The Predictive Ability of Dividend and Earnings Yields for Long-Term Stock Returns

We use empirical models to examine the predictive ability of dividend and earnings yields for long‐term stock returns. Results show that dividend and earnings yields share a similar predictive power for future stock returns and growth. We find that the predictive power of dividend yields increases with the return horizon, but that yields forecast future returns and growth over a much longer horizon. Finally, dividend and earnings yields exhibit high autocorrelation and strong contemporaneous relations.     

CEO incentives and earnings prediction

This study investigates whether information about Chief Executive Officer (CEO) incentives is useful for predicting future earnings. We find that in companies with higher CEO equity incentives, current year earnings are more informative of future earnings than in other companies. Additionally, in an earnings prediction setting, CEO incentives are shown to provide information about future earnings that is incremental to current earnings or earnings components. The predictive power of CEO incentives for future earnings is robust to the inclusion of other predictors of future earnings. Furthermore, we find that CEO incentives are predictive of “real” future earnings, as represented by operating cash flow and non-discretionary accruals, but not predictive of future discretionary accruals. Finally, we find that financial analysts do not incorporate information about CEO incentives when they forecast future earnings. This result suggests that incorporating CEO incentives can potentially improve analyst forecasts of future earnings.     

The puzzling behavior of short sellers around earnings announcements

We examine the performance of ‘predictive’ and ‘reactive’ short sellers who take relatively large short positions immediately before and after quarterly earnings announcements, respectively. While both types short into advancing markets, it is surprising for reactive shorts since their trades are in stocks that just announced unexpected good news and thus, according to the post-earnings announcement drift anomaly, will subsequently have abnormally high cumulative returns. Nevertheless, we find that for both types of short sellers: (1) subsequent cumulative returns are significantly negatively related to the amount of abnormal short selling, suggesting they are informed, and (2) relative to non-earnings dates, the subsequent returns around earnings announcements are significantly more negative, indicating they appear to be adept at exploiting earnings announcements. Surprisingly, we find that the subsequent returns of reactive short sellers are significantly greater than those of predictive short sellers except for S&P 500 stocks, perhaps due to their greater analyst following. Importantly, we are left with two puzzles. First, reactive shorts would have significantly improved their performance had they based their trades on the size of standardized unexpected earnings (‘SUE’). Second, predictive shorts of Micro stocks would have significantly improved their performance had they simply waited until earnings were announced and then based their trades on SUE.     

Structural Breaks and Predictive Regression Models of Aggregate U.S. Stock Returns

In this article we examine the structural stability of predictiveregression models of U.S. quarterly aggregate real stock returnsover the postwar era. We consider predictive regressions modelsof S&P 500 and CRSP equal-weighted real stock returns basedon eight financial variables that display predictive abilityin the extant literature. We test for structural stability usingthe popular Andrews SupF statistic and the Bai subsample procedurein conjunction with the Hansen heteroskedastic fixed-regressorbootstrap. We also test for structural stability using the recentlydeveloped methodologies of Elliott and Müller, and Baiand Perron. We find strong evidence of structural breaks infive of eight bivariate predictive regression models of S&P500 returns and some evidence of structural breaks in the threeother models. There is less evidence of structural instabilityin bivariate predictive regression models of CRSP equal-weightedreturns, with four of eight models displaying some evidenceof structural breaks. We also obtain evidence of structuralinstability in a multivariate predictive regression model ofS&P 500 returns. When we estimate the predictive regressionmodels over the different regimes defined by structural breaks,we find that the predictive ability of financial variables canvary markedly over time.     

Dispersion in analysts’ earnings forecasts and credit rating

This paper shows that the puzzling negative cross-sectional relation between dispersion in analysts’ earnings forecasts and future stock returns may be explained by financial distress, as proxied by credit rating downgrades. Focusing on a sample of firms rated by Standard & Poor's (S&P), we show that the profitability of dispersion-based trading strategies concentrates in a small number of the worst-rated firms and is significant only during periods of deteriorating credit conditions. In such periods, the negative dispersion–return relation emerges as low-rated firms experience substantial price drop along with considerable increase in forecast dispersion. Moreover, even for this small universe of worst-rated firms, the dispersion–return relation is non-existent when either the dispersion measure or return is adjusted by credit risk. The results are robust to previously proposed explanations for the dispersion effect such as short-sale constraints and leverage.     

The disparity between long-term and short-term forecasted earnings growth

We find the disparity between long-term and short-term analyst forecasted earnings growth is a robust predictor of future returns and long-term analyst forecast errors. After adjusting for industry characteristics, stocks whose long-term earnings growth forecasts are far above or far below their implied short-term forecasts for earnings growth have negative and positive subsequent risk-adjusted returns along with downward and upward revisions in long-term forecasted earnings growth, respectively. Additional results indicate that investor inattention toward firm-level changes in long-term earnings growth is responsible for these risk-adjusted returns.     

Does social network sentiment influence the relationship between the S&P 500 and gold returns?

This study explored the relationship between investor sentiment (extracted from the StockTwits social network), the S&P 500 Index and gold returns. We investigated bilateral causality between gold prices and S&P 500 prices, the power of investor sentiment and gold returns to predict S&P 500 returns, and the influence of gold returns on S&P 500 volatility. We also considered whether the influence of sentiment varies according to the user's degree of experience. We considered the sentiment of messages that mentioned the S&P 500 Index and that users posted between 2012 and 2016. Granger causality analysis, ARIMA models and GARCH models were used for predicting S&P 500 Index returns and S&P 500 volatility. We observed a causal relationship between gold price and the S&P 500 Index. Our results also suggest that sentiment and gold returns predict S&P 500 Index returns. Finally, we observed that gold returns influence S&P 500 volatility and that the sentiment of experienced users affects S&P 500 returns.     

Value-relevance of pension transition adjustments and other comprehensive income components in the adoption year of SFAS No. 158

In the present study, we examine the value-relevance of pension transition adjustments and other comprehensive income (OCI) components in the initial adoption year of Statement of Financial Accounting Standard (SFAS) 158—Employers’ Accounting for Defined Benefit Pension and Other Postretirement Plans. Using a sample of 697 Standard and Poor (S&P) firms with the fiscal year ending on December 31, 2006, we perform several cross-sectional regression analyses to test the value-relevance of transition adjustments and OCI components in presence of various earnings measures. The results indicate that there is a negative relationship between both the level and change in stock returns and the magnitude of pension transition adjustments. We also find earnings measures and some OCI components are significantly associated with stock returns. When analyzed separately, we find our main results are mostly confined to the sample large S&P 500 firms. We do not find any result for the S&P mid-cap and small-cap firms. The overall results suggest the stock market negatively reacts to the adverse impact of SFAS #158 pension transition adjustments on net worth and future cash flows when the impact is substantial in its magnitude in dollar terms. The study further provides useful insight into the information processing by documenting that the market evaluates accounting information more effectively when such information is recognized in the financial statements rather than disclosed only in the financial footnotes.     

Predictive versus opportunistic earnings management,executive compensation,and firm performance

We examine the association between earnings management and an important component of corporate governance, the incentives provided through compensation. We argue that firms with predictive (opportunistic) earnings management, in which discretionary accruals do (do not) relate to future cash flows, provide a more (less) ideal setting for the use of compensation as incentives. Our empirical tests show that CEO compensation levels (measured by salary, bonus, and other forms of compensation) are positively related to predictive earnings management and negatively related to opportunistic earnings management. We also find that predictive earnings management is positively associated with future returns, whereas opportunistic earnings management is negatively associated with future returns. Overall, our results suggest that firms provide more incentives if their earnings are also more informative because of discretionary accruals.     

Long-term adjusted volatility: Powerful capability in forecasting stock market returns

We develop the long-term adjusted volatility (LV_ADJ) by removing the interference information of short-term volatility from the simple long-term volatility and examine the role of LV_ADJ in the predictability of stock market returns. Using a sample from January 2000 to December 2019 and considering 19 popular predictors, LV_ADJ positively predicts the next-month returns of S&P 500 and the univariate model with LV_ADJ has the best forecasting performance with adjusted in-sample r-squared of 3.825%, out-of-sample r-squared of 3.356%, return gains of 5.976%, CER gains of 4.708 and Sharpe ratio gains of 0.394. The predictive information of LV_ADJ is independent of that obtained from the 19 popular predictors. Furthermore, we find that LV_ADJ also has predictive power for long-term (3–12 months) stock returns, and can forecast returns of industry portfolios and characteristic portfolios.     

Book-to-Market Components, Future Security Returns, and Errors in Expected Future Earnings

This study investigates whether the ability of book-to-market to predict returns derives from systematic errors in the market's expectation of future earnings. We extend Beaver and Ryan (1996, 2000) by decomposing book-to-market into a more persistent (bias) component and a delayed recognition (lag) component. We find that both components are related to analyst expectations of future earnings, but the lag component is the dominant factor across all forecast horizons. Similarly, we find that the lag component explains most of the inverse relation between book-to-market and future returns. Given that lag is constructed by regressing book-to-market ratios on lagged price changes, our results are consistent with the lag component capturing systematic stock price reversals. We find that the components have unique relations with subsequent earnings forecast revisions, and controlling for these relations substantially mitigates the components' ability to predict returns. Our component-level analysis provides insight into how expected future earnings, summarized in book-to-market ratios help to explain this market anomaly.     

Scale effects of R&D as reflected in earnings and returns

Using returns to scale as a conceptual foundation, we explore how R&D-related earnings performance and earnings variability depend upon firm size. We find that the positive association between the level of future earnings and R&D intensity increases with firm size, and that the positive association between the volatility of future earnings and R&D intensity decreases with firm size, consistent with R&D productivity increasing with scale. We also show that R&D scale is associated with lower market returns, consistent with the idea that R&D investment risk declines with scale.     

Inflation,earnings forecasts,and post-earnings announcement drift

We examine whether financial analysts fully incorporate expected inflation in their earnings forecasts for individual stocks. We find that expected inflation proxies, such as lagged inflation and inflation forecasts from the Michigan Survey of Consumers, predict the future earnings change of a portfolio long in high inflation exposure firms and short in low or negative inflation exposure firms, but analysts do not fully adjust for this relation. Analysts’ earnings forecast errors can be predicted using expected inflation proxies, and these systematic forecast errors are related to future stock returns. Overall, our evidence is consistent with the Chordia and Shivakumar (J Account Res 43(4):521–556, 2005) hypothesis that the post-earnings announcement drift is related to investor underestimation of the impact of expected inflation on future earnings change.     

The predictive power of investment and accruals

We test whether investment explains the accrual anomaly by splitting total accruals into investment-related and “nontransaction” accruals, items such as depreciation and asset write-downs that do not represent new investment expenditures. The two types of accruals have very different predictive power for firm performance, not just for future earnings but also for future cash flow and stock returns. Most importantly, nontransaction accruals have the strongest negative predictive slopes for earnings and stock returns, contrary to the predictions of the investment hypothesis. A long-short portfolio based on nontransaction accruals has a significant average return of 0.71 % monthly from 1972 to 2010 and remains profitable at the end of the sample when returns on other accrual strategies decline. Our results suggest that nontransaction accruals are the least reliable component of accruals and show that a significant portion of the accrual anomaly cannot be explained by investment.     

Understanding analysts' use of stock returns and other analysts' revisions when forecasting earnings

We investigate analysts' use of stock returns and other analysts' forecast revisions in revising their own forecasts after an earnings announcement. We find that analysts respond more strongly to these signals when the signals are more informative about future earnings changes. Although analysts underreact to these signals on average, we find that analysts who are most sensitive to signal informativeness achieve superior forecast accuracy relative to their peers and have a greater influence on the market. The results suggest that the ability to extract information from the actions of others serves as one source of analyst expertise.     

The effect of R&D on future returns and earnings forecasts

Prior studies attribute the future excess returns of research and development activity (R&D) firms to either compensation for increased risk or to mispricing. We suggest a third explanation and show that neither the level of R&D investment nor the change in R&D investment explains future returns. Rather, the positive future returns that prior studies attribute to R&D investment are actually due to the component of the R&D firm??s realized return that is unrelated to R&D investment but present in R&D firms. Our results suggest that the excess returns of R&D firms are part of the larger value/growth anomaly. In addition, we show that while future earnings are positively associated with current R&D, errors in earnings expectations by investors and analysts are not related to R&D investment.     

The Predictive Power of "Head-and-Shoulders" Price Patterns in the U.S. Stock Market

We use the pattern recognition algorithm of Lo, Mamaysky, andWang (2000) with some modifications to determine whether "head-and-shoulders"(HS) price patterns have predictive power for future stock returns.The modifications include the use of filters based on typicalprice patterns identified by a technical analyst. With datafrom the S&P 500 and the Russell 2000 over the period 1990–1999we find little or no support for the profitability of a stand-alonetrading strategy. But we do find strong evidence that the patternhad power to predict excess returns. Risk-adjusted excess returnsto a trading strategy conditioned on "head-and-shoulders" pricepatterns are 5–7% per year. Combining the strategy withthe market portfolio produces a significant increase in excessreturn for a fixed level of risk exposure.     

Do changes in the SG&A ratio provide different information about changes in future earnings,analyst forecast revisions,and stock returns under different circumstances?

In fundamental analysis, increases (decreases) in the ratio of selling, general and administrative (SG&A) costs to sales (SG&A ratio) are perceived as negative (positive) signals regarding future firm performance. However, this interpretation focuses on the overall change in the SG&A ratio and ignores the underlying changes in the components of the ratio (sales and SG&A costs). Although prior research examines the changes in the SG&A ratio under some different circumstances, there is no study that examines all the ways that managers adjust costs in reaction to changes in sales. Therefore, I create six subsamples representing all possible combinations of changes in sales, SG&A costs, and the SG&A ratio and test whether changes in the SG&A ratio are informative about future earnings, analyst forecast revisions, and stock returns under these different circumstances. I find that changes in the SG&A ratio in four of my six subsamples provide information about changes in future earnings. I also find that analysts do not impound all of the information contained in the signals into their forecast revisions and in some cases investors appear to understand this fact.     

Do Decomposed Financial Ratios Predict Stock Returns and Fundamentals Better?

We investigate the prediction of excess returns and fundamentals by financial ratios, which include dividend‐price ratios, earnings‐price ratios, and book‐to‐market ratios, by decomposing financial ratios into a cyclical component and a stochastic trend component. We find both components predict excess returns and fundamentals. Cyclical components predict increases in future stock returns, while stochastic trend components predict declines in future stock returns in long horizons. This helps explain previous findings that financial ratios in the absence of decomposition find weak predictive power in short horizons and some predictive power in long horizons. We also find both components predict fundamentals.     

A frequency-domain alternative to long-horizon regressions with application to return predictability

This paper aims at improved accuracy in testing for long-run predictability in noisy series, such as stock market returns. Long-horizon regressions have previously been the dominant approach in this area. We suggest an alternative method that yields more accurate results. We find evidence of predictability in S&P 500 returns even when the confidence intervals are constructed using model-free methods based on subsampling.