China’s stock market offers US investors attractive diversification opportunities. Diversifying to the China A-share market allows US investors to reduce volatility more than does reallocating to other foreign markets. The diversification power of China A shares is further enhanced by fundamentally weighting the stocks. Finally, rather than sacrificing average return, diversifying to China A shares actually produces a higher average return and Sharpe ratio.
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. Moreover, evidence suggests that analysts failed to anticipate the positive effect of greenness on firms’ earnings. These earnings surprises account for a substantial fraction of green stocks’ outperformance.
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. We construct a novel China E- S index by employing a wide range of non-standard public data and modern machine-learning algorithms. We find that Chinese firms with high E-S scores exhibit significant outperformance in recent years.
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.
我们构建了中国市场的规模和价值因子。规模因子排除了市值最小的30%的公司，这些公司因为能通过反向并购绕过严格的IPO限制而被认为具有潜在的壳价值。价值因子是以市盈率为基础的，该比率包含了账面价值对市值的比率，以反映中国所有的价值效应。在中国，我们的三因子模型在很大程度上是通过复制Fama and French (1993)的步骤而形成的模型。与该模型在市盈率因子上遗留了17%的年化alpha不同的是，我们的模型解释了大多数已知的中国市场异象，包括盈利和波动性异象。
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.
Beta异象，即高(低)beta股票的负(正)alpha，源于beta与异质波动率(IVOL)的正相关。在定价偏低的股票中，IVOL和alpha呈正相关关系，而在定价过高的股票中，IVOL和alpha之间呈现更强的负相关关系(Stambaugh, Yu, and Yuan, 2015)。这种更强的负相关与IVOL-beta正相关相结合，产生了beta异象。这种异象只有在定价过高的股票中才显着，而且只有在贝塔-IVOL相关性和定价过高的可能性同时较高的时期才会出现。无论是控制IVOL，还是简单地排除IVOL高的定价过高的股票，贝塔异象都不再显著。
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.
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.
投资者情绪对股价异象的影响是伪相关关系的概率极低。我们用Stambaugh，Yu and Yuan(2012)发现的回归中的模拟数据序列来代替投资者情绪，即在市场情绪高涨后，多空异象策略的利润更高，而这一利润完全来自策略的空头。在2亿个模拟回归变量中，我们发现没有一个变量能够像投资者情绪那样强烈地支持这些结论。市场异象之间的一致性非常关键。对于上述研究中检查的11个异常，在43个模拟回归变量中仅有1个对回归结果有预测能力。
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 the high-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.