Mingshi’s strategy team embodies a strong culture of rigorous and cutting-edge academic research. Mingshi has one of the largest dedicated China A-share research teams in the region. We have over 30 people dedicated to China factor research.
During the recent half decade in China, stocks of green firms significantly outperformed stocks in the least environmentally friendly industries. Earnings play a key role in this performance. The average difference between announced annual earnings and the consensus forecast is positive for green stocks but negative for the dirtiest stocks.
China occupies a pivotal position in the global campaign against climate change and social inequality. Sustainable investment plays a key role in this campaign. While the US and European markets have experienced explosive growth in sustainable investment, China’s environmental and social (E-S) investments have just begun.
Quality stocks are those of financially healthy firms with strong and growing profits. Based on a quantitative measure of quality, previous evidence shows that quality stocks provide their investors with superior returns in the U.S. and other developed markets. We find the same to be true in China, especially when investing based on “expected” quality, a forecast of future quality.
We construct size and value factors in China. The size factor excludes the smallest 30% of firms, which are companies valued significantly as potential shells in reverse mergers that circumvent tight IPO constraints. The value factor is based on the earnings-price ratio, which subsumes the book-to-market ratio in capturing all Chinese value effects. Our three-factor model strongly dominates a model formed by just replicating the Fama and French (1993) procedure in China. Unlike that model, which leaves a 17% annual alpha on the earnings-price factor, our model explains most reported Chinese anomalies, including profitability and volatility anomalies.
The beta anomaly, negative (positive) alpha on stocks with high (low) beta, arises from beta’s positive correlation with idiosyncratic volatility (IVOL). The relation between IVOL and alpha is positive among underpriced stocks but negative and stronger among over- priced stocks (Stambaugh, Yu, and Yuan, 2015). That stronger negative relation combines with the positive IVOL-beta correlation to produce the beta anomaly. The anomaly is significant only within overpriced stocks and only in periods when the beta-IVOL correlation and the likelihood of overpricing are simultaneously high. Either controlling for IVOL or simply excluding overpriced stocks with high IVOL renders the beta anomaly insignificant.
A four-factor model with two “mispricing” factors, in addition to market and size factors, accommodates a large set of anomalies better than notable four- and five-factor alternative models. Moreover, our size factor reveals a small-firm premium nearly twice usual estimates. The mispricing factors aggregate information across 11 prominent anomalies by averaging rankings within two clusters exhibiting the greatest return co-movement. Investor sentiment predicts the mispricing factors, especially their short legs, consistent with a mispricing interpretation and the asymmetry in ease of buying versus shorting. A three-factor model with a single mispricing factor also performs well, especially in Bayesian model comparisons.
Buying is easier than shorting for many equity investors. Combining this arbitrage asymmetry with the arbitrage risk represented by idiosyncratic volatility (IVOL) explains the negative relation between IVOL and average return. The IVOL-return relation is negative among overpriced stocks but positive among underpriced stocks, with mispricing determined by combining 11 return anomalies. Consistent with arbitrage asymmetry, the negative relation among overpriced stocks is stronger, especially for stocks less easily shorted, so the overall IVOL-return relation is negative. Further supporting our explanation, high investor sentiment weakens the positive relation among underpriced stocks and, especially, strengthens the negative relation among overpriced stocks.
Market-wide attention-grabbing events — record levels for the Dow and front-page articles about the stock market — predict the trading behavior of investors and, in turn, market returns. Both aggregate and household-level data reveal that high market-wide attention events lead investors to sell their stock holdings dramatically when the level of the stock market is high. Such aggressive selling has a negative impact on market prices, reducing market returns by 19 basis points on days following attention-grabbing events.
Extremely long odds accompany the chance that spurious-regression bias accounts for investor sentiment's observed role in stock-return anomalies. We replace investor sentiment with a simulated persistent series in regressions reported by Stambaugh, Yu, and Yuan (2012), who find higher long-short anomaly profits following high sentiment, due entirely to the short leg. Among 200 million simulated regressors, we find none that support those conclusions as strongly as investor sentiment. The key is consistency across anomalies. Obtaining just the predicted signs for the regression coefficients across the 11 anomalies examined in the above study occurs only once for every 43 simulated regressors.
This study explores the role of investor sentiment in a broad set of anomalies in cross-sectional stock returns. We consider a setting in which the presence of market-wide sentiment is combined with the argument that overpricing should be more prevalent than underpricing, due to short-sale impediments. Long-short strategies that exploit the anomalies exhibit profits consistent with this setting. First, each anomaly is stronger (its long-short strategy is more profitable) following high levels of sentiment. Second, the short leg of each strategy is more profitable following high sentiment. Finally, sentiment exhibits no relation to returns on the long legs of the strategies.
We construct investor sentiment indices for six major stock markets and decompose them into one global and six local indices. In a validation test, we find that relative sentiment is correlated with the relative prices of dual-listed companies. Global sentiment is a contrarian predictor of country-level returns. Both global and local sentiment are contrarian predictors of the time-series of cross-sectional returns within markets: When sentiment is high, future returns are low on relatively difficult to arbitrage and difficult to value stocks. Private capital flows appear to be one mechanism by which sentiment spreads across markets and forms global sentiment.
This study shows the influence of investor sentiment on the market’s mean-variance tradeoff. We find that the stock market’s expected excess return is positively related to the market’s conditional variance in low-sentiment periods but unrelated to variance in high-sentiment periods. These findings are consistent with sentiment traders who, during the thehigh-sentiment periods, undermine an otherwise positive mean-variance tradeoff. We also find that the negative correlation between returns and contemporaneous volatility innovations is much stronger in the low-sentiment periods. The latter result is consistent with the stronger positive ex-ante relation during such periods.