Daily happiness and stock returns: The case of Chinese company listed in the United States

Existing literature exclusively focuses on the association between local investor sentiment and local stock market performance. In this paper, we investigate the contemporaneous and the lead-lag relationship between local daily happiness sentiment extracted from Twitter and stock returns of cross-listed companies, i.e., the Chinese companies listed in the United States. The empirical results show that: 1) by respectively controlling for the firm capitalization, liquidity and volatility, there exists the largest skewness on the Most-happiness subgroup. (2) There exist bi-directional relationships between daily happiness sentiment and market variables, i.e., the stock return, range-based volatility and excess trading volume. (3) There are significantly positive stock returns, higher excess trading volume and higher range-based volatility around the daily happiness sentiment spike days. These findings not only suggest that there exists significant interdependence between online activities and stock market dynamics, but also provide evidence for the existence of “home bias”.     

Time-varying relationship of news sentiment,implied volatility and stock returns

I examine the relationship between aggregate news sentiment, S&P 500 index (SPX) returns, and changes in the implied volatility index (VIX). I find a significant negative contemporaneous relationship between changes in VIX and both news sentiment and stock returns. This relationship is asymmetric whereby changes in VIX are larger following negative news and/or stock market declines. Vector autoregression (VAR) analysis of the dynamics and cross-dependencies between variables reveals a strong positive relationship between previous and current period changes in implied volatility and stock returns, while current period and lagged news sentiment has a significant positive (negative) relationship with stock returns (changes in VIX). I develop a simple trading strategy whereby high (low) levels of implied volatility signal attractive opportunities to take short (long) positions in the underlying index, while extremely negative (positive) news sentiment signals opportunities to enter short (long) index positions. The investor fear gauge (VIX) appears to perform better than news sentiment measures in forecasting future returns.     

Where does return and volatility come from? The case of Asian ETFs

We analyze return and volatility of Asian iShares traded in the U.S. The difference in trading schedules between the U.S. and Asia offers a unique market setting that allows us to distinguish various return and volatility sources. We find Asian ETFs have higher overnight volatility than daytime volatility, explained by public information released during each local market's trading session. Local Asian markets also play an important role in determining each Asian ETF return. Nonetheless, returns for these funds are highly correlated with U.S. markets, indicative of the effects of investor sentiment and location of trade. Finally, returns in the U.S. market Granger-cause returns in all six Asian markets are analyzed.     

Investor sentiment,trading behavior and stock returns

This article examines how investor sentiment and trading behaviour affect asset returns. By analysing the unique stock trading dataset of the Korean market, we find that high investor sentiment induces higher stock market returns. We also find that institutional (individual) trades are positively (negatively) associated with stock returns, suggesting the information superiority (inferiority) of institutional (individual) investors. Investor sentiment generally plays a more important role in explaining stock market returns than investor trading behaviour.     

Foreign equity trading and emerging market volatility: Evidence from Indonesia and Thailand

This paper documents a strong contemporaneous relationship between foreign equity trading and market volatility in Indonesia and Thailand. Although foreign selling accounts for only a small portion of daily trading, it has the highest explanatory power for market volatility in both countries. Trading within foreign and local investor groups is often negatively related to volatility. The findings are robust to different sub-periods and different measures for volatility and trading activities. We explore two economic explanations for the asymmetric effects of foreign and local investors.     

Foreign equity trading and emerging market volatility: Evidence from Indonesia and Thailand

This paper documents a strong contemporaneous relationship between foreign equity trading and market volatility in Indonesia and Thailand. Although foreign selling accounts for only a small portion of daily trading, it has the highest explanatory power for market volatility in both countries. Trading within foreign and local investor groups is often negatively related to volatility. The findings are robust to different sub-periods and different measures for volatility and trading activities. We explore two economic explanations for the asymmetric effects of foreign and local investors.     

The importance of fear: investor sentiment and stock market returns

The presence of investor sentiment pushes asset prices away from the equilibrium level justified by underlying fundamentals. While sentiment is not directly observable, identifying appropriate proxies and, quantifying the impact of sentiment on asset prices is an important topic. Asset prices that do not appropriately reflect fundamental values may result in inefficient allocation of capital – impacting portfolio allocation decisions and the cost of capital. Utilizing a number of sentiment proxies, over the period 1990–2015, we demonstrate a strong relationship between investor sentiment and stock returns that is consistent with theoretical explanations of sentiment. We determine that implied volatility index (VIX) is the preferred measure of sentiment in terms of improving model fit and adding explanatory power. Causality tests suggest that investor fear (VIX) drives returns across firm-size and value, and also across industry. We also illustrate that firms that are more subjective to value, or face limits to arbitrage, such as small-cap stocks, or those in the business equipment (technology) or telecoms industry, are most responsive to changes investor sentiment. Finally, we demonstrate that sentiment has a greater influence on market returns during recession, when sentiment is at its lowest ebb, and this is particularly true for those stocks most susceptible to speculative demand.     

Beauty contest,bounded rationality,and sentiment pricing dynamics

We present a dynamic asset pricing model that incorporates investor sentiment, bounded rationality and higher-order expectations to study how these factors affect asset pricing equilibrium. In the model, we utilize a two-period trading market and investors make decisions based on the heterogeneous expectations principle and the “sparsity-based bounded rational” sentiment. We find that bounded rationality results in mispricing and reduces it in next period. Investor sentiment produces more significant effects than private signals, optimistic investor sentiment increases hedging demand, thus causing prices to soar. Higher-order investors are more rational and attentive to the strategies of other participants rather than private signals. This model also derives the dampening effect of higher-order expectations to price volatility and the heterogeneity expectation depicts inconsistent investor behavior in financial markets. In the model, investors' expectations about future price is distorted by their sentiment and bounded rationality, so they obtain a biased mean from the signal extraction.     

Does mixed-frequency investor sentiment impact stock returns? Based on the empirical study of MIDAS regression model

We examine whether mixed-frequency investor sentiment affects stock returns. In line with recent evidence from China, we find that the aggregate effect and the individual effect of mixed-frequency investor sentiment are statistically significant, and mixed-frequency investor sentiment is more important than the low-frequency one. Moreover, mixed-frequency investor sentiment, which is mixed by high-frequency data, can be more important than the market premium.     

Changes in Investors' Market Attention and Near-Term Stock Market Returns

We use Google Search volume to track changes investors' positive and negative market attention. Our results support the hypothesis that this information reflects investors' optimistic and pessimistic anticipation and can be used to predict near-term future returns. We find that changes in negative search term volume of “market crash” and “bear market” and changes in positive search term volume “market rally” explain near-term stock returns. Changes in investors' attention are partly related to past stock market returns, implying that investors are prone to pay attention to possible price reversals. These measures of market attention are potential gauges of investor sentiment.     

Order Flows,Investor Sentiments and Feedback Trade in Index Futures Market

This paper examines the presence of feedback trading, and investor sentiment drove feedback trading by traders in the Nifty 50 index futures contract in India. The results of the study using high-frequency data sampled at 10 min interval using VAR and contemporaneous VAR model as applied to market microstructure settings reveals negative evidence of feedback trade and investor sentiment-driven feedback trade in Nifty 50 futures contract. Further, consistency with noise trading hypothesis, order flows in Nifty 50 futures contract is less informative when traders are overly optimistic.

     

Two-period trading sentiment asset pricing model with information

We present a dynamic asset pricing model with investor sentiment and information, which shows that the investor sentiment plays a systematic and important role in the asset prices and the information is gradually incorporated into prices. The model has an analytical solution to the sentiment equilibrium price. We find that sentiment trading quantity not only increases the market liquidity, but also causes the asset prices' overreaction if the intensity of sentiment demand is more than a constant value. Therefore, the continuing overreactions result in a short-term momentum and a long-term reversal. The model could offer a partial explanation to some financial anomalies such as price bubbles, high volatility, asset prices' overreaction and so on.     

Investor sentiment and the prediction of stock returns: a quantile regression approach

We employ quantile regression to provide a detailed picture of the stock return forecasting ability of investor sentiment. We find that investor sentiment predicts aggregate stock returns at lower quantiles. However, the forecasting power is lost at upper quantiles. The results are robust after controlling for a comprehensive set of macroeconomic and financial predictors and for characteristic portfolios. We also show that investor sentiment consists mainly of cash flow news and contains little information about discount rate news. The ability to forecast cash flows increases gradually from the lower quantiles to upper quantiles. Our results do not support that the ability of investor sentiment to predict stock returns comes from a rational forecast of future cash flows.     

Predicting Equity Markets with Digital Online Media Sentiment: Evidence from Markov-switching Models

The authors examine the predictive capabilities of online investor sentiment for the returns and volatility of MSCI U.S. Equity Sector Indices by including exogenous variables in the mean and volatility specifications of a Markov-switching model. As predicted by the semistrong efficient market hypothesis, they find that the Thomson Reuters Marketpsych Indices (TRMI) predict volatility to a greater extent than they do returns. The TRMI derived from equity specific digital news are better predictors than similar sentiment from social media. In the two-regime setting, there is evidence supporting the hypothesis of emotions playing a more important role during stressed markets compared to calm periods. The authors also find differences in sentiment sensitivity between different industries: it is greatest for financials, whereas the energy and information technology sectors are scarcely affected by sentiment. Results are obtained with the R programming language. Code is available from the authors upon request.     

Investor Attention on the Social Web

We study investor attention through practitioners' tweeting behaviors. We develop formalisms of “cognitive niches,” heuristics from adaptive cognitive control, to account for the selectivity of investor attention. Using asset-specific tweets as direct measures of investor attention, we find evidence supporting contextual cognitive control, depending on asset types, investors' experience and investing approaches. We quantify attention contagion arising from the “social proof” heuristic, whereby the drawing power of the crowd in directing investor attention exceeds that of firm fundamentals. Finally, we demonstrate that different natures of investor attention (active or passive) reveals distinct patterns of trading volume, returns and volatility.     

Global and Extreme Dependence Between Investor Sentiment and Stock Returns in European Markets

The authors investigate the global and extreme dependence structure between investor sentiment and stock returns in 7 European stock markets (Belgium, France, Germany, Greece, the Netherlands, Portugal, and the UK), over the period 1985–2015. Global dependence refers to the correlation of changes in sentiment and stock returns over the whole range of these 2 variables, and extreme dependence refers to the local correlation of high (i.e. asymptotic) changes in sentiment and high stock returns. Using copula models and a bootstrap procedure, 6 statistical tests are performed for this purpose. Among the results of the tests, the authors highlight those that provide evidence of contemporaneous lower extreme dependence and contemporaneous upper extreme independence between sentiment and returns. As policy implications, these results suggest that financial stability can be promoted if regulators consider the impact of their decisions on investor sentiment. Also, the results seem to support the arguments in favor of short selling ban during turmoil periods. Finally, overall, the results are relevant for both investors and regulators and reinforce the importance of considering investor sentiment to better understand the behavior of financial markets.     

Investor attention and stock market activity: Evidence from France

The aim of this paper is to study the influence of investor attention on the French stock market activity and volatility. Following an original way, we construct a non-standard proxy of investor attention on the basis of investors' online search behavior exclusively provided by “Google insights for search”. We find that Google search volume is a reliable proxy of investor attention. Interestingly, we show that investor attention is strongly correlated to trading volume and is a significant determinant of the stock market illiquidity and volatility. Most importantly, this evidence is maintained even after controlling for the financial crisis effect.     

Threshold effect in the relationship between investor sentiment and stock market returns: a PSTR specification

This article verifies whether the hypothesis of heterogeneous agent modelling and the behavioural heterogeneity framework can reproduce recent stylized facts regarding stock markets (e.g. the 1987 crash, internet bubble, and subprime crisis). To this end, we investigate the relationship between investor sentiment and stock market returns for the G7 countries from June 1987 to February 2014. We propose an empirical non-linear panel data specification based on the panel switching transition model to capture the investor sentiment-stock return relationship, while enabling investor sentiment to act asymmetrically, non-linearly, and time varyingly according to the market state and investor attitude towards risk. Our findings are twofold. First, we show that the hypotheses of efficiency, rationality, and representative agent do not hold in reproducing stock market dynamics. Second, investor sentiment affects stock returns significantly and non-linearly, but its effects vary with the market conditions. Indeed, the market appears predominated by fundamental investors in the first regime. In the second regime, investor sentiment effect is positively activated, increasing stock returns; however, when their overconfidence sentiment exceeds some threshold, this effect becomes inverse in the third regime for a high threshold level of market confidence and investor over-optimism.     

Do dark pools amplify volatility in times of stress?

In this article, we explore the relationship between trading on dark pools and equity volatility in the context of the recent concerns about increase in frequency of market shocks combined with changes in market microstructure. In order to understand the potential role of dark pools in times of stress and implications for financial stability, it is essential to investigate the relationship between investor trading preferences and market volatility. For our analysis, we use data on daily trading volumes of FTSE100 stocks on dark and lit order books. We find evidence that dark pool trading has explanatory power in predicting volatility, implying that dark pools may affect the dynamics of price formation through liquidity. Our findings suggest that increased use of dark pools does not increase volatility, but may in fact lower it. Thus, dark pools may not be significantly detrimental to market stability in times of stress. This highlights the need for further analysis of the effects that shifting financial market structure might have on financial stability.     

Information contagion within small worlds and changes in kurtosis and volatility in financial prices

An agent based artificial market is developed to determine the impact of the interaction between investors on prices. It consists of sentiment investors, a single fundamental investor and a market maker. Sentiment investors live in a small world network and have limited liquidity. They trade based on their assessment of the future direction of the market. Consistent with the social learning literature, there are two types of sentiment investors; social learners and experts. Experts only consider private information while social learners also consider the views of neighbours. It is found that the interaction between the agents generate kurtosis and persistence characteristics of volatility in returns. In addition, the level of kurtosis and volatility depends on the inter-connectedness of the network as well as the number of experts and the number of connections from these experts to social learners. Cluster coefficient and characteristic path length analysis show that kurtosis and volatility are lowest within the small world region of the network. This effect is negated as the number of experts increases beyond a threshold.