Overnight stock returns,intraday returns,and firm-specific investor sentiment

In this study, we propose a new index for measuring firm-specific investor sentiment using overnight and intraday stock returns. We use actual equity data to construct the firm-level investor sentiment index and find that the new index has characteristics expected of a sentiment measure. In addition, we propose a novel sentiment-weighted trading strategy and apply it to momentum and short-term reversal strategies. We find that the sentiment-weighted trading strategy generates better performance in momentum and short-term reversal strategies. The sentiment-weighted trading strategy’s superior performance is evidence that our firm-level investor sentiment index possesses predictive powers with regard to future returns.     

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.     

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.     

Individual stock sentiment beta and stock returns

Using the data in Chinese stock market, we measure the individual stock sentiment beta, which is defined as the sensitivity of individual stock returns to the individual stock sentiment changes. We demonstrate that stocks in the highest individual stock sentiment beta portfolio have significantly higher excess returns, CAPM alpha, Fama-French three-factor alpha and Fama-French five-factor alpha. Besides, we find that the high individual stock sentiment beta stocks are smaller, younger, more volatile stocks with higher price and higher market beta. After controlling for firm characteristic, the returns of High-Low individual stock sentiment beta portfolios are still significantly positive. Moreover, we show the effect of the individual stock sentiment beta on stock returns is positive and significant in different stock markets, in different sample periods, and in bull and bear market. Besides, the results of the Bayes-Stein individual stock sentiment beta are still stable.     

Shock-dependent conditional skewness in international aggregate stock markets

This article uses the S_U-normal distribution to model the dynamic behavior of skewness in ten international aggregate stock indices—five indices each from developed and emerging markets. The conditional skewness process is specified as both autoregressive and dependent on lagged return shocks. Our primary result is that a negative return shock skews the time-varying distribution to the right for mature markets but to the left for emerging markets. In addition, we find that the asymmetry in volatility is noticeably larger in developed markets than in emerging markets. Finally, including the skewness process in modeling has no effect on the asymmetry and persistence in volatility obtained. These results are different from those of previous studies, which demonstrate the existence of both effects.     

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.     

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 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.     

Overnight stock returns and realized volatility

The information flow in modern financial markets is continuous, but major stock exchanges are open for trading for only a limited number of hours. No consensus has yet emerged on how to deal with overnight returns when calculating and forecasting realized volatility in markets where trading does not take place 24 hours a day. Based on a recently introduced formal testing procedure, we find that for the S&P 500 index, a realized volatility estimator that optimally incorporates overnight information is more accurate in-sample. In contrast, estimators that do not incorporate overnight information are more accurate for individual stocks. We also show that accounting for overnight returns may affect the conclusions drawn in an out-of-sample horserace of forecasting models. Finally, there is considerably less variation in the selection of the best out-of-sample forecasting model when only the most accurate in-sample RV estimators are considered.     

The impact of sentiment and attention measures on stock market volatility

We analyze the impact of sentiment and attention variables on the stock market volatility by using a novel and extensive dataset that combines social media, news articles, information consumption, and search engine data. We apply a state-of-the-art sentiment classification technique in order to investigate the question of whether sentiment and attention measures contain additional predictive power for realized volatility when controlling for a wide range of economic and financial predictors. Using a penalized regression framework, we identify the most relevant variables to be investors’ attention, as measured by the number of Google searches on financial keywords (e.g. “financial market” and “stock market”), and the daily volume of company-specific short messages posted on StockTwits. In addition, our study shows that attention and sentiment variables are able to improve volatility forecasts significantly, although the magnitudes of the improvements are relatively small from an economic point of view.     

The stochastic volatility in mean model: empirical evidence from international stock markets

In this paper we present an exact maximum likelihood treatment for the estimation of a Stochastic Volatility in Mean (SVM) model based on Monte Carlo simulation methods. The SVM model incorporates the unobserved volatility as an explanatory variable in the mean equation. The same extension is developed elsewhere for Autoregressive Conditional Heteroscedastic (ARCH) models, known as the ARCH in Mean (ARCH‐M) model. The estimation of ARCH models is relatively easy compared with that of the Stochastic Volatility (SV) model. However, efficient Monte Carlo simulation methods for SV models have been developed to overcome some of these problems. The details of modifications required for estimating the volatility‐in‐mean effect are presented in this paper together with a Monte Carlo study to investigate the finite sample properties of the SVM estimators. Taking these developments of estimation methods into account, we regard SV and SVM models as practical alternatives to their ARCH counterparts and therefore it is of interest to study and compare the two classes of volatility models. We present an empirical study of the intertemporal relationship between stock index returns and their volatility for the United Kingdom, the United States and Japan. This phenomenon has been discussed in the financial economic literature but has proved hard to find empirically. We provide evidence of a negative but weak relationship between returns and contemporaneous volatility which is indirect evidence of a positive relation between the expected components of the return and the volatility process. Copyright © 2002 John Wiley & Sons, Ltd.     

Excess volatility and the cross-section of stock returns

We document a reliable positive relation between excess volatility and the cross-section of stock returns over the sample period of 1963 to 2010. Significantly positive differentials have been found between the two decile portfolios with the largest and the least excess volatility, under all the situations we have examined. Size, value, and momentum effects cannot explain our empirical results. Likewise they cannot be explained by liquidity, bid-ask bounce, and risk-aversion-related inventory effects.     

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 fluctuation correlation between investor sentiment and stock index using VMD-LSTM: Evidence from China stock market

The impact of the investor sentiment on China’s capital market price volatility is concerned under the perspective of the behavioral finance. Firstly, in terms of the existing methods of establishing the investor sentiment index, the composite investor sentiment index which include six indicators (five objective indicators and a subjective indicator) are obtained. Secondly, VMD-LSTM (Variational Mode Decomposition and Long Short Term Memory) hybrid neural network model is used to decompose and restructure the investor sentiment index and the Shanghai Security Exchange Composite Index (SSEC) into the short-term, medium-term and long-term trend. Each trend is trained to obtain the forecasting results in three different time scales, and then to achieve the final predicting results by superimposing the output of each trend. Furthermore, compare with other prediction methods, the model can indeed improve the overall predicting accuracy. Finally, GARCH model and the co-integration error regression model are used to discuss the fluctuation correlation and VAR (Vector Auto-regression) models are established to analyze the causality between the stock market indices and the investor sentiment index.     

The conditional autoregressive Wishart model for multivariate stock market volatility

We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes an autoregressive moving average structure for the scale matrix of the Wishart distribution. It accounts for positive definiteness of covariance matrices without imposing parametric restrictions, and can be estimated by Maximum Likelihood. We also propose extensions of the CAW model obtained by including a Mixed Data Sampling (MIDAS) component and Heterogeneous Autoregressive (HAR) dynamics for long-run fluctuations. The CAW models are applied to realized variances and covariances for five New York Stock Exchange stocks.     

Risk-neutral skewness and market returns: The role of institutional investor sentiment in the futures market

This paper investigates the effect of index risk-neutral skewness on subsequent market returns and explores whether this effect will vary with various types of institutional investor sentiment in the futures market. Using index futures returns as the proxy of market returns, the empirical results show that the index risk-neutral skewness has a significantly negative effect on subsequent index futures returns. Moreover, the effect of institutional investor sentiment on subsequent index futures returns varies with various types of institutional investor sentiment. Finally, the effect of index risk-neutral skewness on subsequent index futures returns relies on various types of institutional investor sentiment.     

Wealth effect and investor sentiment

This paper investigated the relationship between the U.S. stock and housing markets as well as their influence on the wealth effect of consumption and found that the stock market sentiment index can explain changes in the wealth effect. The empirical results indicate that these two markets exert a wealth effect on consumption. The estimation results of the Markov-switching model indicate two states: a state in which the stock market influences its coexistence with the housing market and a state in which the housing and stock markets are unrelated. Public optimism regarding stock market investments affects the probability of transitioning between these states.     

Estimating and predicting multivariate volatility thresholds in global stock markets

We propose a general double tree structured AR‐GARCH model for the analysis of global equity index returns. The model extends previous approaches by incorporating (i) several multivariate thresholds in conditional means and volatilities of index returns and (ii) a richer specification for the impact of lagged foreign (US) index returns in each threshold. We evaluate the out‐of‐sample forecasting power of our model for eight major equity indices in comparison to some existing volatility models in the literature. We find strong evidence for more than one multivariate threshold (more than two regimes) in conditional means and variances of global equity index returns. Such multivariate thresholds are affected by foreign (US) lagged index returns and yield a higher out‐of‐sample predictive power for our tree structured model setting. Copyright © 2006 John Wiley & Sons, Ltd.     

Diversification discount and investor sentiment

While previous research has linked the diversification discount to suboptimal managerial decisions, recent empirical work and methods have shown these relationships are not as strong. A rational learning framework indicates the diversification discount is related to economic activity. Building on this framework, we test and find support for the hypothesis that investor sentiment explains the diversification discount. Investor sentiment favors riskier firms when sentiment is high, thereby increasing returns and relative valuations. As a result, diversified firms imputed value based on these multiples leads to a larger diversification discount and reverses when sentiment falls.     

COVID-19 and asymmetric volatility spillovers across global stock markets

In this study, I improve the assessment of asymmetry in volatility spillovers, and define six asymmetric spillover indexes. Employing Diebold-Yilmaz spillover index, network analysis, and my developed asymmetric spillover index, this study investigates the time-varying volatility spillovers and asymmetry in spillovers across stock markets of the U.S., Japan, Germany, the U.K., France, Italy, Canada, China, India, and Brazil based on high-frequency data from June 1, 2009, to August 28, 2020. I find that the global markets are well connected, and volatility spillovers across global stock markets are time-varying, crisis-sensitive, and asymmetric. Developed markets are the main risk transmitters, and emerging markets are the main risk receivers. Downside risk dominates financial contagion effects, and a great deal of downside risk spilled over from stock markets of risk transmitters into the global markets. Moreover, during the coronavirus recession, the total degree of volatility spillover is staying at an extremely high level, and emerging markets are the main risk receivers in the 2020 stock markets crash.