Using earnings quality and firm characteristics to capture the informative signals about firm value, we measure information inconsistency as the variation across the information of the same company. Our findings add to the growing literature documenting the cross-section of stock return predictability due to the different speed of information incorporation across stocks. Dechow and Schrand (2004) find that high earnings quality is considered an accurate indicator of the current operating performance of a company.
Holthausen and Larcker (1992) and Lewellen (2004) suggest that firm characteristics are a strong indicator of the predictability of stock returns based on given financial information. We also examine the impact of inconsistent financial accounting information on the cross-section of stock returns. First, we extend the literature on the role of information quality in predicting asset returns with evidence on stock predictability, using earnings quality and firm characteristics as proxies to capture the informational signals about firm values. to lay.
Second, we contribute to the growing body of literature highlighting the impact of inconsistent financial accounting information on the cross section of stock returns. For example, Dechow, Ge and Schrand (2010) show that earnings quality should be seen as a function of firms' underlying performance.
Data and Variables
The results of Panel C show that three market state variables capture different dimensions of market information. The dependent variable in Eq. 4),RINCONSISTENT,t is the excess monthly return of the individual stocks with inconsistent information in month t. The independent interest variable is the excess monthly return of the portfolio with consistent information in month t-1 (RCONSISTENT,t-1).
Other independent variables include the excess monthly returns of the portfolio with inconsistent information in month t-1 (RINCONSISTENT,t-1) to control for the short-term reversal effect of Jegadeesh (1990); RMKT, SMB and HML are factor returns drawn from Kenneth French's website; and εt is the regression residual. The prediction is that the returns of information-consistent firms should be a significant and positive predictor of the returns of information-inconsistent firms after controlling for all control variables. 5), RCONSISTENT,t is the excess monthly return of the individual stock with consistent information in month t.
The independent interest variable is the excess monthly returns of the portfolio with inconsistent information in month t-1 (RINCONSISTENT,t-1). We expect that the returns of informationally inconsistent firms will not be a significant predictor of the returns of informationally consistent firms in the next month.
Empirical Results
For example, the prediction coefficient of RCONSISTENT,t-1 for the balanced portfolio is 0.185, indicating that a one standard deviation increase in RCONSISTENT leads to a 1% (ie, increase in RINCONSISTENT the following month (equivalent to a 12)% increase in annualized excess return). Meanwhile, the prediction coefficient for the value-weighted portfolio is 0.110, indicating that a one standard deviation increase in RCONSISTENT leads to a 0.68% (i.e. increase in RINCONSISTENT the following month (equivalent to an 8.16% increase in annualized excess return) The monthly R2 statistics for the significant predictors for the equal and value-weighted portfolios are both well above this threshold (8.40% and 8.40%, respectively).
The results show that returns from information-inconsistent firms do not significantly predict the following month's returns from information-consistent firms. According to these results in Table 5, a positive and significant coefficient on the interaction term of the lagged IA and the lagged excess monthly return of the information-consistent portfolio suggests that an increase in IA leads to an increase in the predictability that the portfolio's return on information-consistent firms can predict the results of. According to these results, the coefficients on the interaction terms of the lagged MLF and the lagged excess monthly return of the information-consistent portfolio are positive and significant, indicating that an increase in MLF leads to an increase in the return predictability between information-consistent and information-inconsistent stocks for the equal- and value-weighted portfolio with 0.10% (i.e. and 0.21% (i.e. resp.
For example, the coefficient on the interaction term between lagged IA and the monthly lagged excess return of the portfolio with inconsistent information suggests that a one standard deviation increase in the index of investor attention increases the prediction between equity stocks and the weighted portfolio. with 0.20% (i.e.) and 0.28% (i.e. respectively. Specifically, we use the four-factor Carhart (Carhart, 1997) and the five-factor Fama-French (Fama and French, 2015) instead of the three-factor Fama -French factor for equalizations For example, the coefficient on the interaction term between lagged TED and lagged monthly excess return of the portfolio with stable information suggests that a one standard deviation increase in the TED spread increases the prediction between stable information and - Inconsistent firms for the value-weighted portfolio with 0.15% (ie
The out-of-sample R2 of value-weighted and value-weighted stock return portfolios are positive (2.17% and 0.74%, respectively) and significant, according to McCracken's (2007) statistic. Our next out-of-sample tests use forecasts involving forecasts to compare the information content of the predictive regression forecast based on our model with that of the predictor. We use only the information available from the beginning to the end of the sample when calculating Eq. 7), so that our predictive regression analysis does not have a "look-ahead bias". regression forecast based on historical mean.
We estimate the following predictive regression between the top (ie, inconsistent information) and bottom (ie, consistent information) decile portfolios using a 10-year rolling window: 9), RINCONSISTENT,t, the excess monthly returns of the portfolio with inconsistent information in month t. The independent variable RCONSISTENT,t-1 is the monthly excess return of the portfolio with consistent information in month t-1. CER = 𝑅̅p – 0.5𝛾 𝜎𝑝2 , (11) where 𝑅̅p and 𝜎𝑝2 are the average and variance of the portfolio's return in the expected evaluation period.
Conclusion and Implications
Sloan (1996) shows that some investors are unable to fully incorporate the meaning of the accruals return of high accrual firms. The literature suggests that there is a negative relationship between earnings quality (approximated by accruals) and the degree of information asymmetry since high accruals result in a low quality of earnings and thus a higher degree of information asymmetry. Firms with higher (lower) earnings elasticity values show more (less) earnings elasticity relative to cash flow and accounting freedom.
Lo and MacKinlay (1990) find that portfolio returns of larger capitalization stocks lead while smaller ones mostly follow. That is, stock returns in small firms trail the release returns of large firms within. The dependent variables are the monthly excess return of individual stocks with inconsistent information (RINCONSISTENT,t) and the monthly excess return of individual stocks with consistent information (RCONSISTENT,t).
The explanatory variables are the lagged excess monthly return of the portfolio with consistent information (RCONSISTENT,t-1) and the lagged excess monthly return of the portfolio with inconsistent information (RINCONSISTENT,t-1). Time-varying return predictability based on market state variables with Carhart's four factors. The dependent variables are the excess monthly return of a single stock with inconsistent information (RINCONSISTENT,t).
The explanatory variables are the lagged monthly excess return of the portfolio with consistent information (RCONSISTENT,t-1), the lagged market state variables (MKT_STATE,t-1) include the index of investor attention (IA), the liquidity factor of the market (MLF), and TED Spread (TED), and the monthly lagged excess return of the portfolio with inconsistent information (RINCONSISTENT,t-1). Time-varying return predictability based on market state variables from the Fama and French five factors. Earn surprises, upside expectations and stock returns or don't let a profit torpedo sink your portfolio.
This table presents descriptive statistics of the monthly excess stock returns of a quintile portfolio constructed using standard deviation rank and ranking firms according to the level of transparency of information on financial accounting variables. RINCONSISTENT,t = α + β1 RINCONSISTENT,t-1 + β2 RINCONSISTENT,t-1 + βMKT RMKT,t + βS SMB,t + βH HML,t + εt, RCONSISTENT,t = α + β1 RINCONSISTENT,t-1 + β2 RCONSISTENT,t-1 + βMKT RMKT,t + βS SMB,t + βH HML,t + εt, The dependent variables are the excess monthly return of an individual stock with inconsistent information (RINCONSISTENT,t) and the excess monthly return of an individual stock with consistent information ( RCONSISTENT,t). The explanatory variables are the lagged excess monthly return of the portfolio with consistent information (RCONSISTENT,t-1) and the lagged excess monthly return of the portfolio with inconsistent information (RINCONSISTENT,t-1).
The explanatory variables are the lagged excess monthly return of the portfolio with consistent information (RCONSISTENT,t-1). The lagged market state variables (MKT_STATE,t-1) include investor attention index (IA), market liquidity factor (MLF), and TED Spread (TED), and the lagged excess monthly return of the portfolio with inconsistent information (RINCONSISTENT,t-1) . The second and third columns report the out-of-sample R2 for a predictive regression forecast of the portfolio's excess monthly returns with inconsistent information based on our forecast model versus the historical average benchmark forecast, on which statistical significance is based on the F statistics from McCracken (2007).
