研究

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. 

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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.

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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.

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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.

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除了市场和规模因子外，具有两个“错误定价”因子的四因子模型比著名的四因子和五因子替代模型更好地解释了大量的异象。此外，我们的规模因子显示，小公司的溢价几乎是通常估计的两倍。错误定价因子通过在两个体现了最大的收益共同变化集合中取平均排名的方式在11个市场异象中汇总信息。投资者情绪预测了错误定价因子，特别是它们的空头组合，这与错误定价的解读以及做多和做空的不对称性是一致的。只有一个错误定价因子的三因子模型也表现良好，特别是在贝叶斯模型比较中。

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. 

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对于许多股票投资者来说，买入比做空更容易。将这种套利不对称性与以异质波动率(IVOL)为代表的套利风险相结合，解释了IVOL与平均收益之间的负相关关系。IVOL-收益关系在定价过高的股票中为负，而在定价过低的股票中为正，其错误定价是由11个收益率异象组合决定的。与套利不对称性一致的是，定价过高的股票之间的负相关性更强，特别是对于不太容易做空的股票，因此整体的IVOL-收益关系是负的。进一步支持我们的解释的是，较高的投资者情绪弱化了低估股票之间的正相关关系，并特别强化了高估股票之间的负相关关系。

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.

全市场关注的事件，如道琼斯指数的创纪录水平和有关股市的头版文章，预测了投资者的交易行为，进而预测市场回报。总体和个体层面的数据都显示，当股市价格处于较高水平时，全市场的高关注度事件会导致投资者大幅抛售所持股票。这种大举抛售对市场价格产生负面影响，在引人注目的事件发生后的几天里，市场回报率下降了19个基点。

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.

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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.

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这项研究探讨了投资者情绪在横截面股票收益中的一系列异常现象中的作用。我们考虑了全市场范围的投资者情绪的存在与由于做空限制导致的定价过高比定价过低更普遍的论调相结合的情况。利用市场异象的多空策略的盈利与该情况一致。首先，在市场情绪高涨之后，每个异象都会变得更显著(其多空策略更有利可图)。其次，在情绪高涨后，每种策略的空头部分都更有利可图。最后，情绪与这些策略的多头部分回报没有关系。

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.

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我们构建了六个主要股票市场的投资者情绪指数，并将其重组为一个全球指数和六个地区指数。在验证性检验中，我们发现相对情绪与双重上市公司在两地交易所的相对价格有关。全球情绪是国家层面回报的反向预测指标。全球和地区情绪都是该市场的横截面收益的反向预测指标：当情绪高涨时，相对难以套利和难以估值的股票的未来回报较低。个人资本流动导致情绪在各个市场间传播并形成全球情绪。

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.

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这篇论文显示了投资者情绪对市场风险收益权衡的影响。我们发现，股票市场的预期超额收益在情绪低落时期与市场的条件方差正相关，而在情绪高涨时期与市场条件方差无关。这些发现符合情绪化的交易者的特征，他们在情绪高涨的时期，破坏了本应该正向的风险收益权衡。我们还发现，在情绪低落的时期，收益率与同期波动性变化之间存在较强的负相关性。该发现与这些时期较强的正性事前关系是一致的。

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.

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