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Does firm life cycle stage affect investor perceptions? Evidence from earnings announcement reactions

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Abstract

This paper argues that firms in certain life cycle stages may be more subjectively valued by individual investors, leading to an optimistic bias in stock prices that is subsequently corrected upon the release of earnings news. Using a cash flow-based life cycle stage classification, introduction and decline stage companies exhibit three-day cumulative abnormal returns (CARs) around earnings announcements that are at least 112 bps lower than firms in growth, maturity, and shake-out stages. Specifically, introduction and decline stage stocks exhibit less positive reactions to positive earnings surprises and more negative reactions to negative earnings surprises relative to companies in other life cycle stages. Lottery stocks’ excess returns around earnings announcements (Liu et al. in Journal of Financial Economics 138: 789–817, 2020) also vary based on firm life cycle stage. Our findings suggest that individual investors’ optimistic expectations for introduction and decline stage stocks are met with disappointment when value-relevant earnings news is released. This study demonstrates that firm life cycle stage has real implications for stock price reactions to earnings announcements.

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Data availability

The data that support the findings of our study are available from Compustat, CRSP, I/B/E/S, OptionMetrics, and Thomson Reuters 13F. Restrictions apply to the availability of the data, which were used under university license.

Notes

  1. However, Miller and Friesen (1984) find that the sequence of life cycle stages is not always linear; for instance, a growth stage firm could enter the decline stage but then reverse course to the shake-out stage.

  2. Thus, it is free from discretion and judgment that is a necessary part of accruals-based earnings. We thank the referee for making this point.

  3. Day 0 is the trading day following overnight earnings announcements (89% of sample), and Day 0 is same trading day if announcements happen during the active trading hours (9:30 AM to 4:00 PM).

  4. The literature demonstrates that firm life cycle stage significantly impacts corporate policy, including decisions related to advertising, cash holdings, corporate restructuring, debt issuance, dividend policy, equity issuance, firm rigidity, leverage, and mergers/acquisitions (DeAngelo et al. 2006; Cohen et al. 2010; Owen and Yawson 2010; Dickinson 2011; Koh et al. 2015; Arikan and Stulz 2016; Faff et al. 2016; Loderer and Waelchli 2015; Loderer et al. 2017; Habib and Hasan 2019).

  5. Dickinson (2011) cautions against using quarterly life cycle assignment, due to the underlying seasonality of firms. We follow her methodology which uses annual cash flows to determine firm life cycle stage.

  6. Decline stage firms earn positive 13.5% in year t + 1, but this could be due to survivorship bias (Dickinson 2011). For this reason, Dickinson (2011) examines contemporaneous returns and finds that decline stage firms earn –7.8% in year t.

  7. A firm’s life stage, relative to the industry life stage, is also important, according to Cantrell and Dickinson (2020), who find that industry laggards earn greater operating returns relative to industry leaders.

  8. Hribar and Yehuda (2015) document market mispricing of accrual and cash flow information by firm life stage. However, they classify firms into only three stages (growth, maturity, decline) using sales growth, capital expenditure, firm age, and net capital transactions. They find that accruals generate a higher abnormal return in the growth stage, while free cash flows generate higher returns in maturity and decline stages. Their evidence suggests that cash flows subsume accrual mispricing in the maturity and decline stages but not in the growth stage. Vorst and Yohn (2018) replicate their main forecasting analysis using Hribar and Yehuda’s (2015) firm life stage-classification measure and find quantitively similar results.

  9. Under conservative accounting, risky investments with an uncertain payoff (such as research in R&D) are expensed on the income statement, reducing current earnings (Penman and Reggiani 2018). If these risky investments pay off, then future earnings will be higher, but this outcome is uncertain. According to Collins et al. (2014), growth firms are more likely than mature firms to exhibit conditional accounting conservatism, meaning that firms use a higher verification threshold to recognize good news as gains than to realize bad news as losses. Conditional conservatism is manifest in earnings asymmetric timeliness in accruals and operating cash flow. In other words, good news is less readily accepted as gains for young firms than older firms, while bad news is more readily accepted as losses for young firms than older firms, leading to asymmetry in cash flow from operations and returns for young firms.

  10. Berkman et al. (2009) use institutional ownership as a proxy for short sale constraints, following Nagel (2005). Because institutional investors are the primary lenders of shares, stocks with low institutional ownership are difficult to sell short (Berkman et al. 2009).

  11. In related work, Aboody et al. (2010) find that stocks in the highest percentile of prior 12-month returns exhibit a positive price run-up during the five trading days before their earnings announcements and a significant price decline in the five trading days afterward. These stocks tend to attract individual investors’ attention, where smaller traders have abnormal buyer-initiated trades before earnings announcements. However, Liu et al. (2020) establish that their findings are distinct from an attention-driven channel and are based on retail investors’ lottery preferences.

  12. Introduction and decline firms are more likely to report negative EPS and are less likely to pay dividends, making P/E valuation and dividend growth models difficult to implement. The younger age of introduction -stage firms means less historical data with which to estimate beta for a cost of equity calculation. In addition, operating cash flows are negative for introduction and decline stage firms (Dickinson 2011) and operating income volatility is high, which complicates free cash flow forecasting.

  13. In contrast, institutions avoid investing in lottery stocks due to prudent man rules (Fong and Toh 2014; Kumar and Page 2014).

  14. Anthony and Ramesh (1992) find the market response to unexpected sales growth and unexpected capital investment monotonically declines from the growth stage to the stagnant stage, but there is no pattern for responses to unexpected earnings.

  15. According to DellaVigna and Pollet (2009), earnings announcements prior to 1995 were recorded with an error of at least one trading day. Eight nine percent of total earnings announcements in our sample occur overnight, consistent with the work of Jiang et al. (2012), deHaan et al. (2015), and Lyle et al. (2021a). The overnight period is from 4:00 PM to 9:30 AM. Berkman and Truong (2009) emphasize the importance of accounting for after-hours announcements for event studies around earnings announcements. Michaely et al. (2016) find that earnings news announced within trading hours results in approximately 50% smaller immediate reactions, relative to those not announced during trading hours. Our untabulated results show little difference in the proportion of announcements made during trading hours across life cycle stages.

  16. A value of zero is assigned if no institutional ownership is reported for a stock.

  17. To address the issue of double counting of volume for NASDAQ stocks, we follow Anderson and Dyl (2005) and scale down the volume of NASDAQ stocks by 50% for 1996 and 1997 and 38% after 1997 to make it roughly comparable to the volume on the NYSE.

  18. In the internet appendix, we verify that the patterns identified are not concentrated among stocks with net losses (negative earnings per share) or related to extreme earnings surprises. In the regression analysis, we control for net losses and an ordinal standardized unexpected earnings (SUE) measure to address this concern directly. Also in the internet appendix, we verify that our results are not driven by the dot-com bubble or the financial crisis periods by excluding the years 1999–2002 and 2007–2009. The figure is qualitatively similar to Fig. 1, suggesting that unusual market activity during those periods is not driving our findings. Moreover, since the shake-out stage contains a variety of different cash flow patterns, we split that stage into two categories in the internet appendix. Shake-out I stage includes only those firms with positive cash flow from operations, positive cash flow from investing, and negative cash flow from financing. Shake-out II stage includes firms with all three positive cash flows or all three negative cash flows (operating, investing, and financing). This figure, however, suggests that the firms in the two shake-out stages experience similar earnings announcement reactions to the one shake-out stage.

  19. This methodology resembles that of DellaVigna and Pollet (2009), who split SUE > 0 firms into quintiles and SUE ≥ 0 firms into quintiles, and Hartzmark and Shue (2018), who split SUE into 20 bins. Unlike DellaVigna and Pollet (2009), we do not include a separate group for observations where the standardized unexpected earnings are exactly zero.

  20. This evidence is consistent with Berkman et al. (2009) who find some additional price run-up occurs over periods earlier than immediately before an earnings announcement.

  21. Liu et al. (2020) use earnings announcement dates from Compustat Quarterly over the 1972–2014 period. In their study, excess returns are calculated as buy-and-hold returns in excess of the CRSP-value weighted market index return. SUE is defined the difference in split-adjusted quarterly earnings per share between the current fiscal quarter and the same fiscal quarter in the previous year, divided by the standard deviation of this change over the previous eight quarters. Day 0 is defined as the earnings announcement day by Liu et al. (2020). We define Day 0 as the trading day after the earnings announcement for overnight announcements and as the same trading day for intraday announcements. (We have the time of day an announcement is made for our sample period.) Liu et al. (2020) also focus their analysis on the pre-event (–5,–1) and post-event (1,5) windows, rather than the event window (–1,1) as in our analysis.

  22. Liu et al. (2020) use quintiles rather than terciles. We use terciles because we split the firms further into five firm life cycle stages; quintiles would generate subsamples with too few observations.

  23. We repeat Fig. 4’s graphical analysis for non-lottery stocks in the internet appendix. Announcement reactions are similar for positive earnings surprises across firm life cycle stages (except for the decline stage), but introduction and decline stage companies clearly show the worst subsequent performance in response to negative earnings surprises.

  24. Compared with other life cycle stages, proportionally more (although not all) stocks in introduction and decline stages stocks are lottery-like. Yet the presence of some Low Lottery tercile stocks in introduction and decline stages suggests that, while the firm life cycle and lottery characteristics are related, life cycle stage is not simply a lottery proxy. While both relate to noise/retail trader participation, firm life cycle stage is a cash flow-based measure, while lottery characteristics are market-based measures. Each plays a distinct role in earnings announcement reactions. Moreover, we replicate the analysis in Table 4 using CAR(–5,–1) and CAR(+ 1, + 5) as dependent variables in the internet appendix. We find that lottery characteristics exacerbate the reversal for introduction and decline stage firms after earnings are released.

  25. We use relative short interest as a secondary measure of short sale constraints. Berkman et al. (2009) explain that the variation in the level of short interest across stocks could reflect differences in the transaction costs of shorting, and, for this reason, they use institutional ownership instead of short interest as a proxy for short sale constraints. The Introduction × RSI coefficient is insignificant in Panel A but is significant in Panels B and C. For introduction stage firms, higher relative short interest reduces stock return responses to positive earnings surprises but is associated with a milder negative response to negative earnings surprises. For a further exploration of the impact of RSI, we repeat Table 10 using RSI terciles (instead of Lottery terciles) in the internet appendix. We find that, around earnings announcements, the underperformance of High RSI terciles is concentrated in introduction and decline stage firms.

  26. We thank the editor for this observation.

  27. Under accounting principles, book-to-price (B/P) indicates expected returns, while “E/P is the relevant [risk] characteristic when there is no expected earnings growth, but the weight shifts to B/P with growth” (Penman et al. 2018, p. 488).

  28. Model 2 suggests that low B/P (low B/M) stocks have lower announcement reactions, and this effect is more pronounced for introduction stage firms. For a behavioral-based explanation, this result could relate to the relationship between B/M and skewness. Zhang (2013) demonstrates that low B/M stocks have significantly more positive skewness than high B/M stocks and implies that investors with lottery preferences prefer glamour (low B/M) stocks with high skewness. The positive B/P coefficient especially in Panel C is consistent with the work of Skinner and Sloan (2002), who find that low B/M stocks have more negative responses to negative earnings surprises.

  29. The ROA coefficient alone is negative and significant for All SUEs (Panel A), but this effect is concentrated in negative earnings surprises (Panel C). In Table 7, the ROA coefficient is insignificant for All SUEs and SUE ≥ 0 and is only marginally significant and negative for SUE < 0. (In addition, we control for Net Loss in all regressions.) Therefore, ROA has a weak negative or insignificant impact on earnings announcement reactions.

  30. As for the other variables, B/P, Accruals, and ΔNOA have positive but only marginally significant conditional effects on introduction stage firms but insignificant conditional effects for decline stage firms. For positive earnings surprises (Panel B), the Introduction × Accruals is positive and highly significant, suggesting that higher accruals are associated with conditionally higher announcement reactions to positive earnings surprises for introduction stage firms.

  31. We thank the editor for this suggestion.

  32. We do not simply use firm age to proxy for firm life cycle stage because firms do not move sequentially through the stages (Dickinson 2011). According to Dickinson (2011), “firms in the decline stage are likely to include young firms that succumb to initially high mortality rates” (p. 1975), and therefore firm age has an inverted U-shape across the stages. Thus firm-life cycle stage and firm age are distinct.

  33. We examine average abnormal volume for Day –5 to Day –1 (preannouncement), Day – to Day + 1 (announcement), and Day + 1 to Day + 5 (post-announcement). In the spirit of He and Li (2020) and Liu et al. (2020), abnormal volume is calculated as daily volume minus average volume scaled by average volume. Average volume is calculated as the average daily volume from trading Day –41 to Day –10 before each earnings announcement day.

  34. Introduction and decline stage firms have an average abnormal volume of 17.795% ((19.35 + 16.24)/2), while firms in the other stages have an average abnormal volume of 12.437% ((13.59 + 12.33 + 11.39)/3).

  35. We thank the referee for this suggestion.

  36. An advantage of Dickinson’s (2011) life cycle methodology is that it does not involve time-varying cutoffs between categories. For instance, an idiosyncratic volatility (IVOL) value that would classify a stock as “lottery” one year may not meet the “lottery” cutoff the next year. Portfolio managers will face this issue with most firm characteristics. In contrast, when stocks are neatly classified into five life cycle stages based on cash flows every year using Dickinson’s methodology, this firm life cycle identification largely encompasses many firm characteristic-based sorts.

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Acknowledgements

We appreciate helpful comments from the editor Stephen Penman, an anonymous referee, Anup Agarwal, Haosi Chen, Travis Davidson, Jon Fulkerson, Fabian Gamm, Barry Hettler, Jennifer Stevens, John Stowe, Tony Via, Xinyan Yan, Yu Hu, and seminar participants at Ohio University, 2020 University of Dayton Summer Finance Workshop, 2020 Financial Management Association Annual Meeting, and 2021 Eastern Finance Association Annual Meeting.

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Authors and Affiliations

  1. College of Business, Copeland Hall, Ohio University, Athens, OH, USA

    Andy Fodor

  2. College of Business, Muck Business Annex, Ohio University, Athens, OH, USA

    Kelley Bergsma Lovelace

  3. Pamplin College of Business at Virginia Tech, Blacksburg, VA, USA

    Vijay Singal

  4. College of Business, Ohio University, Muck Business Annex 202B, Athens, OH, 45701, USA

    Jitendra Tayal

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  1. Andy Fodor
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  2. Kelley Bergsma Lovelace
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  3. Vijay Singal
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  4. Jitendra Tayal
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Correspondence to Jitendra Tayal.

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Appendix

Appendix

Tables 9, 10 and 11.

Variable Definitions Appendix

Dependent Variables

DGTW return

The raw return minus the return on an equal-weighted characteristic-matched size, B/M, and momentum portfolio, following Daniel et al. (1997)

CAR(–1, + 1)

The cumulative DGTW announcement returns from Day –1 to Day 1

CAR(–5,–1)

The cumulative DGTW announcement returns from Day –5 to Day –1

CAR(+ 1, + 5)

The cumulative DGTW announcement returns from Day 1 to Day 5

Independent Variables and Other Variables

#Analysts

The most recent number of analysts’ estimates from I/B/E/S

Accruals

The sum of change in accounts receivable, change in inventory, and change in other current assets, minus the sum of change in accounts payable and change in other current liabilities, minus depreciation and amortization expense, all divided by average assets (Penman and Zhu 2022)

B/P

Book value per share divided by stock price (Penman and Zhu 2022)

Decline

A dummy variable equal to one if the firm is in the decline stage in the prior fiscal year. Following Dickinson (2011), companies are classified as in this stage if the firm exhibits negative cash flow from operations and positive cash flow from investing

Dispersion

The most recent standard deviation of analysts’ forecasts from I/B/E/S

Distress

The probability of default in Campbell et al. (2008)

Dividend Payer (%)

The percentage of firms that pay dividends

E/P

Earnings-to-price is earnings per share divided by stock price (Penman and Zhu 2022)

EXTFIN

Change in debt plus the change in equity from net equity transactions, scaled by average assets (Penman and Zhu 2022)

Firm Age

The number of years since the firm appeared in CRSP

Lottery

A ranked lottery variable using IVOL, MAX, ISKEW, and nPRC

ILLIQ

Amihud’s (2002) illiquidity measure

INCVOL

Operating income volatility (in percent). Following Berkman et al. (2009), INCVOL is measured as the standard deviation of the seasonally differenced ratio of quarterly operating income before depreciation divided by average total assets, measured over the 20 quarters prior to the current fiscal quarter. A minimum of eight quarterly observations is required

Introduction

Introduction is a dummy variable equal to one if the firm is in the introduction stage in the prior fiscal year. Following Dickinson (2011), firms are classified as in this stage if the company exhibits negative cash flow from operations, negative cash flow from investing, and positive cash flow from financing

Investment

Change in gross property, plant, and equipment plus change in inventory, all divided by lagged assets, as defined by Penman and Zhu (2022)

IO

Institutional ownership of the prior quarter

ISKEW

The scaled third moment of residuals from a factor model that contains market return over the risk-free rate (RMRF) and RMRF2 as factors

IVOL

Idiosyncratic volatility (IVOL) is defined as the second moment of the residuals by implementing Fama–French-Carhart (FFC) four-factor model on daily returns (Ang et al. 2006; Fama and French 1993; Carhart 1997). Following Fu (2009), a stock must have at least 15 trading days in the rolling window to calculate IVOL

LC Stage Persistence

The percentage of firms that remain in the same life cycle stage the next year

MAX

MAX is calculated as the average of the five highest daily returns of the stock during the rolling window, with a minimum of 15 daily return observations (Bali et al. 2017)

Momentum

The cumulative monthly stock return from month t – 12 to t – 1

Net Loss

A dummy variable equal to one if EPS < 0

Net Loss Firms (%)

The percentage of firms with net losses

nPRC

The negative of the natural logarithm of one plus stock price of six days ago

NSI

As defined by Penman and Zhu (2022), NSI is the natural log of the ratio of split-adjusted shares outstanding at the end of the fiscal year to shares outstanding at the end of the previous fiscal year

Optionable (%)

Percentage of firms with listed options

Price

The stock price six days prior to the earnings announcement

R&D/Sales

R&D expense divided by sales (Eberhart et al. 2004)

Reversal

The buy-and-hold return over the past 20 trading days

ROA

Income before extraordinary items and net interest divided by lagged assets (Penman and Zhu 2022)

RSI

Relative short interest (RSI) is defined as the ratio of total number of shorted shares to total number of shares outstanding at the end of the month

Sales Growth

Sales growth is the change in sales growth divided by sales of the prior year (Penman and Zhu 2022)

Size

Market capitalization

SUE

SUE is defined as earnings per share minus the median analyst forecast divided by the prior day’s stock price

SUE Decile

An ordinal variable ranging from 0 to 9, where firms in each SUE ≥ 0 and SUE < 0 subsample are divided into deciles and assigned rank of 0 to 9 based on SUE. For our regressions with All SUEs, SUE Decile ranges from 0 to 19 where a constant of 10 is added to SUE Decile for the SUE ≥ 0 subsample

Turnover

Turnover is calculated as monthly trading volume divided by the number of shares outstanding. To address the issue of double-counting of volume for NASDAQ stocks, we follow Anderson and Dyl (2005) and scale down the volume of NASDAQ stocks by 50% for 1996 and 1997 and 38% after 1997 to make it roughly comparable to the volume on the NYSE

ΔNOA

Change in net operating assets divided by average assets (Penman and Zhu 2022)

ΔR&D

Change in R&D expense divided by lagged R&D expense

Table 9 Addressing possible concern
Full size table
Table 10 Lottery characteristic tercile analysis
Full size table
Table 11 Dividend payer, firm age, or options availability
Full size table

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Fodor, A., Lovelace, K.B., Singal, V. et al. Does firm life cycle stage affect investor perceptions? Evidence from earnings announcement reactions. Rev Account Stud 29, 1039–1096 (2024). https://doi.org/10.1007/s11142-022-09749-2

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