Chinese institutional investors’ sentiment

We use daily survey data on Chinese institutional investors’ forecasts to measure investors’ sentiment. Our empirical model uncovers that share prices and investor sentiment do not have a long-run relation; however, in the short-run, the mood of investors follows a positive-feedback process. Hence, institutional investors are optimistic when previous market returns were positive. Contrarily, negative returns trigger a decline in sentiment, which reacts more sensitively to negative than positive returns. Investor sentiment does not predict future market movements—but a drop in confidence increases market volatility and destabilizes exchanges. EGARCH models reveal asymmetric responses in the volatility of investor sentiment; however, Granger causality tests reject volatility-spillovers between returns and sentiment.     

Investor Sentiment and Return Predictability of the Option to Stock Volume Ratio

We study the effect of investor sentiment on the relation between the option to stock volume ratio (O/S) and future stock returns. Relative option volume has return predictability under short sale constraints. For this reason, we expect and find a stronger O/S‐return relation during high sentiment periods than during low sentiment periods. We find that Baker and Wurgler's Investor Sentiment Index affects the O/S‐return relation after controlling for consumer sentiment indices and economic environment factors. While prior studies have used consumer sentiment indices as alternative measures of investor sentiment, our results suggest these effects are distinct.     

Consumer confidence indices and stock markets' meltdowns

Consumer confidence indices (CCIs) are a closely monitored barometer of countries' economic health and an informative forecasting tool. Using European and US data, we provide a case study of the two recent stock market meltdowns (the post-dotcom bubble correction of 2000–2002 and the 2007–2009 decline at the beginning of the financial crisis) to contribute to the discussion on their appropriateness as proxies for stock markets' investor sentiment. Investor sentiment should positively covary with stock market movements [DeLong, Shleifer, Summers, and Waldmann. 1990. “Noise Trader Risk in Financial Markets.” Journal of Political Economy 98 (4): 703–738]; however, we find that the CCI–stock market relationship is not universally positive. We also do not find support for the information effect documented in the previous literature, but identify a more subtle relationship between consumer expectations about future household finances and stock market fluctuations.     

Investor Sentiment and Stock Returns: Evidence from Turkey

This paper investigates the relation between investor sentiment and stock returns on the Istanbul Stock Exchange, employing vector autoregressive (VAR) analysis and Granger causality tests. The sample period extends from July 1997 to June 2005. In the VAR models, stock portfolio returns and investor sentiment proxies are used as endogenous variables. Two dummy variables accounting for natural and economic crises are used as exogenous variables. The analysis results suggest that, excepting shares of equity issues in aggregate issues, stock portfolio returns seem to affect all investor sentiment proxies, namely closed-end fund discount, mutual fund flows, odd-lot sales-to-purchases ratio, and repo holdings of mutual funds. Investor sentiment does not appear to forecast future stock returns; only the turnover ratio of the stock market seems to have forecasting potential.     

Sentiment stocks

To study how investor sentiment at the firm level affects stock returns, we match more than 58 million social media messages in China with listed firms and construct a measure of individual stock sentiment based on the tone of those messages. We document that positive investor sentiment predicts higher stock risk-adjusted returns in the very short term followed by price reversals. This association between stock sentiment and stock returns is not explained by observable stock characteristics, unobservable time-invariant characteristics, market-wide sentiment, overreaction to news, or changing investor attention. Consistent with theories of investor sentiment, we find that the link between sentiment and stock returns is mainly driven by positive sentiment and non-professional investors. Finally, exploiting a unique feature of the Chinese stock market, we are able to isolate the causal effect of sentiment on stock returns from confounding factors.     

The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach

Investor sentiment has become an important factor affecting oil price volatility and extreme risk. Therefore, we utilise a VaR-GARCH model to detect the extreme risk of the crude oil market during 2007–2017, and then explore the causality between investor sentiment and extreme risk in the crude oil market, and their lead-lag and co-movement relationships in the time-frequency domain. The empirical results show that: firstly, investor sentiment leads downside risk but lags the upside risk in the crude oil market; secondly, in the time domain, there is a co-movement between investor sentiment and extreme risk in the crude oil market, in particular, investor sentiment may Granger cause extreme risk in the crude oil market at the 1% significance level but not vice versa; thirdly, in the frequency domain, weak coherence can be found in high-frequency bands but increases in low-frequency bands during the whole sample period, which indicates that the impact of investor sentiment on extreme risk in the crude oil market will last for a long time, although the affected period tends to decrease.     

Intraday online investor sentiment and return patterns in the U.S. stock market

We implement a novel approach to derive investor sentiment from messages posted on social media before we explore the relation between online investor sentiment and intraday stock returns. Using an extensive dataset of messages posted on the microblogging platform StockTwits, we construct a lexicon of words used by online investors when they share opinions and ideas about the bullishness or the bearishness of the stock market. We demonstrate that a transparent and replicable approach significantly outperforms standard dictionary-based methods used in the literature while remaining competitive with more complex machine learning algorithms. Aggregating individual message sentiment at half-hour intervals, we provide empirical evidence that online investor sentiment helps forecast intraday stock index returns. After controlling for past market returns, we find that the first half-hour change in investor sentiment predicts the last half-hour S&P 500 index ETF return. Examining users’ self-reported investment approach, holding period and experience level, we find that the intraday sentiment effect is driven by the shift in the sentiment of novice traders. Overall, our results provide direct empirical evidence of sentiment-driven noise trading at the intraday level.     

Applications of a multivariate Hawkes process to joint modeling of sentiment and market return events

To investigate the complex interactions between market events and investor sentiment, we employ a multivariate Hawkes process to evaluate dynamic effects among four types of distinct events: positive returns, negative returns, positive sentiment, and negative sentiment. Using both intraday S&P 500 return data and Thomson Reuters News sentiment data from 2008 to 2014, we find: (a) self-excitation is strong for all four types of events at 15 min time scale; (b) there is a significant mutual-excitation between positive returns and positive sentiment and negative returns and negative sentiment; (c) decay of return events is almost twice as fast as sentiment events, which means market prices move faster than investor sentiment changes; (d) positive sentiment shocks tend to generate negative price jumps; and (e) the cross-excitation between positive and negative sentiments is stronger than their self-excitation. These findings provide further understanding of investor sentiment and its intricate interactions with market returns.     

Investor Trading Behavior and Sentiment in Futures Markets

This study investigates the effects of investor trading behavior and investor sentiment on futures market return. We find that the spot investor trading behavior, futures investor trading behavior, spot market sentiment, and futures market sentiment all have positive effects on daily futures returns in Chinese financial market. More importantly, we show that the effect of (spot) futures investor trading behavior has better explanatory power than (spot) futures market sentiment on futures returns. Further supporting our results, high investor trading behavior and high investor sentiment strengthen the positive relation between sentiment-returns and behavior-returns.     

Investor sentiment and return predictability of disagreement

This study examines the influence of investor sentiment on the relationship between disagreement among investors and future stock market returns. We find that the relationship between disagreement and future stock market returns time-varies with the degree of investor sentiment. Higher disagreement among investors’ opinions predicts significantly lower future stock market returns during high-sentiment periods, but it has no significant effect on future stock market returns during low-sentiment periods. Our findings imply that investor sentiment is related to several causes of short-sale impediments suggested in the previous literature on investor sentiment, and that the stock return predictability of disagreement is driven by investor sentiment. We demonstrate that investor sentiment has a significant impact on the stock market return predictability of disagreement through in-sample and out-of-sample analyses. In addition, the profitability of our suggested trading strategy exploiting disagreement and investor sentiment level confirms the economic significance of incorporating investor sentiment into the relationship between disagreement among investors and future stock market returns.     

Is there an Olympic gold medal rush in the stock market?

Investor sentiment and attention are often linked to the same non-economic events making it difficult to understand why and how asset prices are affected. We disentangle these two potential drivers of investment behaviour by analysing a new data-set of medals for the major participating countries and sponsor firms over four Summer Olympic Games. Our results show that trading volume and volatility are substantially reduced following Olympic success although returns appear to be largely unaffected. Analysis of data from online search volumes and surveys measuring investor sentiment also suggests that the market impact of the Olympics is linked to changes in attention.     

投资者情绪是导致我国投资者对市场信息出现反应偏差的原因吗：基于贝叶斯分位数回归模型的研究

本文使用贝叶斯分位数回归模型实证分析包含投资者情绪的投资者最优选择模型,结果表明：投资者情绪对于股票收益率存在非线性的正向影响,这是造成投资者对于市场信息出现反应偏差的一个重要原因.同时,市场信息和投资者情绪指标对于我国股票收益率都有着较大的影响作用;当股票出现不同涨跌幅时,市场信息对于股票收益率的影响有着较大的差异性.而考虑了投资者情绪指标之后,投资者对于市场信息的反应偏差明显减小,说明投资者情绪是造成我国投资者对于市场信息出现过度反应和反应不足的重要原因.我国投资者应该树立起良好的投资意识和心态,避免潜在的投资损失.     

我国投资者情绪与股票收益实证研究

根据投资者情绪是股票价格形成重要影响因素这一研究观点,围绕投资者情绪是否构成系统性风险及其对不同类型股票的差异化影响,运用我国股市交易数据进行的实证研究结果表明,投资者情绪不构成股市的系统性风险,但对不同市值的股票有着差异化的影响,随着股票的"投机性"增加,投资者情绪对其影响也增大.此外,投资者情绪会削弱股票收益与其波动的正相关性,且对于"投机性"越高的股票,这一影响也越大.     

Does investor sentiment predict bitcoin return and volatility? A quantile regression approach

The study investigates hypotheses relating to the effect of investor sentiment on predicting bitcoin returns and volatility. Using moments quantile regression, we present robust empirical evidence for the period 2017–2021. Our findings demonstrate that investor interest and emotions are significant predictors of bitcoin returns and volatility, while VIX and Bitcointalk.org forum are the most suitable predictors for representing investor emotions and interest, respectively. The findings also indicate a nonlinear relationship between investor sentiment and bitcoin returns and volatility, with predictable power changing based on the market conditions. Thus, the study enriches existing literature by providing empirical evidence to affirm the viability of behavioral finance theories in the bitcoin market and complements investors with more information to seek profits in different market conditions.     

Is VIX an investor fear gauge in BRIC equity markets?

This study examines the intertemporal relationships between CBOE market volatility index (VIX) and stock market returns in Brazil, Russia, India, and China (BRIC), and between VIX and U.S. stock market returns, to uncover if VIX serves as an investor fear gauge in BRIC and U.S. markets. We conduct the VIX-returns analysis for the 1993–2007 period.Our results suggest a strong negative contemporaneous relation between daily changes (innovations) in VIX and U.S. stock market returns. This relation is stronger when VIX is higher and more volatile. A significant negative contemporaneous relation between VIX and equity returns also exists for China and Brazil during 1993–2007 and for India during 1993–1997. Similar to the U.S. market, the immediate negative relation between the Brazilian stock returns and VIX changes is much stronger when VIX is both high and more volatile. Our results also indicate a strong asymmetric relation between innovations in VIX and daily stock market returns in U.S., Brazil, and China, suggesting that VIX is more of a gauge of investor fear than investor positive sentiment. However, the asymmetric relationship between stock market returns and VIX is much weaker when VIX is large and more volatile. These results have potential implications for portfolio diversification and for stock market and option trading timing in the equity markets of Brazil, India, and China. Overall, our results indicate that VIX is not only an investor fear gauge for the U.S. stock market but also for the equity markets of China, Brazil, and India.     

Trading behavior in S&P 500 index futures

This article examines the determinants of trading decisions and the performance of trader types, in the context of the E-Mini S&;P 500 futures and S&;P 500 futures markets. Speculators and small traders tend to follow positive feedback strategies while hedgers dynamically adjust positions in response to market returns. Such strategies apparently reverse during the 2008–09 financial crisis. Investor sentiment and market volatility play an important role in determining the net trading position of traders across the sample period. While all trader types are better at foreseeing market upturns, an out-of-sample test suggests that speculators and small traders have some predictive ability for short-term market returns.     

The systematic pricing of market sentiment shock

ABSTRACT

We show that market sentiment shocks create demand shocks for risky assets and a systematic risk for assets. We measure a market sentiment shock as the unexpected portion of the University of Michigan Consumer Sentiment Index’s growth. This shock prices stock returns in arbitrage pricing theory framework at 1% after controlling for market, size, value, momentum, and liquidity risk factors. Its premium lowered the implied risk aversion by 97.9% to 11.46 between 1978 and 2009 in our sentiment consumption-based capital-asset-pricing model. Merton’s [1973. “An Intertemporal Capital Asset Pricing Model.” Econometrica 41: 867–887]. intertemporal capital-asset-pricing model reconfirms our finding that this market sentiment shock is a systematic risk factor that provides investment opportunities.     

Social media effect,investor recognition and the cross-section of stock returns

Investor recognition affects cross-sectional stock returns. In informationally incomplete markets, investors have limited recognition of all securities, and their holding of stocks with low recognition requires compensation for being imperfectly diversified. Using the number of posts on the Chinese social media platform Guba to measure investor recognition of stocks, this paper provides a direct test of Merton's investor recognition hypothesis. We find a significant social media premium in the Chinese stock market. We further find that including a social media factor based on this premium significantly improves the explanatory power of Fama-French factor models of cross-sectional stock returns, and these results are robust when we control for the mass media effect and liquidity effect. Finally, we find that investment strategies based on the social media factor earn sizable risk-adjusted returns, which signifies the importance of the social media premium in portfolio management.     

Investor Sentiment and the Closed‐end Fund Puzzle: Out‐of‐sample Evidence

In this paper we examine the proposition that small investor sentiment, measured by the change in the discount/premium on closed‐end funds, is an important factor in stock returns. We conduct an out‐of‐sample test of the investor sentiment hypothesis in a market environment that is more likely to be prone to investor sentiment than the USA. We fail to provide supporting evidence for the claim of Lee et al. (1991) that investor sentiment affects the risk of common stocks. Consistent with Elton et al. (1998) , who show that investor sentiment does not enter the return generating process, our tests do not detect investor sentiment in a capital market that is more susceptible to small investor sentiment. Our results provide additional support against the claim that investor sentiment represents an independent and systematic asset pricing risk.     

Do investor expectations affect sell-side analysts’ forecast bias and forecast accuracy?

We examine the association between investor expectations and its components and sell-side analysts’ short-run quarterly earnings forecast bias and forecast accuracy. To measure investor expectations, we use the Index of Consumer Expectations survey and decompose it into the “fundamental” component related to underlying economic factors (FUND) and the “sentiment” component unrelated to underlying economic factors (SENT). We find that analysts are the most optimistic and the least accurate when SENT is higher. Management long-horizon earnings forecasts attenuate the effects of SENT on forecast optimism and forecast accuracy. Analysts are also the most accurate when FUND is higher. Last, the market places more weight on unexpected earnings when SENT is high. These findings suggest that analysts are affected by investor sentiment and the market reacts more strongly to unexpected earnings when analyst forecasts are the least accurate. The last result potentially explains why prior research (for example, Baker and Wurgler, The Journal of Finance 61:1645–1680, 2006) finds an association between investor sentiment and cross-sectional stock returns.