Abstract

We examine whether information acquisition costs affect retail investors’ overconfidence. Using the implementation of the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system as an exogenous event, we find that overconfidence, measured by retail investors’ trading activities and post-trade performance of stocks, is significantly reduced after firms join the EDGAR platform. The reduction in overconfidence is greater for young firms and growth firms. These findings shed light on the effect of information sets on the overconfidence of retail investors.

Abstract

We study the effect of disclosure on ex-ante stock crash risk by examining how management earnings forecasts affect investors’ subjective stock price crash risk. Using option-implied skewness as a proxy for ex-ante stock price crash risk, we find that, on average, investors’ subjective stock price crash risk decreases in the two days following the announcement of quarterly management guidance. However, ex-ante crash risk decreases when firms provide bad earnings forecasts (i.e., manager forecasts are lower than analyst consensus forecasts), and the reverse is true for good news forecasts. Moreover, the decrease in ex-ante skewness following negative news is more pronounced when managers provide quarterly forecasts or multiple guidance measures. The reduction in ex-ante stock price crash risk for bad news forecasts is more significant for firms with higher information asymmetry, whereas the increase for good news forecasts is more pronounced for firms with higher litigation risk. Finally, we demonstrate that changes in subjective stock price crash risk around management disclosure can predict realized stock price crash risk in the following month.

Abstract

Fundamental analysis aims at determining the fair values of firms as well as the mispricing. Using an agnostic approach to fundamental analysis, we show that a cross-sectional model built on the most recently reported 28 common accounting items provides strong out-of-sample predictions of future cash flows and stock returns. We find the stock return predictability of mispricing measure could be attributed to its ability in selecting stocks with high quality score. We also show that the derived mispricing measure can further predict future stock returns, whereas its prediction for future earnings is weak because undervalued stocks tend to be those overly reacted to unfavorable news with biased return expectations.

Abstract

By analyzing over 5 million past transactions in the Jakarta Stock Exchange, this study documents that foreign investors outperform their counterparts in terms of both annualized return and profit amounts. Further analyses reveal that the superiority of foreign investors is attributed to their sophistication level, where they are documented to be a better stock picker and have solid growth investing strategy. In addition, this study also utilizes both statistical and machine learning tools in predicting which local stocks to be included in foreign investors’ portfolios. The results suggest that while the accuracy of models built on artificial neural network significantly outperforms the accuracy of models built on logistic regression and discriminant analysis in the case of in-sample tests, the out-of-sample tests suggest the contrary. This finding is aligned with the current state of literature that emphasizes the importance of out-of-sample tests.