The influence and predictive powers of mixed-frequency individual stock sentiment on stock returns

In this article, we construct mixed-frequency individual stock sentiment using MIDAS model. We first investigate the influence power of mixed-frequency individual stock sentiment on excess returns. The results indicate that the higher the frequency of individual stock sentiment is, the better it explains the variation of excess returns, that mixed-frequency individual stock sentiment, especially mixed high-frequency sentiment, exerts greater influence on excess returns than the same frequency one and that the mixed-frequency sentiment has a stronger explanatory power to the variation of excess returns than size factor, book-to-market factor, profitability factor and investment factor do. Then, we study the predictive content of mixed-frequency individual stock sentiment. The results show that the higher the frequency of individual stock sentiment is, the better the forecast performs. Moreover, by comparing the corresponding statistics in influence and predictive power models, we find that the influence power of mixed-frequency individual stock sentiment is more significant than its predictive power.     

Individual stock crowded trades,individual stock investor sentiment and excess returns

Recent behavioral asset pricing models and the popular press suggest that investors may follow similar strategies resulting in crowded equity positions to push prices further away from fundamentals. This paper develops a new approach to measure individual stock crowded trades, and further investigates the joint effects of individual stock crowded trades and individual stock investor sentiment on excess returns. Specifically, our results show that the combined effect of individual stock crowded trades and individual stock investor sentiment on excess returns is positive and significant, which reveals the importance of “anomaly factors” in asset pricing. Furthermore, our results suggest that increasing individual stock buyer-initiated crowded trades will increase excess returns simultaneously; however, increasing individual stock seller-initiated crowded trades will decrease excess returns simultaneously. Collectively, our results highlight the importance of individual stock crowded trades and individual stock investor sentiment on the formation of stock prices.     

Pricing the hedging factor in the cross-section of stock returns

We investigate the role of investors’ net hedging strategy (factor) in predicting stock returns and pricing the cross-section of individual stocks and equity portfolios. We estimate stock exposure to changes in the hedging factor and show that the hedging premium is driven by outperformance of stocks with large positive net hedging betas, which explains their higher average returns. We find the positive hedging premium indicates risk-averse investors demand extra compensation to hold stocks with higher equity risk premiums, and they are themselves willing to pay higher prices for stocks with positive hedging betas.     

Empirical evidence of conditional asset pricing in the Indian stock market

Studies of various alternative empirical asset pricing models have mostly concentrated on developed markets. However, despite the importance of this issue, surprisingly little is known about how different asset pricing models behave in emerging capital markets. The purpose of this paper is to determine the suitability of conditional compared to unconditional versions namely, the capital asset pricing model and the Fama-French three-factor model for the Indian stock market. The key distinction between the present empirical tests and previous tests is the application of the Kalman filter method for dynamic beta estimation in the Indian market. The findings indicate that the cross-sectional variation in expected returns is driven by mainly two firm characteristics size and book-to-market ratio.Unlike the unconditional model, the market beta is able to capture the variation of expected return in conditional model. The results imply that information has a role and investors use the prior belief and macroeconomic variables as predictive variables to determine the cost of capital. These results are supported by some recent findings that Fama-French three-factor model is the only multifactor model that consistently sources three different types of risk included in the list of anomalies.     

The impact of investor structure on stock price crash sensitivity: Evidence from China's stock market

Stock price crash sensitivity refers to the conditional probability of a stock crash when the market collapses. It focuses on individual stocks' sensitivity to the market crash and can affect stock pricing significantly. Although the crash sensitivity of China's stock market is very high as a whole (Weigert, 2016), different individual stocks show varying degrees of crash sensitivity. This paper, adopting the perspective of institutional investors, explores the reasons for the difference in crash sensitivity in China's stock market, and finds that: First, institutional investors' shareholdings is positively related to firms' stock price crash sensitivity. However, after dividing institutional investors into professional (represented by financial institutions) and non-professional institutional investors (represented by general legal persons), we find that only professional institutional investors' shareholdings is negatively related to firms' stock price crash sensitivity. Second, the impact of professional institutional investors on the crash sensitivity is influenced by stock liquidity and media sentiment: when the stock liquidity of listed companies is good or the media sentiment is strong, the negative impact of professional institutional investors on the crash sensitivity is accordingly high. This paper, by highlighting the investor structure, attempts a pioneering exploration of the influencing factors of the difference in stock price crash sensitivity in China. Our empirical results enrich research on stock price crash sensitivity and the heterogeneity of institutional investors. They can also serve to guide regulatory authorities' development of institutional investors and efforts to maintain market stability.     

Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search

We examine the ability of online ticker searches (e.g. XOM for Exxon Mobil) to forecast abnormal stock returns and trading volumes. Specifically, we argue that online ticker searches serve as a valid proxy for investor sentiment — a set of beliefs about cash flows and investment risks that are not necessarily justified by the facts at hand — which is generally associated with less sophisticated, retail investors. Based on prior research on investor sentiment, we expect online search intensity to forecast stock returns and trading volume, and also expect that highly volatile stocks, which are more difficult to arbitrage, will be more sensitive to search intensity than less volatile stocks. In a sample of S&P 500 firms over the period 2005-2008, we find that, over a weekly horizon, online search intensity reliably predicts abnormal stock returns and trading volumes, and that the sensitivity of returns to search intensity is positively related to the difficulty of a stock being arbitraged. More broadly, our study highlights the potential of employing online search data for other forecasting applications.     

The momentum and reversal effects of investor sentiment on stock prices

In this paper, we illustrate the real function relationship between the stock returns and change of investor sentiment based on the nonparametric regression model. The empirical results show that when the change of investor sentiment is moderate, the stock return is positively correlated with the change of investor sentiment, presenting an obvious momentum effect. However, the stock return is negatively correlated with the change of investor sentiment if the change of investor sentiment is dramatic, presenting significant reversal effects. Moreover, the degree of reversal effect caused by extremely optimistic sentiment is greater than that driven by extremely pessimistic sentiment, which shows a significant asymmetry. Our findings offer a partial explanation for financial anomalies such as the mean reversion of stock returns, the characteristic of slow rise and steep fall in China's stock market and so on.     

Horse race of weekly idiosyncratic momentum strategies with respect to various risk metrics: Evidence from the Chinese stock market

This paper focuses on the horse race of weekly idiosyncratic momentum (IMOM) with respect to various idiosyncratic risk metrics. Using the A-share individual stocks in the Chinese market from January 1997 to December 2017, we first evaluate the performance of the weekly momentum based on raw returns and idiosyncratic returns, respectively. After that the univariate portfolio analysis is conducted to investigate the return predictability with respect to various idiosyncratic risk metrics. Further, we perform a comparative study on the performance of the IMOM portfolios with respect to various risk metrics. At last, we explore the possible explanations to IMOM as well as risk-based IMOM portfolios. We find that 1) there are prevailing contrarian effect and IMOM effect for the whole sample; 2) the negative relations exist between most of the idiosyncratic risk metrics and the cross-sectional stock returns, and better performance is linked to idiosyncratic volatility (IVol) and maximum drawdowns (IMDs); 3) additionally, the IVol-based and IMD-based IMOM portfolios exhibit better explanatory power to the IMOM portfolios with respect to other risk metrics; 4) finally, higher profitability of IMOM as well as IVol-based and IMD-based IMOM portfolios is found to be related to upside market states, high levels of liquidity and high levels of investor sentiment.     

Predicting stock market crises using daily stock market valuation and investor sentiment indicators

The purpose of this paper is to develop a daily early warning system for stock market crises using daily stock market valuation and investor sentiment indicators. To achieve this goal, we use principal components analysis to propose a comprehensive index of daily market indicators that reflects stock market valuation and investor sentiment. Based on the comprehensive index, we employ a logit model with Ensemble Empirical Mode Decomposition to develop a daily early warning system for stock market crises. Finally, we apply the proposed system to the early warning for stock market crises in China. The in-sample forecasting results show that investor sentiment and the forecast horizon by Ensemble Empirical Mode Decomposition improve the forecasting performance of conventional early warning systems. The out-of-sample forecasting results indicate that the proposed warning system still has a good performance.     

The stock performance of America's 100 Best Corporate Citizens

We consider the stock performance of America's 100 Best Corporate Citizens following the annual survey by Business Ethics. We examine both possible short-term announcement effects around the time of the survey's publication, and whether longer-term returns are higher for firms that are listed as good citizens. We find some evidence of a positive market reaction to a firm's presence in the Top 100 firms that are made public, and that holders of the stock of such firms earn small abnormal returns during an announcement window. Over the year following the announcement, companies in the Top 100 yield negative abnormal returns of around 3%. However, such companies tend to be large and with stocks exhibiting a growth style, which existing studies suggest will tend to perform poorly. Once we allow for these firm characteristics, the poor performance of the highly rated firms declines. We also find companies that are newly listed as good citizens and companies in the Top 100 but outside the S&P 500 can provide considerable positive abnormal returns to investors, even after allowing for their market capitalization, price-to-book ratios, and sectoral classification.     

Price effects of steel commodities on worldwide stock market returns

We analyze the price effects of steel commodities on stock market returns in emerging and developed economies. These commodities have recently attained increased media exposure due to the rise in the U.S. steel import tariffs, which pose the threat of reducing global demand for steel products and, consequently, lowering prices abroad. However, little has been investigated on the impact of steel commodity prices on worldwide stock market returns. By performing structural VAR and GARCH techniques on a weekly-frequency time series from 2002 to 2015, we find positive and statistically significant effects of linear and non-linear steel commodity price shocks on real stock returns in the commodity markets. In the highly diversified financial markets such as U.S. and Germany, real stock returns do not significantly respond to steel commodity price shocks, although we find highly significant positive responses from developed economies such as Australia, Japan and South Korea. Results are robust to different model specifications. Our evidence suggests that higher tariffs on steel imports represent a larger disadvantage to commodity markets which are more largely impacted by steel commodity prices. We provide economic policy implications based on recent literature.     

Industries' heterogeneous reactions during the COVID-19 outbreak: Evidence from Chinese stock markets

This study examines the heterogeneous effects of the COVID-19 outbreak on stock prices in China. We confirm what is already known, that the pandemic has had a significant negative impact on stock market returns. Additionally, we find, this effect is heterogeneous across industries. Second, fear sentiment can directly cause stock prices to fall and panic exacerbates the negative impact of the pandemic on stock returns. Third, and most importantly, we demonstrate the underlying mechanisms of four firm characteristics and find that those with high asset intensity, low labor intensity, high inventory-to-revenue ratio, and small market value are more negatively affected than others. For labor-intensive state-owned firms, in particular, stock performance worsened because of higher idle labor costs. Finally, we created an index to measure the relative position of an industry in the supply chain, which shows that downstream companies were more vulnerable to the effects of the pandemic.     

中国市值规模效应研究

本文按市值大小对股票投资回报的差别进行了实证分析,发现股票投资回报明显具有市值规模特征。小规模股票投资回报超过大规模股票。     

Time-varying impact of monetary policy shocks on US stock returns: The role of investor sentiment

This paper investigates how monetary policy shock affects the stock market of the United States (US) conditional on states of investor sentiment. In this regard, we use a recently developed estimator that uses high-frequency surprises as a proxy for the structural monetary policy shocks, which in turn is achieved by integrating the current short-term rate surprises, which are least affected by an information effect, into a vector autoregressive (VAR) model as an exogenous variable. When allowing for time-varying model parameters, we find that, compared to the low investor sentiment regime, the negative reaction of stock returns to contractionary monetary policy shocks is stronger in the state associated with relatively higher investor sentiment. Our results are robust to alternative sample period (which excludes the zero lower bound) and model specification and also have important implications for academicians, investors, and policymakers.     

The impacts of investor sentiment on returns and conditional volatility of international stock markets

One of the main arguments of behavioral finance is that some properties of asset prices are most probably regarded as deviations from fundamental value and they are generated by the participation of traders who are not fully rational, thus called noise traders. Noise trader theory postulates that sentiment traders have greater impact during high-sentiment periods than during low-sentiment periods, and sentiment traders miscalculate the variance of returns undermining the mean-variance relation. The main objective of this research is to construct a model to evaluate the returns and conditional volatility of various stock market indexes considering the changes in the investor sentiment by measuring the effects of noise trader demand shocks on returns and volatility. EGARCH model is used to determine whether earning shocks have more influence on the conditional volatility in high sentiment periods weakening the mean–variance relation. This paper takes an international approach using weekly market index returns of U.S., Japan, Hong Kong, U.K., France, Germany, and Turkey. Weekly trading volumes of these indexes are regressed against a group of macroeconomic variables and the residuals are used as proxies for investor sentiment and significant evidence is found that there is asymmetric volatility in these market indexes and earning shocks have more influence on conditional volatility when the sentiment is high.     

Risk reporting and stock return in the UK: Does market competition Matter?

This study examines the extent to which market competition influences risk reporting practice. It also explores how market competition affects the usefulness of risk reporting. The automated textual analysis measures the level of risk reporting [how much to report] and its tone [how it is reported] of UK FTSE 350 firms. The abnormal stock return is used as a proxy for the usefulness of risk reporting. In contrast to the proprietary cost hypothesis, our results indicate that the level of risk reporting is a positive function of market competition. Besides, UK firms are likely to disseminate more (less) negative (positive) news about their risks when market competition increases. However, after examining the informativeness of this reporting, we provide evidence that the level of reported risk information does not significantly enhance the abnormal stock returns of UK firms. Nevertheless, the tone of the reported risks carries incremental information indicative of a firm’s abnormal stock return, especially when market competition decreases. The findings suggest that firms are likely to alleviate their proprietary costs by framing their reporting of risk information in a way that deters potential competitors from entering their market and that market competition diminishes the perceived informativeness of such reporting. The results provide implications for investors as they should not acknowledge the disclosure of higher risk information when asking for more corporate transparency, as it lacks informativeness. Besides, policymakers may impose extra compulsory requirements on the UK firms to avoid reporting overly optimistic risk news to protect investors and avoid the adverse effects of this reporting.     

Re-examining risk premiums in the Fama–French model: The role of investor sentiment

This paper reconstructs the Fama–French three-factor (F–F) model as a panel smooth transition regression (PSTR) framework to investigate the differentiated effects of investor sentiment proxies-the volatility index (VIX), credit default swap (CDS), and TED spread-on the three risk premiums. Sample period spans from 2003: 1Q to 2013: 4Q. Sample objects are 58 semiconductor companies listed on Taiwan Security Exchange Corporation. The empirical results report that stock returns display a nonlinear path, and the three risk premiums are time-varying, depending on different proxies of investor sentiment in different regimes. Market premiums fall as investors in stock markets show extreme optimism or extreme pessimism. Except in rare situations, the size premium is significant and decreases with the increase in the VIX. Returns in holding growth stocks dominate holding value stocks when the investors show extreme pessimism or optimism. However, in normal sentiment of investment, value stocks earn more returns than growth stocks.     

Monday returns and asset pricing

The seasonal patterns observed on Monday stock returns are still unexplained by different asset pricing models. We attempt to fill this gap in the finance literature by using the Fama-French (Journal of Financial Economics 33:3–56, 1993) risk factors to explain the Monday seasonal. The results in the study show that Monday returns are explained by risk factors such as the market return, the size of the firms, and the book-to-market ratios of firms.     

中国股市的最大值效应

本文证实我国股市的投资者们长期存在对股票历史业绩的反应过度现象。我们发现历史上拥有最高收益率的股票在之后业绩都表现不佳。其主要原因是我国股市的投资者们在做投资选择时都遵循一条简单的规则:即在其他条件都相同的情况下,选择拥有最高的历史收益率的股票进行投资。集中投资使拥有最高的历史收益率的股票被过高评价,导致其后来的业绩表现要比那些拥有较低历史收益率的股票差。我们称之为"最大值效应"。通过使用Fama and Macbeth(1973)横断面回归分析方法,我们确认了"最大值效应"要比CAPM理论,Blitz and Pim van Vliet(2007)发现的"波动性效应"等更为有效地解释我国股市横断面股票收益率。     

Governance and stock market performance

This study uses international asset pricing models to investigate the link between the quality of government institutions and the performance of global stock markets. The results demonstrate a significant positive association between stock market performance measures and the quality of the institutional environment. Performance measures examined for the cross-section of countries were the average monthly stock index excess returns and the Sharpe ratio. All measures of performance were adjusted for global and local risk factors known to explain their international variation. The quality of governance is also found to be negatively associated with stock market total risk and idiosyncratic risk, consistent with the notion that stable institutions are linked to reduced variations in equity returns. These findings suggest countries with better-developed governance systems have stock markets with higher returns on equity and lower levels of risk. The results lend support for the view that a precondition for financial market development is the improvement of the institutions which govern the process of exchange.