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Has momentum lost its momentum?

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Abstract

We evaluate the robustness of momentum returns in the US stock market over the period 1965–2012. We find that momentum profits have become insignificant since the late 1990s. Investigations of momentum profits in high and low volatility months address the concerns about unprecedented levels of market volatility in this period rendering momentum strategy unprofitable. Momentum profits remain insignificant in tests designed to control for seasonality, up or down market conditions, firm size and liquidity. Past returns, can no longer explain the cross-sectional variation in stock returns, even following up markets. Investigation of post holding period returns of momentum portfolios and risk adjusted buy and hold returns of stocks in momentum suggests that investors possibly recognize that momentum strategy is profitable and trade in ways that arbitrage away such profits. These findings are partially consistent with Schwert (Handbook of the economics of finance. Elsevier, Amsterdam, 2003) that documents two primary reasons for the disappearance of an anomaly in the behavior of asset prices, first, sample selection bias, and second, uncovering of anomaly by investors who trade in the assets to arbitrage it away. In further analyses we find evidence that suggest two other possible explanations for the declining momentum profits, besides uncovering of the anomaly by investors, that involve decline in the risk premium on a macroeconomic factor, growth rate in industrial production in particular and relative improvement in market efficiency.

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Notes

  1. Many studies are devoted to explaining the phenomenon of momentum profit. Berk et al. (1999), Liu and Zhang (2008), and Wang et al. (2012) document evidence supporting the momentum profits as compensations for risk argument. On the other hand, Jegadeesh and Titman (2001), Wu (2011), Jiang et al. (2012), and Hur and Singh (2014) find evidence supporting behavioral explanations of momentum profits.

  2. Hwang and Rubesam (2015) build an inter-temporal model that explains momentum returns allowing for structural breaks over an extended sample period 1927–2006. They document that momentum profits have slowly started declining in the last two decades of their sample period, a process that began in the early 1990s but delayed by the occurrence of high-technology stock bubble.

  3. We use VXO instead of VIX since the former that is computed using a different methodology and eventually revised by CBOE provides us with an additional 4 years’ worth of data.

  4. Reducing underreaction or mispricing may also result in similar patterns of returns from loser and winner portfolios, if we were to believe momentum profits were caused in the first place due to delayed price reactions to firm-specific information as suggested by Jegadeesh and Titman (1993, 2001). The distinction between uncovering of anomaly by investors and reducing underreaction is beyond the scope of this paper.

  5. Sourced from McKinsay’s Global Institute forecasts, HedgeFundFacts.com and ICIFACTBOOK.ORG.

  6. These results are not reported for the sake of brevity, but they are available upon request.

  7. We reexamine both raw momentum profits and three-factor alphas generated by 6-month/6-month momentum strategy after skipping a month between the formation and holding periods and find similar results. These results are available upon request.

  8. These results are not reported for the sake of brevity, but are available upon request.

  9. We are aware that momentum returns peaked during 1999 and 2000 riding on the internet bubble. In spite of that we include these years in our last subsample since Jegadeesh and Titman (2001)’s out-of-sample period ends in 1998, after which our out-of-sample period begins.

  10. We repeat our analysis with size subsamples formed on the basis of the market capitalization at the beginning of the identification period to make the size sorting process more independent from the past return sorting process and this has no effect on inferences.

  11. We also include natural logarithm of asset-to-market and asset-to-book ratios as explanatory variables instead of natural logarithm of book-to-market in the regressions and this does not have bearing on our inferences.

  12. These results not presented for the sake of brevity, but they are available upon request.

  13. These results are not tabulated for the sake of brevity, but they are available upon request.

  14. Abnormal returns are defined as the intercepts from Fama–French three-factor regressions since the momentum portfolio returns are computed in calendar time in all tables with the exception of the analysis presented in Fig. 3. In this section, momentum portfolio returns are computed in event time and therefore the abnormal returns are calculated as the difference between the raw and expected returns.

  15. We also analyze the risk-adjusted 24 month post holding period returns of the winner and loser portfolios that show substantial reversal consistent with overreaction and subsequent price correction hypothesis until 1998. Post 1998, there is no evidence for either return continuation or subsequent reversal.

  16. The ten size and ten book-to-market portfolio data are from Kenneth French’s web site.

  17. We also examine whether the change in exposure to the marginal productivity risk factor during the last subperiod is similar for both growth and value firms that constitute the momentum portfolio. To this end, we construct thirty portfolios by independently sorting stocks into ten groups based on their previous six months cumulative returns and into three groups based on their book-to market ratios. We calculate the book-to market ratio as in Sect. 2.7 for each firm at the end of December of each year and use it for sorting firms into three groups (growth, intermediate and value) for portfolio formation months from June of next year to May of the following year. We then estimate separately the loadings of low, intermediate, and high book-to-market momentum portfolios (the three difference portfolios which buy winners of particular book-to-market group and sell the corresponding losers) on the MP factor over the combined subperiod 1 and 2 and over the last subperiod. We indeed find that low book-to-market firms (growth) have higher loadings on the MP factor in 1965–1998 period. We further find that over the combined first two subperiods the momentum portfolio loads significantly on the MP factor except for the high book-to-market ratio (value) firms. In contrast, over the last subperiod none of these loadings on the MP factor are statistically significant. In sum, we report that there is a change in the significance of the loading on the MP factor especially for the growth firm. This evidence taken together with the finding that the risk premium on this factor decrease over the last subperiod suggest that the lack of the priced macroeconomic risk in the more recent period is a potential explanation for disappearance of momentum returns especially for the growth firms.

  18. We repeat the analysis using the percentage of Delay in unrestricted R-square as in Mech (1993). We compute DELAY* by subtracting the ratio of adjusted R2 of restricted market model to the adjusted R2 of the unrestricted market model from 1 (Delay* = 1 − (\(adjR_{restricted}^{2} /adjR_{unrestricted}^{2}\))). Similarly, the unrestricted model uses four lags of weekly market returns and in the restricted model the coefficients on these lagged market returns are constrained to zero. The results for the five size portfolios using Delay* as the measure of delay is consistent with the results presented in Panel A of Table 10. There is improvement in delay in the last subperiod across all size quintiles except for the largest size quintile. These results are not reported for the sake of brevity, but they are available upon request.

  19. As indicated by Griffin et al. (2010), delay measures may be subject to larger estimation error noise for individual firms but in order test the statistical significance of delay measures across the three subperiods we have to use delay measure at the stock level.

  20. These are results are not presented but available upon request.

  21. These are results are not presented but available upon request.

References

  • Amihud Y (2002) Illiquidity and stock returns: cross-section and time-series effects. J Financ Mark 5:31–56

    Article  Google Scholar 

  • Berk J, Green R, Naik V (1999) Optimal investment, growth options, and security returns. J Financ 54:1553–1607

    Article  Google Scholar 

  • Bertone S, Paeglis I, Ravi R (2015) (How) has the market become more efficient? J Bank Financ 54:72–86

    Article  Google Scholar 

  • Bessembinder H (2003) Trade execution costs and market quality after decimalization. J Financ Quant Anal 38:747–777

    Article  Google Scholar 

  • Bramante R, Petrella G, Zappa R (2015) On the use of the market model R-square as a measure of stock price efficiency. Rev Quant Financ Acc 44:379–391

    Article  Google Scholar 

  • Brav A, Heaton JB (2002) Competing theories of financial anomalies. Rev Financ Stud 15:575–606

    Article  Google Scholar 

  • Brogaard J, Hendershott T, Riordan R (2014) High-frequency trading and price discovery. Rev Financ Stud 27:2267–2306

    Article  Google Scholar 

  • Chaboud AP, Chiquoine B, Hjalmarsson E, Vega C (2014) Rise of the machines: algorithmic trading in the foreign exchange market. J Financ 69:2045–2084

    Article  Google Scholar 

  • Chordia T, Shivakumar L (2006) Earnings and price momentum. J Financ Econ 80:627–656

    Article  Google Scholar 

  • Chordia T, Roll R, Subrahmanyam A (2008) Liquidity and market efficiency. J Financ Econ 87:249–268

    Article  Google Scholar 

  • Chordia T, Roll R, Subrahmanyam A (2011) Recent trends in trading activity and market quality. J Financ Econ 101:243–263

    Article  Google Scholar 

  • Chordia T, Subrahmanyam A, Tong Q (2014) Have capital market anomalies attenuated in the recent era of high liquidity and trading activity? J Acc Econ 58:41–58

    Article  Google Scholar 

  • Cooper MJ, Gutierrez RC, Hameed A (2004) Market states and momentum. J Financ 59:1345–1365

    Article  Google Scholar 

  • Daniel K, Moskowitz TJ (2013) Momentum crashes. Working paper

  • Fama EF, French KR (1992) The cross-section of expected stock returns. J Financ 47:427–465

    Article  Google Scholar 

  • Fama EF, French KR (1993) Common risk factors in the returns on stocks and bonds. J Financ Econ 33:3–56

    Article  Google Scholar 

  • Fama EF, French KR (1996) Multifactor explanations of asset pricing anomalies. J Financ 51:55–84

    Article  Google Scholar 

  • Fowler DJ, Rorke CH (1983) Risk measurement when shares are subject to infrequent trading: comment. J Financ Econ 12:279–283

    Article  Google Scholar 

  • George TJ, Hwang CY (2004) The 52-week high and momentum investing. J Financ 59:2145–2176

    Article  Google Scholar 

  • Griffin JM, Kelly PJ, Nardari F (2010) Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Rev Financ Stud 23:3225–3277

    Article  Google Scholar 

  • Gu AY, Finnerty J (2002) The evolution of market efficiency: 103 years daily data of the dow. Rev Quant Financ Acc 18:219–237

    Article  Google Scholar 

  • Hur J, Singh V (2014) Reexamining momentum profits: underreaction or overreaction to firm-specific information? Rev Quant Financ Acc. doi:10.1007/s11156-014-0469-x

    Google Scholar 

  • Hwang S, Rubesam A (2015) The disappearance of momentum. Eur J Financ 21:584–607

    Article  Google Scholar 

  • Jegadeesh N, Titman S (1993) Returns to buying winners and selling losers: implications for stock market efficiency. J Financ 48:65–91

    Article  Google Scholar 

  • Jegadeesh N, Titman S (2001) Profitability of momentum strategies: an evaluation of alternative explanations. J Financ 56:699–720

    Article  Google Scholar 

  • Jiang G, Li D, Li G (2012) Capital investment and momentum strategies. Rev Quant Financ Acc 39:165–188

    Article  Google Scholar 

  • Kadapakkam PR, Krause T, Tse Y (2015) Exchange traded funds, size-based portfolios, and market efficiency. Rev Quant Financ Acc 45:89–110

    Article  Google Scholar 

  • Liu LX, Zhang L (2008) Momentum profits, factor pricing, and macroeconomic risk. Rev Financ Stud 21:2417–2448

    Article  Google Scholar 

  • McLean RD, Pontiff J (2015) Does academic research destroy stock return predictability? J Financ. Available at SSRN: http://ssrn.com/abstract=2156623

  • Mech TS (1993) Portfolio return autocorrelation. J Financ Econ 34:307–344

    Article  Google Scholar 

  • Novy-Marx R (2012) Is momentum really momentum? J Financ Econ 103:429–453

    Article  Google Scholar 

  • Schwert GW (2003) Chapter 15 anomalies and market efficiency. In: Harris M, Constantinides GM, Stulz RM (eds) Handbook of the economics of finance. Elsevier, Amsterdam

    Google Scholar 

  • Wang CP, Huang HH, Tu KJ (2012) Unsystematic risk explanation to momentum profits in Taiwan. Rev Pac Basin Financ Mark Polic 15:115–143

    Google Scholar 

  • Wu Y (2011) Momentum trading, mean reversal and overreaction in Chinese stock market. Rev Quant Financ Acc 37:301–323

    Article  Google Scholar 

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Acknowledgments

We are grateful to C.F. Lee (the editor) and two anonymous referees for their valuable suggestions. We would also like to thank Raman Kumar for all his valuable comments. We would finally like to thank the seminar participants at Virginia Tech, the 2012 Midwest Financial Association meeting, and the 2013 FMA annual meeting for their comments.

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Authors and Affiliations

  1. Palumbo Donahue School of Business, Duquesne University, Pittsburg, PA, 15282, USA

    Debarati Bhattacharya & Gokhan Sonaer

  2. Department of Finance, National Central University, No. 300, Jung-da Rd., Jung-Li, Taoyuan, 320, Taiwan, ROC

    Wei-Hsien Li

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  1. Debarati Bhattacharya
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Correspondence to Debarati Bhattacharya.

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Bhattacharya, D., Li, WH. & Sonaer, G. Has momentum lost its momentum?. Rev Quant Finan Acc 48, 191–218 (2017). https://doi.org/10.1007/s11156-015-0547-8

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