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.     

How does the COVID-19 pandemic shape the relationship between Twitter sentiment and stock liquidity of US firms?

Using a new investor sentiment metric derived from Twitter, this paper examines how the pandemic's death rate influences the impact of investor sentiment on stock liquidity. Recent literature remains inconclusive regarding the effect of COVID-19 information and investor sentiment on financial markets. Using panel smooth transition regression (PSTR) for daily data on 338 listed firms in the S&P500 from January 2, 2020, to May 26, 2021, the findings reveal that the impact of Twitter sentiment on stock liquidity is nonlinear and changes over time and across firms in the function of the pandemic's death rate in the US. The results exhibit a threshold level of 4.32%, above which investor sentiment boosts stock liquidity. The speed of the transition from low to high pandemic death rate regime occurred abruptly rather than smoothly. This translates to severe changes in investor perception and demonstrates that investors are rapidly updating their beliefs during the COVID-19 outbreak.     

Asset pricing with investor sentiment: On the use of investor group behavior to forecast ASEAN markets

A study of investor behavior, using four investor groups (local, foreign, institutional, and dealer's accounts) on the Stock Exchange of Thailand (SET). The daily net purchases of each group are used as leading indicators for sentiment. The sentiments are examined with relation to each other and market returns. Eight proven macroeconomic factors with known cross-sectional relationships and known to forecast with returns are examined as a benchmark for the newly proposed sentiment factor model. Retesting the factors allows for an apples to apples comparison with the proposed sentiment factors. Using a VAR framework this research finds that dealers predominantly sell to institutional accounts, creating a negative correlation between the two groups, in addition to strong institutional herding which is all indicative of potential agency problems on the exchange. Also find that local individual accounts practice negative feedback trading and the other groups practice positive feedback trading. Of the four groups, the only group that influences the SET is the local individual group of investors. The foreign investor is found to be the least significant group on market returns, provide market liquidity to locals, and be the least responsive to daily market changes-following the prudent man rule. Lastly, propose a simple model, using investor behavior to accurately predict the market's direction for the following day 76 percent of the time with market timing ability (66 percent in Malaysia). This can be useful for buying and shorting the market.     

Valuation ratios and price deviations from fundamentals

This paper sheds light on US stock price deviations from fundamentals by analyzing the time-series dynamics of post-1870 S&P valuation ratios. It employs a non-linear, two-regime framework that allows for different behavior over phases of the stock market cycle. Persistence in the ratios implies prolonged price deviations from fundamentals stemming from short run continuation fueled by investor sentiment during bull markets. However, the pull from fundamentals ensures that valuation ratios and prices move toward their equilibrium levels in bear markets. Impulse response functions highlight sluggish adjustment and indicate that the effects of positive shocks are more pronounced and long-lasting in bull markets. The main conclusion is that, while market sentiment plays an important transitory role, valuation ratios do mean revert and so prices reflect fundamentals in the long run.     

Effects of investor sentiment and country governance on unexpected conditional volatility during the COVID-19 pandemic: Evidence from global stock markets

This paper first investigates the relationship between investor sentiment, captured by internet search behaviour, and the unexpected component of stock market volatility during the COVID-19 pandemic. According to data on 12 major stock markets, our research indicates a positive correlation between the Google search volume index on COVID-19 and the unexpected volatility of stock markets. The result suggests that greater COVID-19-related investor sentiment during this pandemic is associated with higher stock market uncertainty.Our study further examines whether country-level governance plays a role in protecting stock markets during this pandemic and reveals that the unexpected conditional volatility is lower when a country's governance is more effective. The impact of investor sentiment and country governance on unexpected volatility after the initial shock of COVID-19 is also investigated. The findings demonstrate the importance of establishing good country-level governance that can effectively reduce stock market uncertainty in the context of this pandemic, and support continual policy development related to investor protection.     

Commercial Real Estate Valuation: Fundamentals Versus Investor Sentiment

This paper investigates the role of fundamentals and investor sentiment in commercial real estate valuation. In real estate markets, heterogeneous properties trade in illiquid, highly segmented and informationally inefficient local markets. Moreover, the inability to short sell private real estate restricts the ability of sophisticated traders to enter the market and eliminate mispricing. These characteristics would seem to render private real estate markets highly susceptible to sentiment-induced mispricing. Using error correction models to carefully model potential lags in the adjustment process, this paper extends previous work on cap rate dynamics by examining the extent to which fundamentals and investor sentiment help to explain the time-series variation in national-level cap rates. We find evidence that investor sentiment impacts pricing, even after controlling for changes in expected rental growth, equity risk premiums, T-bond yields, and lagged adjustments from long run equilibrium.

How Much Does Investor Sentiment Really Matter for Equity Issuance Activity?

We study the extent to which investor sentiment matters for aggregate equity issuance activity. We focus on firms that are susceptible to investor sentiment and for which accurate measures of economic fundamentals are available. While sentiment on its own matters for equity issuance, it matters relatively little once we control for accurately measured fundamentals. Collectively, proxies for sentiment explain roughly 10 percentage points of the time-series variation of equity issuance beyond the roughly 40% explained by fundamentals. We conclude that investor sentiment does not seem to matter very much for aggregate equity issuance activity.     

Industry policy,investor sentiment,and cross-industry capital flow: Evidence from Chinese listed companies’ cross-industry M&As

This study investigates how the government’s industry policies affect investor sentiment, and whether the influenced investor sentiment guides corporate capital flow in the real economy. By examining a sample of cross-industry mergers and acquisitions (M&As) of Chinese listed companies, we find that industry policies promulgated by the government have a significant asymmetric influence on investor sentiment. Furthermore, investor sentiment under the exogenous shock of industry policies has a significant real effect on companies’ cross-industry M&A behavior, generating cross-industry capital flow. Additional analyses reveal that this effect arises because the acquirer depends on equity financing and has incentive to cater to investor sentiment. Our findings help clarify the effect of public policies on the stock market, theoretically, from the company’s micro-level perspective, as well as the mechanism by which stock market volatility transmits to the real economy.     

Herd behaviour & investor sentiment: Evidence from UK mutual funds

The aims of this paper are to detect evidence of institutional investor herding behaviour and examine the role that investor sentiment plays in institutional investor herding behaviour. The herding behaviour is investigated by examining the dispersion of time varying beta of UK open-end and closed-end funds. The study finds evidence of fund managers' herding behaviour, which suggests they are likely to herd on market portfolio, size, and value factors. UK market-wide investor sentiment index is used for investigating the effects of investor sentiment on institutional herding behaviour. We find a unidirectional investor sentiment effect on the herding of UK mutual fund managers. We also reveal that the sentiment factors affecting UK open-end and closed-end fund managers herding behaviour are different due to the differences in fund structure.     

What Can “Nine‐Eleven” Tell Us about Closed‐end Fund Discounts and Investor Sentiment?

We use the horrific events of September 11, 2001 (“nine‐eleven”) as a natural test of the hypothesis that closed‐end mutual fund discounts from fund net asset values reflect small investor sentiment. Because nine‐eleven was a sudden, unforeseen, and significantly negative and exogenous shock to the world, the capital markets, and investor sentiment, our test avoids many of the problems of extant studies. Discounts worsened dramatically following the event, and then recovered alongside the broader market. This finding is consistent with the hypothesis that discounts reflect the sentiment of small investors, who took their cues from the broader market's overall movement.     

US ADR and Hong Kong H-share discounts of Shanghai-listed firms

This paper examines the differential between the share prices of Chinese securities traded on their home market of Shanghai versus prices observed offshore in New York and Hong Kong. The discounts attached to Chinese securities, whether trading as ADRs on the NYSE or as H-shares on the Hong Kong market, appear to have been significantly influenced by changes in both exchange rate expectations and investor sentiment during 1998–2006. Expected exchange rate changes alone account for approximately 40% of the total variation in each case. This is combined with large cross-sectional variation, however, reflecting additional significant market-wide and company-specific sentiment effects.     

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.     

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 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.     

Consumer Confidence and Asset Prices: Some Empirical Evidence

We explore the time-series relationship between investor sentimentand the small-stock premium using consumer confidence as a measureof investor optimism. We estimate the components of consumerconfidence related to economic fundamentals and investor sentiment.After controlling for the time variation of beta, we study thetime-series variation of the pricing error with sentiment. Overthe last 25 years, investor sentiment measured using consumerconfidence forecasts the returns of small stocks and stockswith low institutional ownership in a manner consistent withthe predictions of models based on noise-trader sentiment. Sentimentdoes not appear to forecast time-series variation in the valueand momentum premiums. (JEL G10, G12, G14)     

Disentangling Sentiment from Cyclicality in Firm Capital Structure

This study examines the relationship between capital structure choices and investor and managerial sentiment, finding that periods of positive sentiment are associated with reduced leverage within firms. We focus on the cyclicality of leverage using non-orthogonalized sentiment indices and find a strong negative relationship. Leverage, therefore, appears countercyclical, implying that the decision to take on debt is a consequence of either Admati et al.'s (2018) ratchet effect or a managerial attempt to time the market. Our findings lead us to question some fundamental capital structure theories, namely, trade-off (Kraus and Litzenberger, 1973), and Hackbarth's (2008) managerial traits theory. Instead, we favour the idea that leverage is a consequence of countercyclical market timing behaviour.     

Equilibrium Exchange Rate Hedging

We assume a world like the one that gives the capital asset pricing model, but with many goods and many countries. We assume that investors in a given country have homothetic utility functions with the same weights, and a currency that has a sure end-of-period value using a price index with those weights. Siegel's paradox (derived from Jensen's inequality) makes investors want a positive amount of exchange risk. When average risk tolerance is the same across countries, every investor will hold the same mix of market risk (through the world market portfolio of all assets) and exchange risk (in a diversified basket of foreign currencies). In fact, the ratio of exchange risk to market risk is equal to the average investor's risk tolerance. We can write the ratio of exchange risk to market risk (and the fraction of the market's exchange risk that investors hedge) as depending on an average of world market risk premia, an average of world market volatilities, and an average of exchange rate volatilities. The weights in these averages are the same as the weights of the different countries in the currency basket. Given these averages, the ratio (and the fraction hedged) will not depend directly on exchange rate means or covariances. In equilibrium, we can use the ratio of exchange risk to market risk to measure average risk tolerance: in this model, risk tolerance is observable.     

Exchange-traded fund ownership and underlying stock mispricing

In response to the increasing proliferation of exchange-traded funds (ETFs), and a warning from the Wall Street hero Michael Burry that passive investing has put the stock market into ‘bubble’ territory, we examine the relation between stock ownership by ETFs and mispricing from 2002 to 2018. We find that increased ETF ownership induces overpricing in underlying stocks. We then identify three mechanisms for this relationship: the overpricing of stocks attributable to increased ETF ownership is stronger for stocks that experience an increase in passive ETF ownership; during periods characterised by high investor sentiment; and for illiquid stocks. Our results are robust to a battery of tests including alternative measures for all key variables and are not confounded by the global financial crisis. Additional analyses show that mispricing caused by ETF ownership change is not driven by firm fundamentals and does not exacerbate stocks' information environment around earnings announcement.     

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.