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.     

Spillovers of volatility index: evidence from U.S., European,and Asian stock markets

We investigated the cross-market relations of volatility indexes with U.S. and non-U.S. stock market returns. We found that the pervasive VIX influence at both U.S. and non-U.S. stock markets. The VSTOXX and VKOSPI capture the major shocks to the global economy and show movements similar to the VIX. The empirical findings indicate that volatility index changes are important in explaining stock returns. We also examined spillover effects across volatility indexes. The VIX is a main transmitter, and the VKOSPI the main receiver, of these spillovers. The results point to a leading role for the VIX in the international market.     

Reuters Sentiment and Stock Returns

Sentiment from more than 3.6 million Reuters news articles is tested in a vector autoregression model framework on its ability to forecast returns of the Dow Jones Industrial Average stock index. We show that Reuters sentiment can explain and predict changes in stock returns better than macroeconomic factors. We further find that negative Reuters sentiment has more predictive power than positive Reuters sentiment. Trading strategies with Reuters sentiment achieve significant outperformance with high success rates as well as high Sharpe ratios.     

Industry-level determinants of the linkage between credit and stock markets

This paper examines the relationship between US credit default swaps (CDS) and stock returns on an industry-wide basis across a number of investment horizons, with particular focus on the major determinants of such a relationship. Wavelet analysis is first applied to extract the CDS–stock wavelet correlation for each US industry. Then, Bayesian Model Averaging is employed to identify the key driving factors of the industry CDS–stock wavelet correlations at short- and long-term horizons. The empirical results indicate that the wavelet correlations between the industry CDS and stock returns are primarily negative over time and across time scales. Moreover, the CDS–stock correlation at longer horizons exhibits a much more stable pattern than its counterpart at shorter time frames. The results also demonstrate that the volatility of US Treasury and stock markets, as measured by the MOVE and VIX indices, respectively, the volatility of volatility, as captured by the VVIX index, and US economic policy uncertainty, as measured by the EPU index, are the most robust determinants of the correlation between CDS and stock returns at shorter and longer horizons for most US industries. In contrast, the Fama–French systematic equity factors exhibit a practically negligible explanatory power on the CDS–stock link.     

Examination of the impacts of the immediate interest rate of the United States and the VIX on the Dow Jones Islamic Market Index

This study examines the effects of the market volatility index of the Chicago Board Options Exchange (VIX) and the immediate interest rate of the United States on the Dow Jones Islamic Market Index (DJIMI) using quantile-based techniques and wavelet coherence (WTC) analysis with monthly data for the period January 2010to May 2021. A quantile cointegration model indicated that the relationship between the VIX and the DJIMI can be valid in the long term since the estimated coefficients are negative and statistically significant across the quantiles 0.05 and 0.50, while a quantile autoregressive model revealed that large negative and positive changes in the VIX and the immediate interest rate of the United States do not have a significant impact on the DJIMI in the short term. Allowing the role of regime changes, it was found by the quantile regression model that an increase in the VIX lowers the performance in the DJIMI, supported by the WTC. It was also underlined that the DJIMI may not benefit from the positive financial conditions. According to the quantile regression models, the immediate interest rate of the US has asymmetrical effects, and the stabilizing effect of the increase/decrease is valid during bearish/bullish market conditions in the DJIMI.     

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”.     

Short interest and the stock market relation with news sentiment from traditional and social media sources

We examine how the stock market relation to news sentiment—from traditional and social media (Twitter) sources—interacts with short selling of stocks. Our sample includes the S&P500 constituents for the period January 2016 to December 2020, providing 704,452 firm-day observations. We find evidence that both news sources are positively related to returns. The relationship is stronger for firms with a high short interest ratio, for small firms, and particularly for firms that are both small and highly shorted. This is consistent with short sellers targeting firms that are most responsive to (negative) news releases and so more likely to compensate for the additional costs encountered in shorting.     

On the robustness of alternative rankings methodologies: Australian and New Zealand economics departments, 1988 to 2002

Theoretical considerations appear to support the conjecture that stock returns are positively related to growth in the long run. However, the empirical literature does not give unanimous support to the theory. Based on a stochastic general equilibrium model it is argued that the long-run relationship between stock returns and per capita income growth is ambiguous and depends on output volatility. Using a century of data for 20 Organization for Economic Co-operation and Development (OECD) countries it is shown that the relationship between stock returns and growth is positive over the period 1916–1951, in which output volatility was persistent. Outside this period no relationship between stock returns and growth is found. These findings are consistent with the predictions of the theoretical model.     

Bidirectional relationship between investor sentiment and excess returns: new evidence from the wavelet perspective

We propose to use the wavelet concept of the phase angle to determine the lead–lag relationship between investor sentiment and excess returns that are related to the bubble component of stock prices. The wavelet phase angle allows for decoupling short- and long-run relations and is additionally capable of identifying time-varying comovement patterns. Based on the monthly S&P500 index and two alternative monthly US sentiment indicators, we find that in the short run (until 3 months), sentiment is leading returns whereas for periods above 3 months the opposite can be observed. Moreover, the initially strong positive relationship becomes less pronounced with increasing time horizon, thereby indicating that the over- or undervaluation in the short run is gradually corrected in the long run.     

Oil Price Shocks and Volatility in Australian Stock Returns

This paper examines the effect of oil shocks on return and volatility in the sectors of Australian stock market and finds significant effects for most sectors. For the overall market index, an increase in oil price return significantly reduces return, and an increase in oil price return volatility significantly reduces volatility. An advantage of looking at sector returns rather than a general index of stock returns is that sectors may well differ markedly in how they respond to oil price shocks. The energy and material sectors (as expected) and the financial sector (surprisingly) are out of step (in different ways) with results for the other sectors and for the overall index. A rise in oil price increases returns in the energy and material sectors and an increase in oil price return volatility increases stock return volatility in the financial sector. Explanation for the negative (positive) association between oil return (oil return volatility) and returns (volatility of returns) in the financial sector must be based on the association via lending to and/or holdings of corporate bonds issued by firms with significant exposure to oil price fluctuations and their speculative positions in oil‐related instruments.     

The Time Varying Relation Between Consumer Confidence and Equities

We examine the impact of changes in consumer confidence measures on future stock index returns. Our analysis is built on the growing understanding that investor sentiment is an important factor in the stock market. By using frequency dependent regression methods, we show that there is a time-varying relation between consumer confidence and stock returns. Higher levels of consumer confidence imply greater returns in the short term but negative returns in the medium term. However, this effect is only observed for the small firm index. Moreover, there is evidence to suggest that consumer confidence is significantly affected by stock returns in reverse causality.     

Investor Pessimism and the German Stock Market: Exploring Google Search Queries

We analyze the relationship of retail investor sentiment and the German stock market by introducing four distinct investor pessimism indices (IPIs) based on selected aggregate Google search queries. We assess the predictive power of weekly changes in sentiment captured by the IPIs for contemporaneous and future DAX returns, volatility and trading volume. The indices are found to have individually varying, but overall remarkably high explanatory power. An increase in retail investor pessimism is accompanied by decreasing contemporaneous market returns and an increase in volatility and trading volume. Future returns tend to increase while future volatility and trading volume decrease. The outcome is in line with the conjecture of correction effects. Overall, the results are well in line with modern investor sentiment theory.     

不完全理性、投资者情绪与封闭式基金之谜

本文的主要贡献在于运用不完全理性投资者的情绪解析中国"封闭式基金之谜",并且论证投资者情绪是资产定价的重要因素。首先通过国内数据检验封闭式基金价格的过度波动说明国内投资者的不完全理性;其次提出了对LST(1991)的改进方法,通过提出假说与统计论证,解释了尽管国内封闭式基金的投资者结构与美国的不同,却有和LST类似的实证结果;进而利用其他反映情绪的指标间接证明封闭式基金折价是情绪指标;最后,检验情绪对市场收益的长期反向影响(长期收益反转)和情绪对短期市场收益的正向影响(短期收益惯性),论证了投资者情绪是资产定价的重要因素。     

Daily market news sentiment and stock prices

In recent years there has been a tremendous growth in readily available news related to traded assets in international financial markets. This financial news is now available through real-time online sources such as Internet news and social media sources. The increase in the availability of financial news and investor’s ease of access to it has a potentially significant impact on market stock price movement as these news items are swiftly transformed into investors sentiment which in turn drives prices. In this study, we use the Thomson Reuters News Analytics (TRNA) data set to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index constituents. We use these daily DJIA market sentiment scores to study the influence of financial news sentiment scores on the stock returns of these constituents using a multi-factor model. We augment the Fama–French three-factor model with the day’s sentiment score along with lagged scores to evaluate the additional effects of financial news sentiment on stock prices in the context of this model using Ordinary Least Square (OLS) and Quantile Regression (QR) to analyse the effect around the tail of the return distribution. We also conduct the analysis using the seven-day simple moving average (SMA) of the scores to account for news released on non-trading days. Our results suggest that even when market factors are taken into account, sentiment scores have a significant effect on Dow Jones constituent returns and that lagged daily sentiment scores are often significant, suggesting that information compounded in these scores is not immediately reflected in security prices and related return series. The results also indicate that the SMA measure does not have a significant effect on the returns. The analysis using Quantile Regression provides evidence that the news has more impact on left tail compared to the right tail of the returns.     

Fiscal policy and stock market efficiency: An ARDL Bounds Testing approach

The aim of this paper is to investigate the semi-strong market efficiency hypothesis with respect to fiscal policy information, in the context of the Bucharest Stock Exchange. Taking into account that macroeconomic data series of emerging countries usually have a limited size and may be plagued by inconsistencies and structural breaks, this paper proposes an ARDL Bounds testing approach for studying the relationship between stock returns and lagged macroeconomic variables. Moreover, this approach allows us to examine both the long and short-term relationship between fiscal policy and stock returns. The results indicate that, in the long run, stock prices fully and efficiently reflect information on past fiscal policy. However, in the short run, the Romanian stock market reacts efficiently only to unexpected fiscal policy news, while anticipated fiscal policy information displays a significant lagged relationship with current stock returns. In addition, the results also showed that monetary policy information is not incorporated efficiently into stock prices, both in the short and the long run, and its impact on stock returns is larger than the one exerted by fiscal policy.     

An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series

This article features an analysis of the relationship between the DOW JONES Industrial Average (DJIA) Index and a sentiment news series using daily data obtained from the Thomson Reuters News Analytics (TRNA) provided by SIRCA (The Securities Industry Research Centre of the Asia Pacific). The recent growth in the availability of on-line financial news sources, such as internet news and social media sources provides instantaneous access to financial news. Various commercial agencies have started developing their own filtered financial news feeds which are used by investors and traders to support their algorithmic trading strategies. TRNA is one such data set. In this study, we use the TRNA data set to construct a series of daily sentiment scores for DJIA stock index component companies. We use these daily DJIA market sentiment scores to study the relationship between financial news sentiment scores and the stock prices of these companies using entropy measures. The entropy and mutual information (MI) statistics permit an analysis of the amount of information within the sentiment series, its relationship to the DJIA and an indication of how the relationship changes over time.     

The risk-return relation and VIX: evidence from the S&P 500

A significantly positive risk-return relation for the S&P 500 market index is detected if the squared implied volatility index (VIX) is allowed for as an exogenous variable in the conditional variance equation of the parsimonious GARCH(1,1) model. This result holds for both daily and weekly observations, for extended conditional mean and variance specifications, and is robust to sub-samples. We show that the conditional variance obtained from the GARCH model with VIX has better predictive ability for realized volatility than the conditional variance from GARCH without VIX and VIX itself, thereby documenting an important information content of VIX for conditional variance. The results are interpreted as evidence that adding VIX squared in the conditional variance equation yields a better measure of conditional variance which, subsequently, uncovers a strong risk-return relation.     

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 sentiment,variance risk premium and delta-hedged gains

Delta-hedged gains are supposed to be negative and represent a volatility risk premium. Using a sample of Standard & Poor 500 index options from 2006 to 2009, this study documents two anomalies that cannot be explained by the volatility risk premium. First, delta-hedged gains are more negative for out-of-money options than for at-the-money options. Second, delta-hedged gains are significantly positive during financial crisis period. We propose a behavioural explanation in which both option prices and stock prices are affected by investor’s sentiment, but pessimistic sentiment has a greater impact on stock market than option market. This asymmetric response to pessimistic mood in turn affects the relative expensiveness of option prices.     

S&P 500 volatility,volatility regimes,and economic uncertainty

We assess the relationship between regime-dependent volatility in S&P 500, economic policy uncertainty, the S&P 500 bull and bear sentiment spread (bb_sp), as well as the Chicago Board Options Exchange's VIX over the period 2000–2018. Our findings from two-covariate GARCH–MIDAS (GM) methodology, regime switching Markov Chain, and quantile regressions suggest that the association of realized volatility and sentiment varies across high- and low-volatility regimes and depends on investors’ sensitivity toward incidents of market uncertainties under these regimes. The findings suggest that these indicators may not be useful in volatility forecasting, especially under high-volatility regimes.