large volatility of individual stocks and the long-term reversal to news on long-term future expected returns. But this interpretation still lacks a plausible economic interpretation. Recently, Yoshida (2008) argues that the predictability of mean stock prices might just reflect the predictability of cash flow even the asset pricing in the capital market is totally rational, thus the intermediate-term price momentum and long-term price reversal is possibly caused by the long-term mean reversion of cash flow. Zhang (2002) provides an explanation based on the overreaction behavior assumption. Since stock price reflects the market expectation of its underling value, if the market is over-optimistic about the prospect of one corporation, this corporation would more likely to overinvest first and experience financial problem in the long-run, then a logical deduction is its stock return will overreact to its pervious price information and demonstrates intermediate-term momentum and long-term reversal pattern.

Fourth, in general, the hotel portfolio demonstrates a slight price reversal in 3 months and after that a weak but long momentum pattern up to 5 years takes over. No long-term price reversal is found for hotel portfolio. This result means that price information are likely to be impounded in hotel stock prices in the intermediate- and long-term compared with the whole market. Yoshida (2008)’s “cash flow predictability” model and Zhang (2002)’s “overinvestment” hypothesis are helpful to explain the segmentation of momentum behaviors between the market portfolio and the hotel portfolio. Since the middle of 1990s, the rapid growth of REITs in hotel industry resulted in a greater reliance of REITs in hotel industry relative to the overall market. In 1990, there are three hotel REITs which only accounts for 6.1% of the total number of hotel stocks. In 1995, 2000, and 2005, this proportion increased rapidly to 16.2%, 27.4%, and 42.6%, respectively. While for the whole market, the number of REITs only accounts for a minor part (from 1.7% to 2.8%) of the total number of stocks during the observation period. To maintain REIT status, REITs must distribute 95% (90% after 2001) of their taxable income to shareholders. Given the different dividend policy, REITs generate much less cash flow and are less likely to overinvest and less predictable in cash-flow. Therefore REITs would exhibit weaker intermediate-term momentum and long-term reversal pattern than non-REIT stocks. Therefore, the hotel portfolio of which REITs account for a substantial part would produce less significant momentum returns.

Fifth, compared to the price momentum strategy, the profits associated with earning momentum strategies tend to persist for a longer period up to 5 years. This evidence confirm the underreaction hypothesis of Chan et al. (1996), that a market does not incorporate the news of past earnings promptly and indeed the adjustment is gradual, so that there are drifts in subsequent returns. But our evidence is not consistent with their idea that the market also sluggishly responds to past price information.

Furthermore, focusing on earning momentum portfolios, we get some interesting findings. Although earning information for market momentum portfolios is not likely incorporated into their prices in 5 years, earning momentum profits for hotel portfolios will disappear after 12 months since formation, except for the 6-month formation scenario (K = 6). The results show that past earning information has longer persistence and larger effect on the performance of the whole stock market compared with that on the performance of hotel stocks. This evidence indicates that in general the earning momentum effect of a market portfolio tends to be stronger and longer-lived than that of a hotel portfolio.

Finally, we also find that price news tends to have a larger impact in terms of magnitude on future long-term stock prices than past earnings news does. It is not surprising when we look back at the U.S. hotel industry’s overbuilding which started in the earlier 1980s and continued through the decade. In spite of the serious successive losses reported during that time investors and developers still dreamed big, build big, and profited royally (Lundberg et al. 1995). The stock price information overreaction dominated earning underreaction with the hotel overbuilding.

Volume-Based Price and Earning Momentum Strategies

Table 12 presents returns for hotel and market momentum portfolios based on past return and past trading volume. Several general empirical results are found.
Table 12

Mean annual returns for price momentum strategy portfolios based on past return and past trading volume

Portfolio

J = 3 Mon

J = 6 Mon

J = 9 Mon

J = 12 Mon

J = 36 Mon

J = 48 Mon

J = 60 Mon

K

R1

R3

R3-R1

R1

R3

R3-R1

R1

R3

R3-R1

R1

R3

R3-R1

R1

R3

R3-R1

R1

R3

R3-R1

R1

R3

R3-R1

Panel A: Hotel portfolio

3 Mon

V1

0.175

0.024

−0.133

0.072

0.084

0.012

0.105

0.112

0.007

0.095

0.125

0.030

0.109

0.069

−0.042

0.109

0.087

−0.031

0.111

0.044

−0.128

(1.91)*

(0.37)

(−1.34)

(1.12)

(1.83)*

(0.20)

(1.69)*

(2.29)*

(0.11)

(1.89)*

(2.97)*

(0.52)

(2.97)*

(2.09)*

(−0.86)

(3.11)*

(2.34)*

(−0.58)

(2.71)*

(1.49)

(−1.75)*

V2

0.086

0.065

−0.020

0.082

0.091

0.008

0.084

0.107

0.023

0.088

0.137

0.049

0.112

0.150

0.044

0.132

0.149

0.034

0.153

0.154

0.003

(0.80)

(0.86)

(−0.21)

(1.17)

(1.53)

(0.13)

(1.65)*

(2.13)*

(0.46)

(1.94)*

(2.90)*

(1.01)

(3.34)*

(4.69)*

(1.57)

(3.25)*

(3.80)*

(0.61)

(3.02)*

(2.89)*

(0.04)

V2-V1

−0.079

0.040

0.126

0.011

0.007

−0.004

−0.020

−0.005

0.016

−0.007

0.012

0.019

0.012

0.093

0.070

0.035

0.090

0.061

0.069

0.147

0.064

(−0.87)

(0.65)

(1.02)

(0.15)

(0.14)

(−0.04)

(−0.31)

(−0.09)

(0.17)

(−0.14)

(0.23)

(0.23)

(0.36)

(2.54)*

(1.41)

(0.76)

(1.98)*

(0.89)

(0.99)

(2.59)*

(0.75)

6 Mon

V1

0.086

0.049

−0.034

0.087

0.139

0.050

0.123

0.145

0.022

0.138

0.187

0.049

0.170

0.107

−0.095

0.156

0.083

−0.132

0.171

0.061

−0.303

(0.79)

(0.78)

(−0.32)

(1.18)

(1.95)*

(0.53)

(1.66)*

(2.85)*

(0.26)

(1.84)*

(3.39)*

(0.56)

(2.94)*

(3.34)*

(−1.21)

(3.04)*

(2.37)*

(−1.48)

(2.81)*

(2.04)*

(−1.74)*

V2

0.084

0.068

−0.015

0.067

0.096

0.028

0.063

0.116

0.053

0.084

0.127

0.044

0.111

0.167

0.069

0.093

0.162

0.090

0.066

0.185

0.122

(0.75)

(0.96)

(−0.16)

(1.04)

(2.01)*

(0.48)

(1.12)

(2.98)*

(1.05)

(1.65)*

(3.22)*

(0.95)

(2.96)*

(4.82)*

(1.70)*

(2.36)*

(4.40)*

(2.10)*

(1.93)*

(4.13)*

(2.53)*

V2-V1

−0.002

0.018

0.019

−0.019

−0.040

−0.021

−0.058

−0.028

0.030

−0.055

−0.059

−0.005

−0.089

0.075

0.146

−0.116

0.097

0.168

−0.252

0.141

0.214

(−0.02)

(0.26)

(0.16)

(−0.29)

(−0.61)

(−0.23)

(−0.75)

(−0.57)

(0.31)

(−0.68)

(−1.06)

(−0.05)

(−1.65)*

(1.74)*

(2.55)*

(−1.72)*

(2.15)*

(2.49)*

(−1.90)*

(2.52)*

(2.35)*

9 Mon

V1

0.121

0.075

−0.042

0.114

0.146

0.031

0.114

0.153

0.038

0.104

0.148

0.044

0.153

0.094

−0.097

0.151

0.065

−0.169

0.149

0.033

−0.250

(1.09)

(1.12)

(−0.37)

(1.72)*

(2.98)*

(0.45)

(1.68)*

(3.27)*

(0.52)

(2.07)*

(3.90)*

(0.76)

(3.48)*

(3.13)*

(−1.64)*

(2.81)*

(2.25)*

(−1.72)*

(2.72)*

(1.13)

(−1.82)*

V2

0.102

0.104

0.002

0.114

0.127

0.012

0.098

0.130

0.031

0.106

0.131

0.026

0.074

0.158

0.103

0.085

0.187

0.140

0.082

0.183

0.135

(0.87)

(1.65)*

(0.02)

(1.24)

(2.75)*

(0.15)

(1.32)

(3.19)*

(0.51)

(1.66)*

(3.42)*

(0.48)

(2.12)*

(4.63)*

(2.82)*

(1.91)*

(4.36)*

(2.50)*

(1.83)*

(4.07)*

(3.04)*

V2-V1

−0.017

0.028

0.045

0.000

−0.019

−0.019

−0.016

−0.023

−0.007

0.002

−0.017

−0.019

−0.123

0.086

0.178

−0.145

0.150

0.244

−0.208

0.162

0.233

(−0.16)

(0.44)

(0.38)

(0.00)

(−0.43)

(−0.21)

(−0.19)

(−0.50)

(−0.08)

(0.03)

(−0.42)

(−0.27)

(−2.18)*

(2.02)*

(3.18)*

(−1.67)*

(3.04)*

(3.33)*

(−1.43)

(2.95)*

(2.88)*

12 Mon

V1

0.211

0.183

−0.024

0.113

0.204

0.086

0.104

0.210

0.104

0.099

0.194

0.095

0.171

0.085

−0.131

0.159

0.059

−0.205

0.140

0.041

−0.140

(1.73)*

(2.35)*

(−0.21)

(1.68)*

(4.12)

(1.16)

(1.54)

(4.02)*

(1.33)

(2.08)*

(4.39)*

(1.66)*

(3.18)*

(2.27)*

(−1.69)*

(2.81)*

(1.85)*

(−1.97)*

(2.49)*

(1.22)

(−1.51)

V2

0.128

0.127

−0.001

0.115

0.149

0.032

0.100

0.155

0.054

0.114

0.158

0.043

0.091

0.179

0.106

0.105

0.194

0.149

0.079

0.209

0.145

(1.05)

(1.95)*

(−0.01)

(1.28)

(3.05)*

(0.46)

(1.39)

(3.63)*

(0.87)

(1.77)*

(4.40)*

(0.71)

(2.83)*

(5.42)*

(2.97)*

(2.49)*

(4.89)*

(3.25)*

(2.23)*

(4.25)*

(3.04)*

V2-V1

−0.072

−0.049

0.024

0.002

−0.050

−0.051

−0.004

−0.053

−0.049

0.016

−0.036

−0.052

−0.123

0.113

0.202

−0.173

0.155

0.247

−0.241

0.177

0.222

(−0.79)

(−0.73)

(0.23)

(0.03)

(−0.86)

(−0.64)

(−0.04)

(−0.93)

(−0.54)

(0.24)

(−0.72)

(−0.69)

(−2.25)*

(2.39)*

(3.44)*

(−2.05)*

(3.30)*

(3.63)*

(−1.79)*

(2.97)*

(2.44)*

Panel B: Market portfolio

3 Mon

V1

0.171

0.172

0.001

0.160

0.190

0.029

0.168

0.210

0.041

0.187

0.224

0.037

0.216

0.180

−0.055

0.203

0.164

−0.071

0.193

0.156

−0.082

(2.33)*

(3.41)*

(0.02)

(3.07)*

(4.94)*

(1.00)

(4.28)*

(5.95)*

(1.82)*

(5.27)*

(6.56)*

(1.55)

(9.73)*

(10.19)*

(−2.81)*

(9.87)*

(10.85)*

(−3.04)*

(10.17)*

(11.81)*

(−3.42)*

V2

0.062

0.126

0.061

0.052

0.130

0.076

0.069

0.145

0.075

0.091

0.145

0.054

0.138

0.110

−0.037

0.133

0.107

−0.040

0.132

0.117

−0.026

(0.71)

(1.89)*

(1.17)

(0.93)

(2.71)*

(2.52)*

(1.52)

(3.68)*

(3.14)*

(2.32)*

(4.05)*

(2.42)*

(7.43)*

(7.41)*

(−2.58)*

(9.55)*

(9.00)*

(−2.91)*

(9.02)*

(8.12)*

(−1.44)

V2-V1

−0.097

−0.041

0.060

−0.099

−0.055

0.047

−0.095

−0.062

0.034

−0.097

−0.079

0.017

−0.122

−0.101

0.016

−0.137

−0.098

0.026

−0.149

−0.076

0.041

(−2.51)*

(−1.24)

(1.72)*

(−4.44)*

(−2.32)*

(1.89)*

(−5.42)*

(−3.40)*

(1.81)*

(−6.99)*

(−4.72)*

(1.09)

(−6.70)*

(−7.35)*

(1.17)

(−6.70)*

(−6.67)*

(1.72)*

(−6.31)*

(−4.06)*

(2.61)*

6 Mon

V1

0.164

0.203

0.034

0.160

0.219

0.055

0.179

0.239

0.058

0.212

0.230

0.018

0.233

0.168

−0.104

0.215

0.155

−0.120

0.206

0.153

−0.132

(2.14)*

(4.05)*

(0.73)

(3.12)*

(5.78)*

(1.80)*

(4.32)*

(6.84)*

(1.99)*

(5.15)*

(6.60)*

(0.57)

(9.62)*

(9.96)*

(−4.50)*

(9.30)*

(10.48)*

(−4.13)*

(9.74)*

(11.61)*

(−4.13)*

V2

0.036

0.150

0.112

0.044

0.164

0.118

0.072

0.159

0.086

0.106

0.142

0.036

0.148

0.103

−0.061

0.140

0.103

−0.057

0.137

0.115

−0.039

(0.39)

(2.26)*

(2.00)*

(0.79)

(3.38)*

(4.02)*

(1.54)

(3.95)*

(3.23)*

(2.46)*

(4.14)*

(1.39)

(7.63)*

(6.75)*

(−3.51)*

(9.21)*

(8.43)*

(−3.53)*

(9.07)*

(7.61)*

(−1.83)*

V2-V1

−0.115

−0.046

0.076

−0.107

−0.049

0.061

−0.103

−0.076

0.027

−0.106

−0.088

0.017

−0.139

−0.093

0.035

−0.156

−0.085

0.045

−0.188

−0.074

0.059

(−2.85)*

(−1.41)

(2.35)*

(−4.84)*

(−2.06)*

(3.00)*

(−6.07)*

(−4.23)*

(1.74)*

(−7.17)*

(−4.81)*

(0.94)

(−6.56)*

(−7.11)*

(2.48)*

(−6.23)*

(−5.82)*

(2.71)*

(−6.73)*

(−4.12)*

(3.54)*

9 Mon

V1

0.184

0.228

0.039

0.183

0.243

0.055

0.203

0.235

0.030

0.237

0.215

−0.022

0.246

0.155

−0.152

0.223

0.155

−0.143

0.213

0.155

−0.157

(2.35)*

(4.77)*

(0.81)

(3.42)*

(6.62)*

(1.52)

(4.40)*

(7.07)*

(0.89)

(5.09)*

(7.69)*

(−0.64)

(9.59)*

(9.64)*

(−5.44)*

(9.88)*

(10.45)*

(−4.73)*

(10.27)*

(9.39)*

(−4.27)*

V2

0.055

0.178

0.118

0.066

0.173

0.103

0.099

0.150

0.050

0.135

0.135

0.000

0.162

0.097

−0.090

0.149

0.101

−0.076

0.146

0.113

−0.061

(0.59)

(2.83)*

(1.99)*

(1.15)

(3.54)*

(3.00)*

(1.99)*

(3.89)*

(1.59)

(2.88)*

(4.19)*

(0.01)

(8.01)*

(6.33)*

(−4.73)*

(10.02)*

(8.14)*

(−4.42)*

(9.80)*

(7.32)*

(−2.70)*

V2-V1

−0.113

−0.043

0.077

−0.107

−0.063

0.047

−0.100

−0.080

0.020

−0.103

−0.080

0.023

−0.140

−0.080

0.046

−0.158

−0.088

0.044

−0.190

−0.082

0.055

(−2.80)*

(−1.39)

(2.29)*

(−4.87)*

(−2.65)*

(2.50)*

(−5.72)*

(−4.35)*

(1.13)

(−6.24)*

(−4.69)*

(1.30)

(−5.89)*

(−5.99)*

(2.98)*

(−6.12)*

(−6.05)*

(2.48)*

(−6.77)*

(−5.13)*

(3.57)*

12 Mon

V1

0.207

0.240

0.029

0.207

0.227

0.018

0.233

0.215

−0.016

0.262

0.202

−0.060

0.251

0.153

−0.168

0.227

0.147

−0.175

0.219

0.146

−0.226

(2.63)*

(5.08)*

(0.57)

(3.66)*

(6.45)*

(0.48)

(4.64)*

(7.27)*

(−0.47)

(5.37)*

(7.72)*

(−1.66)*

(9.46)*

(9.52)*

(−5.73)*

(9.48)*

(11.59)*

(−5.55)*

(9.92)*

(11.66)*

(−5.86)*

V2

0.081

0.160

0.074

0.093

0.140

0.045

0.128

0.123

−0.005

0.160

0.113

−0.048

0.169

0.089

−0.114

0.154

0.098

−0.093

0.153

0.108

−0.088

(0.84)

(2.54)*

(1.17)

(1.54)

(3.03)*

(1.18)

(2.41)*

(3.43)*

(−0.15)

(3.26)*

(3.66)*

(−1.49)

(8.28)*

(5.85)*

(−5.69)*

(10.13)*

(7.66)*

(−4.87)*

(9.70)*

(7.36)*

(−3.77)*

V2-V1

−0.110

−0.069

0.045

−0.103

−0.078

0.026

−0.099

−0.087

0.011

−0.102

−0.090

0.012

−0.138

−0.088

0.039

−0.157

−0.078

0.050

−0.194

−0.071

0.062

(−2.65)*

(−2.37)*

(1.24)

(−5.14)*

(−3.64)*

(1.45)

(−5.93)*

(−5.05)*

(0.71)

(−6.08)*

(−5.70)*

(0.70)

(−6.06)*

(−7.46)*

(2.34)*

(−6.04)*

(−5.70)*

(3.01)*

(−6.36)*

(−4.66)*

(4.27)*

T statistics are shown in parentheses below the returns values. Statistics superscripted by * are significant at 10% level for two-tailed T-tests

First, conditional on past returns R1 or R3, low volume stocks V1, tend to outperform stocks with high past trading volume V2, over the intermediate- and long-term holding periods. But this result is somewhat weak in the long-term for the hotel portfolio. This evidence provides support for the liquidity hypothesis suggested by Lee and Swaminathan (2000), Campbell et al. (1993), and Conrad et al. (1994).

Second, looking at each column of R3-R1 in Panel A for hotel stocks, both low and high volume momentum portfolios do not earn significant profits in the intermediate-term. In the long-term, low volume V1 hotel portfolios experience price reversal and earn significant negative momentum profits, while high volume hotel portfolios keep their momentum pattern. Meanwhile, for the market portfolio, we see that both high and low volume stocks tend to earn significant momentum profits over the intermediate and long-term (the momentum profits in long-term is significantly negative). A probable explanation of long-term momentum pattern for high volume hotel portfolio is that high volume hotel stocks are big size REITs because the dividend policy of REITs together with their more limited free cash flow, mitigate any tendency toward overinvestment in the hotel industry. What’s more, less cash flow means less cash flow momentum in the intermediate-term and reversion in the long-term, which result in a less predictable price pattern.

Third, the cells crossed by column (R3-R1) and row (V2-V1) illustrate that price momentum portfolio with high past volume trading firms significantly outperforms that of low volume firms in the long-term for both the hotel and market portfolio. These results suggest that low volume stocks contribute more to reversal profits than high volume stocks.

Table 13 presents the results of volume-based earning momentum strategy. few find that first, conditional on past earnings news, for the market portfolio, low volume stocks tends to outperform high volume stocks both in the intermediate-term and long-term. But for hotel portfolio, we cannot find such a pattern. Second, at each column of (E3-E1) in market portfolio, we see both high volume V2 and low volume V1 momentum profits are positive in the intermediate- and long-term, except in 3 months and 6 months of some V1 portfolios. This finding is consistent with the earning underreaction hypothesis. It means the market stocks tend to gradually respond to prior earning news. Similar but weak result could also be found for the hotel portfolio over the intermediate-term.
Table 13

Mean annual returns for earning momentum strategy portfolios based on past earning and past trading volume

Portfolio

J = 3 Mon

J = 6 Mon

J = 9 Mon

J = 12 Mon

J = 36 Mon

J = 48 Mon

J = 60 Mon

K

E1

E3

E3-E1

E1

E3

E3-E1

E1

E3

E3-E1

E1

E3

E3-E1

E1

E3

E3-E1

E1

E3

E3-E1

E1

E3

E3-E1

Panel A: Hotel portfolio

3 Mon

V1

0.027

0.256

0.222

0.068

0.196

0.130

0.105

0.156

0.050

0.107

0.142

0.035

0.082

0.085

−0.001

0.097

0.069

−0.038

0.058

0.081

0.036

(0.42)

(2.39)*

(1.98)*

(1.16)

(3.07)*

(1.81)*

(1.92)*

(3.29)*

(0.80)

(2.31)*

(3.45)*

(0.63)

(2.85)*

(2.68)*

(−0.02)

(3.11)*

(2.38)*

(−0.95)

(2.29)*

(2.54)*

(1.21)

V2

0.157

0.119

−0.036

0.161

0.110

−0.048

0.147

0.119

−0.020

0.102

0.151

0.050

0.125

0.120

−0.002

0.109

0.098

−0.016

0.115

0.084

−0.034

(1.79)*

(1.50)

(−0.50)

(2.91)*

(1.96)*

(−0.96)

(3.56)*

(2.60)*

(−0.48)

(3.02)*

(3.70)*

(1.36)

(2.57)*

(3.88)*

(−0.04)

(2.97)*

(3.17)*

(−0.34)

(2.87)*

(2.58)*

(−0.65)

V2-V1

0.126

−0.115

−0.229

0.085

−0.083

−0.167

0.042

−0.032

−0.067

−0.001

0.009

0.013

0.048

0.033

−0.017

0.032

0.031

−0.009

0.093

0.001

−0.181

(1.44)

(−1.24)

(−2.01)*

(1.23)

(−1.37)

(−2.14)*

(0.70)

(−0.60)

(−0.93)

(−0.01)

(0.18)

(0.20)

(0.96)

(1.34)

(−0.29)

(0.81)

(0.93)

(−0.15)

(1.88)*

(0.03)

(−1.95)*

6 Mon

V1

0.073

0.339

0.252

0.119

0.250

0.124

0.126

0.217

0.088

0.096

0.229

0.133

0.065

0.152

0.095

0.060

0.119

0.032

0.048

0.124

0.041

(1.03)

(3.25)*

(2.14)*

(1.27)

(3.61)*

(1.09)

(1.88)*

(4.24)*

(1.07)

(1.92)*

(4.06)*

(1.74)*

(2.06)*

(2.65)*

(1.55)

(1.83)*

(2.68)*

(0.96)

(1.63)*

(2.09)*

(1.54)

V2

0.256

0.119

−0.120

0.169

0.131

−0.029

0.091

0.176

0.084

0.070

0.170

0.099

0.077

0.141

0.066

0.057

0.120

0.075

0.061

0.105

0.054

(2.74)*

(1.67)*

(−1.78)*

(2.98)*

(2.48)*

(−0.59)

(2.22)*

(3.64)*

(1.76)*

(1.88)*

(4.36)*

(2.31)*

(2.28)*

(4.23)*

(1.48)

(2.03)*

(3.37)*

(1.61)*

(1.92)*

(2.89)*

(1.04)

V2-V1

0.180

−0.176

−0.313

0.047

−0.101

−0.144

−0.031

−0.034

−0.003

−0.025

−0.059

−0.034

0.014

−0.020

−0.061

−0.007

−0.005

0.012

0.011

−0.023

−0.035

(1.69)*

(−1.99)*

(−2.86)*

(0.49)

(−1.70)*

(−1.35)

(−0.45)

(−0.58)

(−0.03)

(−0.45)

(−1.09)

(−0.43)

(0.38)

(−0.34)

(−0.73)

(−0.18)

(−0.10)

(0.17)

(0.27)

(−0.34)

(−0.44)

9 Mon

V1

−0.082

0.261

0.365

0.088

0.220

0.126

0.095

0.169

0.072

0.078

0.188

0.110

0.095

0.102

0.004

0.081

0.080

−0.010

0.055

0.079

0.011

(−1.40)

(3.00)*

(3.58)*

(0.67)

(3.69)*

(0.92)

(0.98)

(3.76)*

(0.75)

(1.09)

(4.19)*

(1.38)

(1.91)*

(3.37)*

(0.07)

(2.11)*

(2.74)*

(−0.26)

(1.69)*

(2.46)*

(0.34)

V2

0.165

0.110

−0.050

0.084

0.189

0.100

0.079

0.184

0.102

0.078

0.182

0.100

0.112

0.130

0.019

0.110

0.113

0.005

0.107

0.098

−0.022

(1.78)*

(1.39)

(−0.75)

(1.46)

(3.38)*

(2.25)*

(1.66)*

(4.01)*

(2.33)*

(2.04)*

(4.65)*

(2.58)*

(2.07)*

(3.74)*

(0.31)

(2.92)*

(3.33)*

(0.11)

(3.03)*

(2.99)*

(-0.42)

V2-V1

0.269

−0.123

−0.330

0.003

−0.023

−0.029

−0.011

0.020

0.026

0.006

−0.006

−0.017

0.028

0.039

0.016

0.014

0.045

0.024

0.058

0.021

−0.024

(2.40)*

(−1.62*)

(−3.39)*

(0.02)

(−0.38)

(−0.21)

(−0.11)

(0.42)

(0.24)

(0.07)

(−0.14)

(−0.19)

(0.35)

(0.90)

(0.17)

(0.34)

(0.98)

(0.32)

(1.36)

(0.44)

(−0.29)

12 Mon

V1

−0.007

0.354

0.363

0.021

0.256

0.233

0.014

0.243

0.228

0.021

0.233

0.219

0.063

0.084

0.005

0.076

0.077

0.004

0.054

0.014

−0.048

(−0.10)

(3.59)*

(3.33)*

(0.32)

(4.02)*

(2.97)*

(0.23)

(4.47)*

(3.04)*

(0.41)

(4.41)*

(3.37)*

(1.86)*

(2.72)*

(0.12)

(1.98)*

(2.36)*

(0.08)

(1.54)

(0.53)

(−0.95)

V2

0.032

0.207

0.168

0.010

0.180

0.169

0.022

0.162

0.139

0.050

0.152

0.105

0.078

0.104

0.034

0.079

0.100

0.048

0.089

0.084

0.006

(0.36)

(2.62)*

(2.08)*

(0.18)

(3.22)*

(2.97)*

(0.50)

(4.07)*

(3.64)*

(1.30)

(4.72)*

(3.06)*

(2.27)*

(3.84)*

(0.90)

(2.51)*

(3.36)*

(1.19)

(2.64)*

(3.12)*

(0.13)

V2-V1

0.042

−0.117

−0.154

−0.007

−0.060

−0.054

0.011

−0.072

−0.083

0.030

−0.081

−0.111

0.020

0.045

0.020

0.009

0.060

0.044

0.056

0.096

0.063

(0.42)

(−1.33)

(−1.15)

(−0.10)

(−0.92)

(−0.54)

(0.18)

(−1.39)

(−1.04)

(0.52)

(−1.50)

(−1.47)

(0.45)

(1.48)

(0.27)

(0.20)

(1.61)*

(0.56)

(1.08)

(3.04)*

(0.67)

Panel B: Market portfolio

3 Mon

V1

0.016

0.258

0.239

0.055

0.249

0.189

0.089

0.238

0.146

0.106

0.241

0.134

0.149

0.186

0.049

0.146

0.174

0.041

0.140

0.165

0.042

(0.33)

(4.69)*

(15.39)*

(1.71)*

(6.17)*

(14.96)*

(3.44)*

(7.12)*

(11.79)*

(4.61)*

(7.77)*

(10.68)*

(9.44)*

(10.33)*

(7.92)*

(10.02)*

(11.02)*

(6.56)*

(10.97)*

(11.59)*

(6.23)*

V2

−0.019

0.178

0.200

0.019

0.147

0.126

0.041

0.135

0.093

0.053

0.135

0.082

0.091

0.112

0.025

0.091

0.107

0.020

0.089

0.106

0.024

(−0.30)

(2.70)*

(13.05)*

(0.49)

(3.55)*

(12.38)*

(1.29)

(4.10)*

(9.29)*

(1.88)*

(4.69)*

(10.05)*

(6.77)*

(8.43)*

(4.24)*

(8.29)*

(9.61)*

(3.65)*

(9.51)*

(12.16)*

(4.78)*

V2-V1

−0.034

−0.067

−0.034

−0.035

−0.091

−0.057

−0.047

−0.097

−0.051

−0.053

−0.105

−0.053

−0.078

−0.110

−0.027

−0.088

−0.120

−0.024

−0.095

−0.127

−0.021

(−1.11)

(−2.30)*

(−2.12)*

(−2.05)*

(−5.54)*

(−5.40)*

(−3.33)*

(−6.79)*

(−4.62)*

(−4.13)*

(−7.88)*

(−3.93)*

(−7.22)*

(−8.10)*

(−2.93)*

(−7.83)*

(−8.08)*

(−2.54)*

(−7.72)*

(−7.79)*

(−2.54)*

6 Mon

V1

0.011

0.327

0.314

0.057

0.289

0.226

0.078

0.276

0.194

0.100

0.260

0.160

0.149

0.190

0.053

0.146

0.176

0.044

0.140

0.164

0.040

(0.24)

(6.09)*

(21.61)*

(1.82)*

(7.69)*

(18.85)*

(3.01)*

(8.73)*

(17.41)*

(4.13)*

(8.93)*

(15.27)*

(9.03)*

(10.93)*

(8.39)*

(9.46)*

(11.05)*

(6.25)*

(10.08)*

(11.55)*

(5.68)*

V2

−0.021

0.214

0.239

0.016

0.165

0.149

0.034

0.155

0.120

0.050

0.147

0.097

0.091

0.113

0.026

0.090

0.109

0.024

0.087

0.108

0.028

(−0.33)

(3.35)*

(13.10)*

(0.39)

(4.10)*

(12.23)*

(1.04)

(4.71)*

(12.19)*

(1.73)*

(5.17)*

(12.25)*

(6.59)*

(8.53)*

(4.99)*

(8.30)*

(9.99)*

(5.29)*

(9.17)*

(12.18)*

(5.33)*

V2−V1

−0.032

−0.091

−0.061

−0.040

−0.109

−0.070

−0.043

−0.113

−0.071

−0.050

−0.112

−0.063

−0.079

−0.115

−0.030

−0.090

−0.122

−0.024

−0.098

−0.119

−0.014

(−1.00)

(−3.07)*

(−3.54)*

(−2.26)*

(−6.04)*

(−5.95)*

(−2.94)*

(−8.12)*

(−6.83)*

(−3.91)*

(−8.56)*

(−5.54)*

(−6.53)*

(−8.71)*

(−4.01)*

(−7.28)*

(−8.07)*

(−2.81)*

(−7.05)*

(−7.13)*

(−1.51)

9 Mon

V1

0.006

0.351

0.344

0.032

0.319

0.282

0.067

0.285

0.214

0.097

0.265

0.168

0.151

0.188

0.048

0.148

0.176

0.042

0.141

0.167

0.043

(0.13)

(6.66)*

(21.35)*

(1.03)

(8.53)*

(22.07)*

(2.51)*

(8.95)*

(18.04)*

(3.84)*

(9.14)*

(17.82)*

(8.88)*

(10.94)*

(7.54)*

(9.40)*

(11.31)*

(5.46)*

(9.97)*

(11.81)*

(5.86)*

V2

−0.015

0.208

0.226

0.010

0.174

0.163

0.031

0.158

0.125

0.050

0.149

0.099

0.092

0.113

0.026

0.092

0.110

0.023

0.088

0.110

0.030

(−0.23)

(3.31)*

(12.14)*

(0.24)

(4.36)*

(13.13)*

(0.94)

(4.76)*

(13.34)*

(1.68)*

(5.13)*

(10.93)*

(6.51)*

(8.90)*

(4.25)*

(8.06)*

(10.44)*

(4.32)*

(8.67)*

(12.09)*

(4.64)*

V2−V1

−0.021

−0.114

−0.094

−0.022

−0.125

−0.104

−0.035

−0.119

−0.085

−0.046

−0.116

−0.070

−0.081

−0.110

−0.025

−0.090

−0.120

−0.022

−0.099

−0.123

−0.015

(−0.62)

(−4.44)*

(−5.30)*

(−1.20)

(−8.02)*

(−8.87)*

(−2.51)*

(−8.27)*

(−8.22)*

(−3.78)*

(−8.53)*

(−7.29)*

(−7.07)*

(−8.15)*

(−3.30)*

(−7.60)*

(−8.00)*

(−2.67)*

(−7.37)*

(−7.62)*

(−1.97)*

12 Mon

V1

−0.045

0.409

0.468

0.020

0.334

0.311

0.066

0.293

0.224

0.100

0.265

0.165

0.151

0.186

0.047

0.152

0.174

0.033

0.143

0.167

0.038

(−0.98)

(7.96)*

(26.51)*

(0.58)

(8.92)*

(23.76)*

(2.27) )*

(9.21) )*

(17.31 )*

(3.63)*

(9.26)*

(14.84)*

(8.48)*

(10.96)*

(7.26)*

(9.18)*

(10.98)*

(4.24)*

(9.91)*

(11.88)*

(5.36)*

V2

−0.052

0.241

0.304

−0.006

0.189

0.196

0.024

0.168

0.143

0.047

0.161

0.114

0.093

0.112

0.023

0.094

0.112

0.023

0.086

0.112

0.035

(−0.78)

(3.78)*

(14.59)*

(−0.15)

(4.59)*

(14.58)*

(0.64)

(4.85)*

(12.40)*

(1.40)

(5.18)*

(11.92)*

(6.12)*

(8.39)*

(2.80)*

(7.41)*

(9.98)*

(3.12)*

(7.97)*

(11.43)*

(4.66)*

V2−V1

−0.007

−0.129

−0.123

−0.026

−0.125

−0.100

−0.041

−0.117

−0.077

−0.052

−0.104

−0.052

−0.078

−0.109

−0.026

−0.093

−0.109

−0.012

−0.111

−0.117

−0.004

(−0.20)

(−4.52)*

(−7.71)*

(−1.46)

(−6.70)*

(−10.1)*

(−2.54)*

(−6.96)*

(−7.31)*

(−3.89)*

(−6.38)*

(−5.58)*

(−5.82)*

(−7.89)*

(−3.19)*

(−7.40)*

(−7.35)*

(−1.50)

(−7.93)*

(−6.82)*

(−0.44)

T statistics are shown in parentheses below the returns values. Statistics superscripted by * are significant at 10% level for two-tailed T-tests

Size-Based Price and Earning Momentum Strategies

Table 14 presents returns for hotel and market momentum portfolios on the basis of past return and firm size. Several key results emerge from this table. First, conditional on past returns for the hotel portfolio, the big firms tend to earn the highest significant positive returns in the intermediate- and long-term. The result is consistent with the findings of Jegadeesh and Titman (1993, 2001) that return reversals in long-term (36 month to 60 month) for small firms are stronger. The result for the market portfolio is different, small firms C1 tend to outperform big firms C2 over all holding periods except in 3 months period. Second, the columns R3-R1 indicate that, for the market portfolios, we see both big and small firms earning significant momentum or reversal (negative momentum) profits over the intermediate and long-term. However, for hotel portfolio both small and big firm momentum portfolios do not earn significant profits. Small firm hotel portfolios experience intermediate-term price momentum and long-term reversal, while big firm hotel portfolios do not exhibit such patterns. Third, the cells crossed by column (R3-R1) and row (C2-C1) illustrate that small hotel firms earn higher momentum profits over intermediate-term and higher contrarian profits over long-term holding period, whereas for the overall market portfolio, price momentum portfolios of big firms significantly outperform those of small firms over all holding periods. It strongly suggests that firm size is a useful variable to predict the profitability of price momentum portfolios. It also implies that we should be very cautious when trying to generalize the results from the whole market to a special industry.
Table 14

Mean annual returns for price momentum strategy portfolios based on past return and firm size

Portfolio

J = 3 Mon

J = 6 Mon

J = 9 Mon

J = 12 Mon

J = 36 Mon

J = 48 Mon

J = 60 Mon

K

R1

R3

R3-R1

R1

R3

R3-R1

R1

R3

R3-R1

R1

R3

R3-R1

R1

R3

R3-R1

R1

R3

R3-R1

R1

R3

R3-R1

Panel A: Hotel portfolio

3 Mon

C1

0.108

0.009

−0.092

0.009

0.091

0.082

0.035

0.118

0.082

0.038

0.110

0.072

0.102

0.066

−0.012

0.099

0.089

0.008

0.106

0.046

−0.074

(1.04)

(0.10)

(−0.77)

(0.13)

(1.27)

(1.07)

(0.62)

(1.72)*

(1.31)

(0.81)

(2.18)*

(1.27)

(2.53)*

(2.01)*

(−0.29)

(2.75)*

(2.28)*

(0.17)

(2.26)*

(1.51)

(−1.07)

C2

0.177

0.075

−0.094

0.186

0.093

−0.085

0.151

0.120

−0.028

0.149

0.128

−0.019

0.128

0.141

0.009

0.146

0.147

0.000

0.165

0.152

−0.003

(1.63)*

(1.18)

(−1.06)

(2.76)*

(1.84)*

(−1.66)*

(2.98)*

(2.95)*

(−0.68)

(3.49)*

(3.26)*

(−0.47)

(3.62)*

(5.11)*

(0.22)

(3.59)*

(3.90)*

(−0.01)

(3.20)*

(2.91)*

(−0.05)

C2-C1

0.073

0.065

0.001

0.175

0.002

−0.156

0.111

0.002

−0.102

0.105

0.018

−0.087

0.036

0.080

0.028

0.076

0.076

0.033

0.085

0.131

0.081

(0.71)

(0.85)

(0.01)

(2.65)*

(0.04)

(−2.06)*

(1.88)*

(0.04)

(−1.48)

(2.31)*

(0.41)

(−1.34)

(0.73)

(2.66)*

(0.43)

(1.69)*

(1.63)*

(0.46)

(1.11)

(2.25)*

(0.98)

6 Mon

C1

−0.016

0.051

0.068

0.017

0.132

0.098

0.058

0.137

0.071

0.068

0.183

0.108

0.160

0.113

−0.076

0.124

0.084

−0.069

0.128

0.048

−0.177

(−0.15)

(0.62)

(0.55)

(0.26)

(1.56)

(1.00)

(0.97)

(2.23)*

(0.91)

(1.44)

(2.94)*

(1.56)

(3.24)*

(2.67)*

(−1.00)

(2.64)*

(2.29)*

(−0.93)

(2.34)*

(1.47)

(−1.44)

C2

0.202

0.080

−0.107

0.178

0.126

−0.048

0.134

0.130

−0.001

0.151

0.142

−0.010

0.143

0.171

0.031

0.148

0.163

0.023

0.124

0.181

0.039

(1.73)*

(1.28)

(−1.18)

(2.62)*

(2.71)*

(−0.81)

(2.60)*

(3.68)*

(−0.03)

(3.42)*

(4.29)*

(−0.24)

(3.59)*

(5.12)*

(0.62)

(3.83)*

(4.77)*

(0.48)

(3.44)*

(4.63)*

(0.83)

C2-C1

0.212

0.028

−0.153

0.151

−0.006

−0.110

0.067

−0.006

−0.053

0.078

−0.041

−0.081

−0.026

0.075

0.102

0.017

0.100

0.078

−0.022

0.152

0.125

(1.97)*

(0.35)

(−1.26)

(2.09)*

(−0.08)

(−1.09)

(1.03)

(−0.11)

(−0.57)

(1.56)

(−0.71)

(−1.06)

(−0.58)

(1.45)

(1.46)

(0.35)

(2.30)*

(1.11)

(−0.28)

(3.02)*

(1.22)

9 Mon

C1

0.063

0.084

0.020

0.074

0.120

0.044

0.086

0.147

0.060

0.105

0.136

0.024

0.138

0.094

−0.085

0.130

0.063

−0.135

0.115

0.036

−0.207

(0.58)

(1.05)

(0.18)

(1.07)

(2.28)*

(0.64)

(1.46)

(3.34)*

(0.94)

(2.03)*

(3.25)*

(0.41)

(2.94)*

(2.46)*

(−1.21)

(2.22)*

(1.90)*

(−1.32)

(1.84)*

(1.10)

(−1.42)

C2

0.354

0.118

−0.181

0.261

0.146

−0.097

0.169

0.150

−0.009

0.146

0.152

0.012

0.113

0.165

0.071

0.128

0.178

0.055

0.118

0.174

0.063

(2.53)*

(2.03)*

(−1.78)*

(3.02)

(3.19)*

(−1.43)

(2.70)*

(3.78)*

(−0.17)

(2.84)*

(4.29)*

(0.26)

(3.55)*

(4.93)*

(1.79)*

(2.89)*

(4.41)*

(0.86)

(2.62)*

(4.30)*

(1.59)

C2-C1

0.264

0.032

−0.208

0.163

0.024

−0.134

0.061

0.003

−0.066

0.034

0.019

−0.009

−0.035

0.102

0.157

−0.018

0.149

0.180

−0.005

0.156

0.163

(2.08)*

(0.47)

(−1.76)*

(2.18)*

(0.50)

(−1.65)*

(1.01)

(0.07)

(−0.86)

(0.67)

(0.47)

(−0.12)

(−0.75)

(2.09)*

(2.37)*

(−0.23)

(3.00)*

(2.04)*

(−0.04)

(3.04)*

(1.52)

12 Mon

C1

0.142

0.191

0.045

0.061

0.211

0.145

0.063

0.192

0.128

0.095

0.173

0.078

0.090

0.076

−0.024

0.121

0.050

−0.127

0.091

0.034

−0.034

(1.08)

(1.97)*

(0.35)

(0.89)

(3.26)*

(1.76)*

(1.11)

(2.87)*

(1.67)*

(1.69)*

(3.28)*

(1.11)

(2.07)*

(1.92)*

(−0.41)

(2.07)*

(1.50)

(−1.39)

(1.82)*

(0.97)

(−0.50)

C2

0.349

0.099

−0.197

0.189

0.135

−0.048

0.154

0.150

−0.008

0.140

0.171

0.025

0.154

0.184

0.045

0.161

0.192

0.063

0.131

0.205

−0.010

(2.33)*

(1.75)*

(−1.72)*

(2.40)*

(3.16)*

(−0.70)

(2.66)*

(4.05)*

(−0.16)

(3.04)*

(5.15)*

(0.54)

(3.57)*

(5.46)*

(0.81)

(3.52)*

(4.93)*

(1.19)

(3.09)*

(4.30)*

(−0.21)

C2-C1

0.180

−0.081

−0.230

0.122

−0.069

−0.180

0.085

−0.041

−0.128

0.050

−0.004

−0.057

0.068

0.129

0.062

0.019

0.160

0.137

0.045

0.181

−0.036

(1.28)

(−1.03)

(−1.57)

(1.72)*

(−1.08)

(−1.85)*

(1.45)

(−0.63)

(−1.53)

(0.97)

(−0.08)

(−0.75)

(2.12)*

(2.59)*

(0.82)

(0.28)

(3.46)*

(1.61)*

(0.61)

(3.16)*

(−0.31)

Panel B: Market portfolio

3 Mon

C1

0.153

0.181

0.025

0.149

0.199

0.047

0.165

0.221

0.054

0.194

0.233

0.039

0.221

0.176

−0.069

0.205

0.160

−0.084

0.196

0.163

−0.075

(1.72)*

(2.67)*

(0.58)

(2.38)*

(3.96)*

(1.63)*

(3.30)*

(4.88)*

(2.26)*

(4.24)*

(5.42)*

(1.55)

(9.29)*

(9.01)*

(−3.78)*

(9.86)*

(10.15)*

(−3.91)*

(9.89)*

(10.66)*

(−3.22)*

C2

0.046

0.117

0.068

0.032

0.116

0.082

0.041

0.130

0.087

0.057

0.128

0.071

0.114

0.107

−0.009

0.116

0.107

−0.012

0.115

0.113

−0.004

(0.64)

(2.17)*

(1.45)

(0.72)

(3.14)*

(2.75)*

(1.18)

(4.25)*

(3.47)*

(1.95)*

(4.59)*

(3.15)*

(7.25)*

(7.94)*

(−0.66)

(9.00)*

(9.19)*

(−0.89)

(9.20)*

(8.88)*

(−0.22)

C2-C1

−0.096

−0.056

0.042

−0.109

−0.076

0.035

−0.119

−0.086

0.033

−0.137

−0.105

0.032

−0.175

−0.100

0.053

−0.185

−0.088

0.057

−0.228

−0.104

0.054

(−2.15)*

(−1.65)*

(1.49)

(−3.38)*

(−3.63)*

(1.49)

(−4.61)*

(−3.88)*

(1.92)*

(−5.37)*

(−4.85)*

(2.23)*

(−6.86)*

(−6.04)*

(3.65)*

(−6.53)*

(−5.53)*

(3.91)*

(−6.77)*

(−5.61)*

(3.42)*

6 Mon

C1

0.136

0.222

0.078

0.146

0.235

0.084

0.175

0.247

0.069

0.216

0.231

0.016

0.234

0.161

−0.119

0.212

0.149

−0.127

0.204

0.157

−0.117

(1.50)

(3.32)*

(1.58)

(2.34)*

(4.60)*

(2.85)*

(3.38)*

(5.47)*

(2.33)*

(4.26)*

(5.54)*

(0.49)

(9.35)*

(8.49)*

(−5.55)*

(9.54)*

(9.33)*

(−5.06)*

(9.71)*

(9.82)*

(−3.77)*

C2

0.033

0.127

0.091

0.024

0.144

0.119

0.041

0.141

0.100

0.066

0.128

0.062

0.125

0.105

−0.025

0.126

0.106

−0.028

0.122

0.113

−0.014

(0.42)

(2.41)*

(1.75)*

(0.53)

(3.94)*

(3.87)*

(1.12)

(4.63)*

(3.55)*

(2.05)*

(4.78)*

(2.45)*

(7.38)*

(7.66)*

(−1.54)

(8.72)*

(9.23)*

(−1.74)*

(8.92)*

(8.64)*

(−0.70)

C2-C1

−0.094

−0.082

0.013

−0.114

−0.082

0.034

−0.129

−0.100

0.030

−0.150

−0.103

0.046

−0.183

−0.076

0.075

−0.184

−0.067

0.071

−0.246

−0.085

0.069

(−2.06)*

(−2.75)*

(0.42)

(−3.60)*

(−3.75)*

(1.60)

(−4.91)*

(−4.95)*

(1.81)*

(−5.53)*

(−4.85)*

(2.64)*

(−7.00)*

(−5.53)*

(5.70)*

(−6.33)*

(−4.53)*

(5.20)*

(−6.78)*

(−4.96)*

(4.28)*

9 Mon

C1

0.158

0.245

0.078

0.175

0.254

0.074

0.204

0.239

0.034

0.245

0.219

−0.027

0.245

0.145

−0.169

0.220

0.148

−0.150

0.210

0.158

−0.135

(1.70)*

(3.87)*

(1.50)

(2.68)*

(5.09)*

(1.95)*

(3.62)*

(5.57)*

(0.94)

(4.43)*

(5.99)*

(−0.74)

(9.53)*

(8.09)*

(−6.75)*

(10.19)*

(9.25)*

(−5.62)*

(10.33)*

(7.73)*

(−3.62)*

C2

0.037

0.161

0.121

0.032

0.155

0.122

0.053

0.137

0.082

0.083

0.124

0.041

0.139

0.102

−0.049

0.133

0.106

−0.041

0.130

0.111

−0.032

(0.46)

(3.26)*

(2.09)*

(0.71)

(4.19)

(3.56)*

(1.39)

(4.54)*

(2.63)*

(2.33)*

(4.88)*

(1.44)

(7.46)*

(7.30)*

(−2.55)*

(9.27)*

(8.95)*

(−2.41)*

(9.16)*

(8.45)*

(−1.53)

C2-C1

−0.108

−0.071

0.040

−0.132

−0.088

0.046

−0.144

−0.097

0.048

−0.162

−0.095

0.068

−0.182

−0.058

0.088

−0.188

−0.066

0.073

−0.244

−0.093

0.065

(−2.28)*

(−2.47)*

(1.18)

(−3.96)*

(−4.37)*

(2.04)*

(−5.00)*

(−5.15)*

(2.52)*

(−5.52)*

(−5.20)*

(3.52)*

(−6.37)*

(−4.81)*

(6.22)*

(−6.27)*

(−4.48)*

(4.45)*

(−6.87)*

(−3.95)*

(3.67)*

12 Mon

C1

0.194

0.250

0.049

0.206

0.228

0.020

0.237

0.210

−0.026

0.273

0.196

−0.076

0.248

0.138

−0.191

0.221

0.137

−0.180

0.214

0.144

−0.206

(2.05)*

(3.90)*

(0.87)

(2.97)*

(4.67)*

(0.49)

(3.93)*

(5.40)*

(−0.70)

(4.71)*

(5.65)*

(−2.05)*

(9.38)*

(7.45)*

(−7.17)*

(9.76)*

(10.09)*

(−6.58)*

(9.98)*

(9.50)*

(−5.86)*

C2

0.053

0.144

0.088

0.045

0.132

0.085

0.075

0.120

0.044

0.101

0.112

0.011

0.144

0.099

−0.061

0.139

0.105

−0.052

0.138

0.111

−0.047

(0.62)

(2.92)*

(1.38)

(0.97)

(3.67)*

(2.32)*

(1.83)*

(4.19)*

(1.35)

(2.71)*

(4.51)*

(0.37)

(8.09)*

(7.00)*

(−3.26)*

(9.59)*

(8.54)*

(−2.93)*

(9.45)*

(8.56)*

(−2.26)*

C2-C1

−0.123

−0.089

0.037

−0.146

−0.086

0.065

−0.153

−0.085

0.071

−0.172

−0.084

0.087

−0.179

−0.052

0.091

−0.176

−0.049

0.079

−0.233

−0.060

0.080

(−2.50)*

(−3.22)*

(1.00)

(−4.19)*

(−4.33)*

(2.61)*

(−5.18)*

(−5.07)*

(3.39)*

(−5.73)*

(−4.71)*

(4.27)*

(−6.61)*

(−4.08)*

(5.60)*

(−6.11)*

(−4.11)*

(5.77)*

(−6.65)*

(−4.09)*

(6.08)*

T statistics are shown in parentheses below the returns values. Statistics superscripted by * are significant at 10% level for two-tailed T-tests

Table 15 presents the results of size-based earning momentum strategy. Some empirical results are reported as followings. First, focusing on the firm size, we do not find the highest rewarding portfolio for hotel stocks. However, for the market portfolio, the small firms tend to outperform big firm portfolios over both intermediate and long-term holding periods. Second, at each column of E3-E1 in Panel A, we see that some hotel firms’ momentum profits are significantly positive within 4 years holding period. In Panel B, the market momentum portfolio can earn significant positive momentum profits over the intermediate and long-term holding periods and across all formation periods. This finding of earning momentum in the long-term is highly consistent with the earning underreaction hypothesis. Finally, the cells crossed by column (E3-E1) and row (C2-C1) indicate that earning momentum portfolios of small firms significantly outperform those of big firms over all holding periods for market portfolio, however, this evidence is less significant for hotel stocks, particularly over the long term holding period.
Table 15

Mean annual returns for earning momentum strategy portfolios based on past earning and firm size

Portfolio

J = 3 Mon

J = 6 Mon

J = 9 Mon

J = 12 Mon

J = 36 Mon

J = 48 Mon

J = 60 Mon

K

E1

E3

E3-E1

E1

E3

E3-E1

E1

E3

E3-E1

E1

E3

E3-E1

E1

E3

E3-E1

E1

E3

E3-E1

E1

E3

E3-E1

Panel A: Hotel portfolio

3 Mon

C1

0.042

0.241

0.193

0.061

0.178

0.113

0.092

0.154

0.060

0.090

0.149

0.056

0.064

0.085

0.015

0.081

0.053

−0.047

0.020

0.037

0.007

(0.51)

(2.02)*

(1.53)

(1.00)

(2.58)*

(1.36)

(1.60)

(2.86)*

(0.79)

(1.79)*

(3.09)*

(0.85)

(2.16)*

(2.30)*

(0.38)

(2.21)*

(1.72)*

(−0.93)

(0.84)

(1.19)

(0.18)

C2

0.093

0.128

0.031

0.125

0.120

−0.001

0.121

0.116

0.000

0.090

0.136

0.046

0.107

0.126

0.023

0.117

0.105

−0.009

0.121

0.093

−0.035

(1.35)

(1.93)*

(0.64)

(2.81)*

(2.45)*

(−0.02)

(3.61)*

(3.00)*

(0.01)

(3.13)*

(4.05)*

(1.68)*

(3.27)*

(3.94)*

(0.59)

(3.55)*

(3.43)*

(−0.20)

(3.43)*

(2.98)*

(−0.70)

C2-C1

0.050

−0.096

−0.153

0.057

−0.048

−0.110

0.030

−0.032

−0.058

0.001

−0.013

−0.006

0.048

0.040

−0.015

0.042

0.039

−0.005

0.087

0.049

−0.043

(0.57)

(−0.99)

(−1.31)

(0.98)

(−0.77)

(−1.33)

(0.52)

(−0.63)

(−0.72)

(0.03)

(−0.27)

(−0.08)

(1.48)

(1.40)

(−0.34)

(1.02)

(1.32)

(−0.07)

(2.22)*

(1.58)

(−0.65)

6 Mon

C1

0.099

0.296

0.183

0.124

0.235

0.106

0.097

0.210

0.111

0.073

0.227

0.154

0.057

0.149

0.033

0.036

0.092

0.016

0.001

0.093

0.033

(1.34)

(2.68)*

(1.55)

(1.31)

(3.17)*

(0.86)

(1.44)

(3.88)*

(1.27)

(1.37)

(3.62)*

(1.85)*

(1.79)*

(2.51)*

(1.04)

(1.02)

(1.91)

(0.48)

(0.04)

(1.54)

(1.25)

C2

0.167

0.135

−0.033

0.140

0.135

0.001

0.101

0.150

0.048

0.077

0.152

0.072

0.076

0.135

0.069

0.068

0.133

0.078

0.087

0.122

0.041

(2.29)*

(1.86)*

(−0.58)

(3.06)*

(2.45)*

(0.03)

(2.65)*

(3.62)*

(1.24)

(2.48)*

(4.53)*

(2.18)*

(2.50)*

(4.75)*

(1.83)*

(2.62)*

(4.08)*

(1.76)*

(2.75)*

(3.29)*

(0.72)

C2-C1

0.068

−0.132

−0.189

0.015

−0.085

−0.099

0.007

−0.053

−0.060

0.008

−0.076

−0.082

0.018

−0.023

0.008

0.019

0.028

0.041

0.067

0.022

0.026

(0.73)

(−1.25)

(−1.74)*

(0.17)

(−1.16)

(−0.84)

(0.11)

(−0.89)

(−0.67)

(0.15)

(−1.32)

(−0.97)

(0.51)

(−0.40)

(0.15)

(0.51)

(0.58)

(0.60)

(1.78)*

(0.34)

(0.40)

9 Mon

C1

−0.049

0.214

0.273

0.097

0.213

0.111

0.102

0.188

0.084

0.072

0.178

0.106

0.064

0.083

0.017

0.048

0.069

0.007

0.012

0.080

0.048

(−0.75)

(2.20)*

(2.36)*

(0.72)

(3.12)*

(0.79)

(1.02)

(3.54)*

(0.82)

(0.95)

(3.77)*

(1.22)

(1.48)

(2.73)*

(0.39)

(1.28)

(2.40)*

(0.18)

(0.39)

(2.67)*

(1.31)

C2

0.109

0.137

0.030

0.066

0.168

0.098

0.075

0.156

0.080

0.086

0.160

0.074

0.101

0.122

0.034

0.110

0.114

0.010

0.114

0.096

−0.033

(1.63)*

(1.85)*

(0.58)

(1.37)

(3.23)*

(2.51)*

(1.82)*

(3.87)*

(2.21)*

(2.52)*

(4.51)*

(2.34)*

(2.73)*

(3.90)*

(0.85)

(2.93)*

(3.62)*

(0.20)

(3.28)*

(3.03)*

(−0.61)

C2-C1

0.164

−0.063

−0.202

−0.023

−0.035

−0.012

−0.023

−0.025

−0.002

0.015

−0.018

−0.033

0.020

0.042

0.010

0.050

0.038

0.019

0.100

0.005

−0.085

(1.96)*

(−0.67)

(−1.79)*

(−0.18)

(−0.50)

(−0.09)

(−0.24)

(−0.49)

(−0.02)

(0.19)

(−0.43)

(−0.35)

(0.42)

(1.12)

(0.14)

(1.27)

(0.90)

(0.24)

(2.79)*

(0.09)

(−0.82)

12 Mon

C1

−0.079

0.303

0.405

−0.046

0.256

0.308

−0.043

0.252

0.297

−0.025

0.223

0.249

0.050

0.087

0.037

0.047

0.076

0.046

0.013

0.023

0.017

(−1.17)

(2.94)*

(3.83)*

(−0.72)

(3.82)*

(3.71)*

(−0.68)

(4.35)*

(3.63)*

(−0.49)

(4.29)*

(3.52)*

(1.46)

(2.83)*

(0.90)

(1.22)

(2.43)*

(1.03)

(0.42)

(0.92)

(0.40)

C2

0.061

0.205

0.134

0.078

0.154

0.073

0.082

0.150

0.067

0.095

0.147

0.055

0.090

0.118

0.033

0.108

0.106

0.028

0.125

0.086

−0.041

(0.76)

(2.88)*

(2.18)*

(1.27)

(3.20)*

(1.43)

(1.77)*

(3.67)*

(1.58)

(2.59)*

(4.05)*

(1.51)

(2.54)*

(3.78)*

(0.78)

(3.31)*

(3.25)*

(0.61)

(3.59)*

(2.96)*

(−0.73)

C2-C1

0.153

−0.080

−0.209

0.131

−0.084

−0.202

0.129

−0.091

−0.214

0.120

−0.073

−0.193

0.049

0.047

−0.001

0.069

0.063

−0.012

0.121

0.087

−0.036

(1.81)*

(−0.90)

(−1.95)*

(1.72)*

(−1.39)

(−2.17)*

(1.97)*

(−1.77)*

(−2.48)*

(2.16)*

(−1.42)

(−2.32)*

(1.05)

(1.37)

(−0.01)

(1.57)

(1.63)*

(−0.14)

(2.80)*

(2.46)*

(−0.32)

Panel B: Market portfolio

3 Mon

C1

−0.006

0.282

0.289

0.045

0.269

0.220

0.085

0.259

0.171

0.107

0.264

0.157

0.154

0.200

0.060

0.150

0.186

0.054

0.142

0.176

0.055

(−0.10)

(3.79)*

(14.46)*

(1.05)

(5.00)*

(13.45)*

(2.41)*

(5.91)*

(10.94)*

(3.37)*

(6.53)*

(10.88)*

(8.46)*

(9.45)*

(7.80)*

(8.93)*

(10.14)*

(6.95)*

(9.82)*

(10.44)*

(6.70)*

C2

−0.004

0.166

0.170

0.030

0.136

0.105

0.045

0.126

0.080

0.054

0.121

0.066

0.090

0.106

0.019

0.093

0.103

0.013

0.091

0.104

0.017

(−0.08)

(3.37)*

(14.60)*

(1.02)

(4.59)*

(14.08)*

(1.91)*

(5.26)*

(12.20)*

(2.63)*

(5.89)*

(11.49)*

(7.67)*

(9.45)*

(4.52)*

(9.47)*

(10.98)*

(3.30)*

(11.05)*

(14.17)*

(4.27)*

C2-C1

0.002

−0.096

−0.098

−0.015

−0.118

−0.104

−0.039

−0.125

−0.087

−0.053

−0.144

−0.091

−0.089

−0.145

−0.046

−0.092

−0.159

−0.048

−0.095

−0.172

−0.048

(0.07)

(−2.53)*

(−5.29)*

(−0.65)

(−4.08)*

(−7.19)*

(−2.03)*

(−4.99)*

(−5.93)*

(−3.02)*

(−5.64)*

(−6.09)*

(−6.04)*

(−7.09)*

(−4.86)*

(−5.85)*

(−7.31)*

(−4.98)*

(−6.05)*

(−6.85)*

(−4.65)*

6 Mon

C1

−0.008

0.368

0.377

0.049

0.316

0.261

0.075

0.303

0.224

0.101

0.292

0.191

0.155

0.202

0.061

0.149

0.185

0.053

0.142

0.173

0.049

(−0.13)

(4.92)*

(18.77)*

(1.17)

(6.01)*

(15.90)*

(2.18)*

(6.96)*

(15.34)*

(3.13)*

(7.25)*

(14.13)*

(8.32)*

(9.87)*

(8.27)*

(8.70)*

(9.90)*

(6.93)*

(9.19)*

(10.41)*

(7.25)*

C2

−0.006

0.187

0.194

0.022

0.152

0.129

0.037

0.138

0.100

0.051

0.126

0.075

0.089

0.108

0.023

0.093

0.108

0.019

0.090

0.107

0.023

(−0.11)

(4.04)*

(12.54)*

(0.75)

(5.39)*

(13.12)*

(1.52)

(5.92)*

(12.09)*

(2.39)*

(6.39)*

(11.46)*

(7.43)*

(9.55)*

(5.51)*

(9.22)*

(11.45)*

(4.73)*

(10.67)*

(13.97)*

(5.05)*

C2-C1

0.002

−0.142

−0.144

−0.027

−0.142

−0.117

−0.038

−0.154

−0.117

−0.050

−0.166

−0.116

−0.091

−0.145

−0.044

−0.090

−0.144

−0.039

−0.099

−0.151

−0.032

(0.06)

(−3.79)*

(−6.89)*

(−1.28)

(−4.75)*

(−7.00)*

(−2.24)*

(−5.83)*

(−7.89)*

(−2.94)*

(−6.25)*

(−7.68)*

(−6.21)*

(−7.35)*

(−5.27)*

(−5.76)*

(−6.55)*

(−4.32)*

(−5.90)*

(−6.29)*

(−3.92)*

9 Mon

C1

−0.006

0.388

0.395

0.027

0.350

0.319

0.062

0.319

0.254

0.098

0.299

0.201

0.156

0.200

0.057

0.149

0.184

0.051

0.142

0.175

0.053

(−0.10)

(5.22)*

(19.07)*

(0.63)

(6.54)*

(18.96)*

(1.72)*

(7.09)*

(17.09)*

(2.87)*

(7.32)*

(14.90)*

(7.99)*

(9.71)*

(7.36)*

(8.61)*

(9.95)*

(6.68)*

(8.94)*

(10.41)*

(6.97)*

C2

−0.007

0.190

0.198

0.015

0.154

0.138

0.037

0.136

0.098

0.053

0.126

0.074

0.091

0.109

0.021

0.095

0.109

0.018

0.091

0.110

0.026

(−0.14)

(4.12)*

(11.13)*

(0.49)

(5.67)*

(12.43)*

(1.49)

(6.00)*

(11.20)*

(2.43)*

(6.49)*

(10.46)*

(7.52)*

(10.40)*

(4.17)*

(8.91)*

(12.15)*

(3.24)*

(10.25)*

(14.15)*

(4.27)*

C2-C1

−0.001

−0.154

−0.153

−0.012

−0.168

−0.157

−0.025

−0.170

−0.146

−0.045

−0.173

−0.128

−0.090

−0.140

−0.041

−0.088

−0.140

−0.039

−0.096

−0.151

−0.035

(−0.05)

(−4.07)*

(−6.60)

(−0.56)

(−5.41)

(−9.22)*

(−1.35)

(−6.09)*

(−9.28)*

(−2.47)*

(−6.36)*

(−8.54)*

(−5.73)*

(−6.87)*

(−4.25)*

(−5.63)*

(−6.24)*

(−3.97)*

(−5.72)*

(−6.12)*

(−3.99)*

12 Mon

C1

−0.053

0.458

0.531

0.013

0.376

0.362

0.063

0.331

0.264

0.102

0.302

0.200

0.153

0.194

0.053

0.152

0.180

0.042

0.142

0.173

0.051

(−0.90)

(6.22)*

(28.21)*

(0.28)

(6.85)*

(23.70)*

(1.61)*

(7.30)*

(17.35)*

(2.78)*

(7.35)*

(14.99)*

(7.92)*

(9.37)*

(6.32)*

(8.63)*

(9.49)*

(4.80)*

(9.06)*

(10.50)*

(7.04)*

C2

−0.043

0.200

0.251

0.002

0.155

0.154

0.028

0.137

0.109

0.045

0.131

0.086

0.093

0.109

0.019

0.096

0.111

0.020

0.091

0.110

0.027

(−0.81)

(4.35)*

(11.25)*

(0.05)

(5.74)*

(11.08)*

(1.03)

(5.91)*

(9.79)

(1.84)*

(6.43)*

(8.55)*

(6.82)*

(9.95)*

(2.50)*

(7.93)*

(11.74)*

(2.49)*

(9.02)*

(13.55)*

(3.61)*

C2-C1

0.010

−0.193

−0.202

−0.011

−0.187

−0.177

−0.035

−0.179

−0.146

−0.057

−0.172

−0.114

−0.083

−0.128

−0.038

−0.092

−0.126

−0.025

−0.097

−0.141

−0.029

(0.33)

(−5.18)*

(−9.84)*

(−0.49)

(−5.86)*

(−11.6)*

(−1.73)*

(−6.61)*

(−9.78)*

(−3.15)*

(−6.41)*

(−7.71)*

(−5.72)*

(−6.21)*

(−3.70)*

(−6.00)*

(−5.54)*

(−2.39)*

(−6.11)*

(−5.87)*

(−2.71)*

T statistics are shown in parentheses below the returns values. Statistics superscripted by * are significant at 10% level for two-tailed T-tests

Risk-Adjusted Momentum Returns

The Fama-French three-factor model (Fama and French 1993) adjusted results are reported in this section to investigate the source of the intermediate- and long-term momentum/contrarian profits,

Tables 16 and 17 summarize the returns and risk-adjusted returns for the basic price/earning momentum strategy portfolios for both the hotel and the general market. Due to the space limitation, tables summarizing the risk-adjusted returns for four advanced portfolios are not reported in the paper but they are available upon request.
Table 16

Risk-adjusted and non-risk-adjusted mean annual returns for hotel stock and market price momentum (R3-R1) strategy portfolios

Portfolio

Not risk-adjusted

Risk-adjusted

  

J = 3

J = 6

J = 9

J = 12

J = 36

J = 48

J = 60

J = 3

J = 6

J = 9

J = 12

J = 36

J = 48

J = 60

K

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Panel A: Hotel portfolio

3 Mon

R3-R1

−0.064

0.012

0.021

0.048

−0.018

−0.005

−0.048

−0.042

0.019

0.027

0.062

−0.014

−0.021

−0.085

(−0.83)

(0.26)

(0.60)

(1.47)

(−0.59)

(−0.10)

(−1.00)

(−0.51)

(0.36)

(0.62)

(1.56)

(−0.37)

(−0.37)

(−1.37)

6 Mon

R3−R1

−0.048

0.027

0.037

0.053

0.014

0.017

0.045

−0.019

0.014

0.044

0.064

0.016

0.003

0.048

(−0.58)

(0.52)

(0.86)

(1.53)

(0.34)

(0.39)

(0.95)

(−0.21)

(0.23)

(0.85)

(1.56)

(0.33)

(0.06)

(0.84)

9 Mon

R3−R1

−0.043

0.022

0.037

0.046

0.021

0.048

0.041

−0.000

0.029

0.065

0.045

0.018

0.022

0.035

(−0.51)

(0.44)

(0.84)

(1.23)

(0.58)

(0.99)

(1.05)

(−0.001)

(0.50)

(1.25)

(1.00)

(0.41)

(0.37)

(0.72)

12 Mon

R3−R1

−0.026

0.058

0.075

0.063

0.038

0.053

0.090

0.051

0.077

0.102

0.061

0.032

0.038

0.097

(−0.31)

(1.12)

(1.68)*

(1.59)

(0.95)

(1.10)

(2.00)*

(0.55)

(1.30)

(1.92)*

(1.27)

(0.66)

(0.62)

(1.81)*

Panel B: Market portfolio

3 Mon

R3-R1

0.020

0.046

0.053

0.040

−0.050

−0.055

−0.050

0.057

0.044

0.075

0.052

−0.045

−0.058

−0.067

(0.45)

(1.63)*

(2.30)*

(1.76)*

(−3.24)*

(−3.31)*

(−2.67)*

(1.23)

(1.43)

(2.82)*

(1.94)*

(−2.39)*

(−2.88)*

(−2.93)*

6 Mon

R3−R1

0.066

0.083

0.068

0.021

−0.080

−0.082

−0.072

0.093

0.103

0.106

0.052

−0.080

−0.089

−0.111

(1.28)

(2.83)*

(2.45)*

(0.78)

(−4.32)*

(−4.07)*

(−3.09)*

(1.80)*

(3.03)*

(3.34)*

(1.65)*

(−3.56)*

(−3.64)*

(−3.81)*

9 Mon

R3−R1

0.074

0.076

0.036

−0.014

−0.117

−0.100

−0.095

0.144

0.114

0.082

0.031

−0.105

−0.114

−0.129

(1.35)

(2.15)*

(1.13)

(−0.43)

(−5.45)*

(−4.95)*

(−3.57)*

(2.62)*

(2.90)*

(2.32)*

(0.91)

(−4.21)*

(−4.57)*

(−3.85)*

12 Mon

R3−R1

0.046

0.028

−0.014

−0.056

−0.135

−0.120

−0.132

0.134

0.068

0.045

−0.022

−0.129

−0.140

−0.181

(0.79)

(0.73)

(−0.40)

(−1.71)*

(−6.20)*

(−5.54)*

(−4.89)*

(2.33)*

(1.64)*

(1.25)

(−0.60)

(−5.05)*

(−5.20)*

(−5.29)*

This table reports the mean annual returns and Fama-French three-factor risk adjusted mean annual returns for intermediate- and long-term price momentum strategy portfolio. T statistics are shown in parentheses below the returns values. Statistics superscripted by * are significant at 10% level for two-tailed T-tests

Table 17

Risk-adjusted and non-risk-adjusted mean annual returns for hotel stock and market earning momentum (E3-E1) strategy portfolios

Portfolio

Not risk-adjusted

Risk-adjusted

  

J = 3

J = 6

J = 9

J = 12

J = 36

J = 48

J = 60

J = 3

J = 6

J = 9

J = 12

J = 36

J = 48

J = 60

K

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Panel A: Hotel portfolio

3 Mon

E3-E1

0.062

0.034

0.009

0.037

0.011

−0.003

−0.002

0.029

0.014

0.063

0.057

0.000

−0.025

−0.020

(0.98)

(0.82)

(0.23)

(1.09)

(0.37)

(−0.11)

(−0.07)

(0.41)

(0.30)

(1.36)

(1.36)

(−0.01)

(−0.66)

(−0.49)

6 Mon

E3-E1

0.057

0.063

0.092

0.119

0.071

0.065

0.056

0.033

0.075

0.123

0.125

0.074

0.059

0.060

(0.94)

(1.07)

(2.12)*

(2.93)*

(2.50)*

(2.45)*

(1.73)*

(0.48)

(1.07)

(2.41)*

(2.50)*

(2.08)*

(1.80)*

(1.54)

9 Mon

E3-E1

0.115

0.133

0.112

0.120

0.030

0.004

−0.018

0.038

0.133

0.117

0.112

0.018

−0.036

−0.035

(2.07)*

(3.01)*

(3.15)*

(3.42)*

(1.12)

(0.14)

(−0.57)

(0.63)

(2.63)*

(2.84)*

(2.56)*

(0.54)

(−0.92)

(−0.91)

12 Mon

E3-E1

0.274

0.212

0.208

0.163

0.028

0.016

−0.025

0.251

0.215

0.203

0.162

0.029

0.009

−0.036

(4.28)*

(4.90)*

(5.21)*

(5.29)*

(1.45)

(0.64)

(−0.89)

(3.64)*

(4.22)*

(4.25)*

(4.25)*

(1.25)

(0.31)

(−1.09)

Panel B: Market portfolio

3 Mon

E3-E1

0.220

0.157

0.119

0.108

0.036

0.030

0.032

0.216

0.147

0.102

0.103

0.035

0.032

0.034

(16.96)*

(15.70)*

(12.21)*

(13.04)*

(8.46)*

(7.06)*

(6.83)*

(15.04)*

(13.45)*

(9.45)*

(10.41)*

(6.64)*

(6.25)*

(5.98)*

6 Mon

E3-E1

0.277

0.187

0.157

0.128

0.039

0.033

0.033

0.269

0.176

0.141

0.123

0.042

0.037

0.033

(20.37)*

(18.04)*

(17.12)*

(16.92)*

(7.70)*

(6.86)*

(6.54)*

(18.25)*

(15.89)*

(14.15)*

(13.83)*

(6.81)*

(6.34)*

(5.54)*

9 Mon

E3-E1

0.282

0.222

0.170

0.133

0.036

0.032

0.036

0.279

0.215

0.151

0.131

0.038

0.032

0.036

(18.77)*

(19.78)*

(17.88)*

(16.22)*

(6.41)*

(5.50)*

(5.86)*

(17.25)*

(17.05)*

(15.26)*

(13.61)*

(5.50)*

(4.66)*

(4.91)*

12 Mon

E3-E1

0.385

0.253

0.183

0.139

0.034

0.029

0.037

0.386

0.241

0.163

0.129

0.034

0.031

0.036

(21.70)*

(20.30)*

(16.27)*

(14.36)*

(5.03)*

(3.95)*

(5.62)*

(20.47)*

(18.70)*

(14.22)*

(11.69)*

(4.11)*

(3.57)*

(4.60)*

This table reports the mean annual returns and Fama-French three-factor risk adjusted mean annual returns for intermediate- and long-term earning momentum strategy portfolio. T statistics are shown in parentheses below the returns values. Statistics superscripted by * are significant at 10% level for two-tailed T-tests

All of the results indicate that the risk-adjusted returns of the portfolio relative to the Fama-French three-factor model for both hotel stock and the market portfolios still exhibit highly similar magnitude and persistence of intermediate-term momentum and long-term reversal patterns. It means that our results are not compatible with the Fama and French’s hypothesis. Thus, something other than the three factors explains the profits of momentum portfolios.

Hotel REITs

Since the sample of hotel REITs is small, in order to examine the impact of hotel REITs on the mean return patterns of the hotel stocks, the study introduces a non-REIT hotel portfolio.

Table 18 indicates that the price momentum strategy for non-REIT hotel portfolio produces a long-term price reversal after intermediate-term price momentum which is not found for the full hotel portfolio. This evidence implies that the bust of hotel REITs since the middle of 1990s gives rise to less overreaction of mean returns for hotel stocks as a whole. As discussed in subsection “Basic Price and Earning Strategies”, Yoshida (2008)’s “cash flow predictability” model and Zhang (2002)’s “overinvestment-financial problem” hypothesis are possible explanations of the REITs impact. At the same time, we don’t find significant impact of hotel REITs on the earning momentum portfolio for hotel stock portfolio.
Table 18

Mean profits of price/earning momentum strategy for hotel, non-REIT hotel, and market portfolio

Portfolio

Hotel portfolio

Hotel portfolio (REITs excluded)

Market portfolio

  

J = 3

J = 6

J = 9

J = 12

J = 36

J = 48

J = 60

J = 3

J = 6

J = 9

J = 12

J = 36

J = 48

J = 60

J = 3

J = 6

J = 9

J = 12

J = 36

J = 48

J = 60

K

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Mon

Panel A: Price momentum portfolio

 3 Mon

R3-R1

−0.064

0.012

0.021

0.048

−0.018

−0.005

−0.048

−0.042

0.020

0.023

0.047

−0.047

−0.041

−0.061

0.020

0.046

0.053

0.040

−0.050

−0.055

−0.050

(−0.83)

(0.26)

(0.60)

(1.47)

(−0.59)

(−0.10)

(−1.00)

(−0.47)

(0.39)

(0.54)

(1.25)

(−1.33)

(−1.09)

(−1.47)

(0.45)

(1.63)*

(2.30)*

(1.76)*

(−3.24)*

(−3.31)*

(−2.67)*

 6 Mon

R3-R1

−0.048

0.027

0.037

0.053

0.014

0.017

0.045

−0.026

0.013

0.011

0.011

−0.029

−0.066

−0.051

0.066

0.083

0.068

0.021

−0.080

−0.082

−0.072

(−0.58)

(0.52)

(0.86)

(1.53)

(0.34)

(0.39)

(0.95)

(−0.31)

(0.23)

(0.22)

(0.24)

(−0.71)

(−1.46)

(−1.26)

(1.28)

(2.83)*

(2.45)*

(0.78)

(−4.32)*

(−4.07)*

(−3.09)*

 9 Mon

R3-R1

−0.043

0.022

0.037

0.046

0.021

0.048

0.041

−0.067

−0.010

0.006

−0.016

−0.069

−0.072

−0.102

0.074

0.076

0.036

−0.014

−0.117

−0.100

−0.095

(−0.51)

(0.44)

(0.84)

(1.23)

(0.58)

(0.99)

(1.05)

(−0.75)

(−0.17)

(0.12)

(−0.36)

(−1.59)

(−1.58)

(−2.06)*

(1.35)

(2.15)*

(1.13)

(−0.43)

(−5.45)*

(−4.95)*

(−3.57)*

 12 Mon

R3-R1

−0.026

0.058

0.075

0.063

0.038

0.053

0.090

−0.023

0.031

0.028

0.004

−0.068

−0.049

−0.042

0.046

0.028

−0.014

−0.056

−0.135

−0.120

−0.132

(−0.31)

(1.12)

(1.68)*

(1.59)

(0.95)

(1.10)

(2.00)*

(−0.27)

(0.56)

(0.56)

(0.10)

(−1.84)*

(−1.27)

(−1.33)

(0.79)

(0.73)

(−0.40)

(−1.71)*

(−6.20)*

(−5.54)*

(−4.89)*

Panel B: Earning momentum portfolio

 3 Mon

E3-E1

0.062

0.034

0.009

0.037

0.011

−0.003

−0.002

0.056

0.032

−0.013

0.024

0.005

−0.024

−0.018

0.220

0.157

0.119

0.108

0.036

0.030

0.032

(0.98)

(0.82)

(0.23)

(1.09)

(0.37)

(−0.11)

(−0.07)

(0.67)

(0.58)

(−0.26)

(0.53)

(0.14)

(−0.68)

(−0.49)

(16.96)*

(15.70)*

(12.21)*

(13.04)*

(8.46)*

(7.06)*

(6.83)*

 6 Mon

E3−E1

0.057

0.063

0.092

0.119

0.071

0.065

0.056

0.080

0.097

0.132

0.152

0.075

0.047

0.062

0.277

0.187

0.157

0.128

0.039

0.033

0.033

(0.94)

(1.07)

(2.12)*

(2.93)*

(2.50)*

(2.45)*

(1.73)*

(1.05)

(1.45)

(2.64)*

(3.40)*

(2.35)*

(1.27)

(1.58)

(20.37)*

(18.04)*

(17.12)*

(16.92)*

(7.70)*

(6.86)*

(6.54)*

 9 Mon

E3-E1

0.115

0.133

0.112

0.120

0.030

0.004

−0.018

0.106

0.134

0.121

0.135

0.057

−0.003

−0.063

0.282

0.222

0.170

0.133

0.036

0.032

0.036

(2.07)*

(3.01)*

(3.15)*

(3.42)*

(1.12)

(0.14)

(−0.57)

(1.59)

(2.32)*

(2.64)*

(3.12)*

(1.71)*

(−0.08)

(−1.35)

(18.77)*

(19.78)*

(17.88)*

(16.22)*

(6.41)*

(5.50)*

(5.86)*

 12 Mon

E3−E1

0.274

0.212

0.208

0.163

0.028

0.016

−0.025

0.326

0.224

0.214

0.157

−0.001

−0.035

−0.085

0.385

0.253

0.183

0.139

0.034

0.029

0.037

(4.28)*

(4.90)*

(5.21)*

(5.29)*

(1.45)

(0.64)

(−0.89)

(3.70)*

(3.63)*

(3.82)*

(3.88)*

(−0.02)

(−0.92)

(−1.93)*

(21.70)*

(20.30)*

(16.27)*

(14.36)*

(5.03)*

(3.95)*

(5.62)*

T statistics are shown in parentheses below the returns values. Statistics superscripted by * are significant at 10% level for two-tailed T-tests

Conclusions

This study examines the performance of hotel stocks during the period from 1990 to 2007. Several important results are found in this study.

The momentum (or contrarian) strategies based on past price, past earning surprise, past trading volume, and firm size give rise to substantial profits in the short-term, intermediate-term, and long-term for both hotel stocks and the overall stock market in the United States, although the results are much less significant for the hotel portfolio particularly in the long-term. This finding strongly supports that average hotel and market stock returns in different horizons can be predicted by past returns and past earnings.

The study finds that contrarian price strategies in the short-term can earn significant profit even if the returns of these portfolios are risk-adjusted by the Fama-French three-factor model. Similarly, in the intermediate- and long-term, the study finds momentum (or contrarian) price strategies profit of can not be significantly reduced by Fama-French three-factor model. Positive cross autocorrelations due to big firms lead small firms’ returns can be one of the reasonable factors for short-term contrarian profits. This evidence supports the lead-lag hypothesis (Lo and Mackinlay 1990). We also find the contrarian portfolio of small firms significantly outperforms that of large firms is also supported.

Two pieces of publicly available information—stocks’ past return and the past earning surprises—individually predict future returns in the intermediate-term and long-term. The study suggests that their abilities to predict stock returns are compatible; one effect is not subsumed by the other. The difference in the performances of the two kinds of strategies—price momentum strategy and earning momentum strategy—has some intuitive basis. The earnings momentum strategies are based on the performance of the most recent two quarters’ announced net incomes (earnings), their spread divided by the standard deviation of unexpected earnings. Earnings or net income is a financial value which is an indicator of the operational performance in the earning period reported. In comparison, past returns reflect a broad set of market expectations of the firm’s future outlook not limited to near-term profitability. On this basis these two momentum strategies are able to predict future returns individually.

Price momentum portfolios experience price revision in the long-term confirms the price overreaction hypothesis (Lehmann 1990; Lo and Mackinlay 1988, 1990 and etc.). It suggests that at least a portion of momentum profits is better characterized as an overreaction; the market initially tends to be overly optimistic and then adjusts downward over time. For earning surprises, the evidence that the earning momentum profits persist for up to three, even 5 years supports the earning underreaction theory (Chan et al. 1996) that the market does not incorporate the news of past earnings promptly, and indeed, the adjustment is gradual so that there are drifts in subsequent returns. The empirical results of this paper are clearly inconsistent with the earning overreaction hypothesis of Conrad and Kaul (1998) and the price underreaction hypothesis (Chan et al. 1996).

The intermediate-term stock price overreaction and long-term error correction could possibly be caused by a combination effect of business cycles and oversupply cycles. The business cycles of the hotel industry are closely positively related to the general economic climate. For instance, Choi et al. (1999) report the cyclical pattern in the hotel sector. Lundberg et al. (1995), Powers and Barrows (2002), and Vogel (2001) find overbuilding cycles which are characterized by the oversupply in expansions and huge losses in recessions. These oversupply cycles overlap the economic cyclical curve and exaggerate the performance of hotel firms. Therefore, in prosperity, hotel stock prices tend to overreact with the irrational expansion or oversupply of the hotel industry. In recession, hotel stock prices drop until the demand can catch up with supply.

The empirical evidence found in this study confirms that in general the earning momentum effect of the market portfolio tends to be stronger and longer-lived than that of the hotel portfolio. The possible explanations are as follows: products and services of hotel industry such as hotel guestrooms and conference rooms are highly perishable and intangible (Harris and Brown 1998), if the consumption does not take place, the loss will be occurred simultaneously. On the other hand, unlike the goods produced in factories and sold elsewhere, the operation information of the hotel products and services can be simply acquired. Their current operational performance (such as sales) could be more easily observed, and near-term financial performance (such as earnings) could be more precisely estimated by analysts and investors than what could be done for other industries, such as the traditional manufacturing industry, whose unsold products can be stored and sold after the next earning quarterly disclosure to recover a proportion of cost in a worst-case scenario and whose earnings information could not be easily observed by the public. Since the market has already made very large revisions based on the information revealed before the earnings disclosure date for the current quarter or fiscal year for hotel stocks, their earning momentum effect is expected to be more short-lived and smaller in magnitude than for the whole market.

Trading volume and firm size are useful information sources about future price responses. The evidence in this section supports. The magnitude and persistence of momentum (or contrarian) profitability can be predicted based on past trading volume and firm size. The study finds that market price momentum portfolios of big firms significantly outperform those of small firms over the intermediate- and long-term. It is perhaps due to the big firms’ aggressive expansion in prosperity and other good news. One might expect the same findings for the hotel stocks since in the past decades. The hotel industry has been dominated by a few major players. Historical evidence in 1980’s shows that big firms tend to oversupply in prosperity or to other good news, such as tax deregulation, “cheap” dollar policy, and hotel construction cost declination (Powers and Barrows 2002; Vogel 2001; and Lundberg et al. 1995). Apparently, the big hotel firms should be largely responsible for those irrational fluctuations. For example, the “overbuilding” in the U.S. hotel industry in the middle 1980s caused by the combined impact of tax deregulation and general economic expansion. The dramatic expansion resulted in a serious oversupply and financial problems for the hotel sector from the middle of 1980s to the beginning of 1990s (Vogel 2001). However, examining the sample period from 1990 to 2007, the hotel industry exhibits significantly different statistical behavior patterns than that of the market portfolio—the stock prices of big hotel firms tend to overreact less aggressively in magnitude than those of small hotel stocks. More precisely, price momentum portfolios (or contrarian portfolios) of big hotel firms underperform small hotel firms and the hotel price momentum portfolio (or contrarian portfolios) significantly underperform the overall market over the intermediate-term (or the long-term). We partially attribute this phenomenon to the rapid growth of hotel REITs since 1994. These hotel REITs are big players in the hotel real estate industry. Mooradian and Yang (2001) argues that REITs hotel are less likely to overinvest because the dividend policy of REITs together with their more limited free cash flow, mitigate any tendency toward overinvestment in the hotel industry. Yoshida (2008) explains the predictability of mean return as a reflection of the cash-flow predictability. Therefore, more limited cash flow of REITs implies a weak mean return pattern. Another possible explanation is that learning from lesson of the 1980’s serious oversupply and financial problem in hotel industry, the capital market might conduct more rigorous monitoring on the management of hotel firms than before who has the incentive to overbuild or overpay for assets, then reduce the risks of overinvestment.

In the comparison between the stock performance between the overall market and the hotel portfolio, we find that the hotel portfolio has its own characteristics. As a result, the performance of portfolios based on past returns and past earnings is very different between the hotel and market portfolios. For example, the effect of price information will be more likely to be impounded in hotel stock prices within 5 years; thus it has a shorter contrarian effect than the market portfolio.

Given the real constraints many investors facing, the contrarian returns for the short-term can generally produce arbitrage profits when considering transactions costs and risk premium. However, it may not be profitable to establish intermediate-term momentum strategies and long-term contrarian strategies. A momentum (or contrarian) strategy is trading-intensive, and the trading of small stocks and short-share are costly. Thus trading costs might be high. These implementation issues will substantially reduce the benefits from pursuing a momentum (or contrarian) portfolio.

The study contributes to the hotel industry by offering better understanding of its impacts on stock performance. Overinvestment in the hotel industry had hurt hotel stocks’ return and increased their volatility. The findings of our study suggest that first, fast expansion due to market overreaction will create serious financial problems in subsequent recession. Consolidation via mergers and acquisitions within the hotel industry, rather than aggressive expansion by building new properties, is a wise growth strategy to pursue. Mergers and acquisitions with other related companies may bring additional returns to existing shareholders due to a reduction in operating and capital costs gained from consequent economies of scale. In the post-1990 period, such a strategy helped reduce oversupply and created favorable market conditions for the hotel industry, and therefore helped improve stock performance. This finding is consistent with Gu and Kim (2003) which finds that low-debt financing and within-industry consolidation will lower both the systematic risk and unsystematic risk of hotel REITs.

Second, the hotel industry should be very careful about their new financing activities in the open market. Such activities, particularly when used in funding new properties, not only magnify the financial and market risks but also create downward pressure on hotel stocks due to earnings dilution and increased uncertainty (Brueggeman and Fisher 1997). A conservative growth policy would be helpful. In brief, executives of hotel companies and policy markers in hotel industry should carefully review their growth strategies and financing policies. A conservative growth strategy accompanied by an internal-oriented financing policy can lower risk and improve their return, and thus improve their risk-adjusted performance in the long run.

Admittedly, this empirical research is in non-experimental settings and thus it is limited by data availability. The sample pool for the hotel industry is small, so the explanatory power of some evidence could be weak in “out-of-sample performance”. The sample period coincides with the long bull market in the U.S. stock market history, and therefore the results might be time-sensitive. Also, it is difficult to justify the reliability of the explanations of investment patterns, such as price or earning momentum strategy, from individual studies which have different methodologies and samples. In this vein, the explanations and conclusions of this study are only suggestive.

Footnotes
1

The employment data are the aggregate of industry of Hotels & Motels (IBISWorld Industry Report No. 72111), Casino Hotels (72112), and Bed and Breakfast & Hostel Accommodations (72119). The employment data are 1,411,238, 402,290, and 54,502 respectively.

 
2

“Hotel REITs’ Market Cap Rate Reaches $19.4 Billion—Real Estate Investment Trusts”, Real Estate Weekly, Sept 23, 1998.

 
3

For details see: NAREIT Constituents List (http://nareit.org/library/performance/companies.cfm).

 

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© Springer Science+Business Media, LLC 2009