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This paper aims to investigate a relatively new anomaly of investment growth and revisits well-known anomalies of size and value. It aims to answer two main research questions. First, can covariance risks (i.e. factor loadings) be excluded from being determining variables that drive return premiums and explain stock returns? Second, from a behavioral finance standpoint, the authors examine whether using firm characteristics is a more practical and accessible approach and also meets the necessary and sufficient conditions to analyze stock returns.

The authors create the investment-growth-based factor (LMH) which is defined as the return difference between low and high investment growth portfolios. The authors then incorporate the LMH factor along with other characteristic-based factors and their loadings into characteristic-balanced portfolio and three-factor model tests.

The authors find that covariance risks on investment growth, size and value are not necessary as determining variables. Instead, they find that behavioral-related firm characteristics of investment growth, size and value are necessary and sufficient as determinants of return premiums and stock returns.

The results have practical and useful implications for investors in their stock portfolio analysis and selection because firm characteristics are relatively more available than covariance risks that need estimation and typically contain measurement errors.

The paper has practical value to investors in their stock portfolio analysis and selection. Methodologically, in contrast to prior studies that do not directly use the investment growth to control for portfolio characteristics, the use of the newly created LMH factor and its loadings allows us to directly and properly test if the investment growth anomaly is related to the investment growth characteristic that is hypothesized to drive return premiums and determine stock returns from behavioral finance perspectives.

The authors investigate the extent to which online talk can influence contemporaneous and future stock trading, especially when market news is unpresented.

The authors propose an improved sentiment formula incorporating online hype, neutral sentiment and poster reputation. In addition, they conduct event study, OLS regression analyses and probit models.

First, investors tend to be more talkative in relation to firms that are (1) smaller size, (2) more growth-like, (3) with lower prices and higher short interests and (4) of higher beta. Second, the bullish tone of investors positively affects the abnormal returns of small-capitalization stocks. However, online talk has little impact on large-capitalization stocks, except that more postings boost trading liquidity. Third, online talk predicts the presence of future news regardless of firm size, with stronger predictive power found for small-capitalization stocks.

It is of interest to practitioners and researchers to study online talk so as to better understand the trading psychology of retail investors and the effects on the stock market. Furthermore, policymakers are interested in tracking activities on stock message boards in order to prevent security fraud and protect investors' interests.

The results are robust and suggest that online talk has significant impacts on stock trading exploiting an information asymmetry. This study of stock message board posting activities helps researchers to understand whether message contents contain valuable and unique content compared with information available via more traditional media channels.

Previous research has found that industry concentration and firm efficiency affect stock returns. However, it is not clear if concentration is a byproduct of efficiency and hence its effect on stock returns is driven by efficiency. This paper aims to examine the relationships between industry concentration, firm efficiency and average stock returns. Mainly, it aims to answer if the effects of industry concentration and firm efficiency on stock returns are independent and significant.

The stochastic frontier approach is used to estimate firm efficiency. The Herfindahl index is used to measure industry concentration. Regression and vector autoregressive analyses are performed to examine cross-sectional and lagged relationships between concentration, efficiency, profitability and stock returns. The characteristics-based benchmark approach is also used to investigate performance of test portfolios.

Industry concentration and firm efficiency have independent and significant effects on average stock returns through profit margins and market shares, which are related to firms’ profitability. Industry concentration has a greater positive impact on market shares than on profit margins, whereas firm efficiency has a greater positive impact on profit margins than on market shares. In sum, highly efficient firms in highly concentrated markets have lower distress risks and hence provide lower average stock returns.

The paper shows the linkages between industry concentration, firm efficiency, profitability and stock returns that have not been documented together in prior studies. Businesses can better understand the impact of concentration and efficiency on market shares and profit margins. Researchers may consider incorporating concentration and efficiency, both of which are meaningful microeconomic variables, into an asset pricing model. Investors can enhance their returns by having a zero-cost portfolio with long and short positions in stocks of firms with different levels of concentration and efficiency.