New Evidence on Psychology and Stock Returns

This article provides econometric evidence on the importance of psychological considerations for aggregate stock price fluctuations. To this end, a novel measure of stock market sentiment, dubbed the Net Psychology Index (NPI), based on information contained in Bloomberg News's end-of-the-day stock market reports, is confronted with a battery of multivariate empirical analyses. Results suggest that NPI is statistically different from popular sentiment proxies within the literature. NPI exhibits predictive power, increasing stock returns in the short run with this impact dissipating in the medium term. NPI does not exhibit asymmetric effects on returns for size- and momentum-related portfolios. A trading strategy based on NPI generates a statistically significant positive monthly return. Recursive out-of-sample fit analyses report a lower standard deviation of forecasting errors for NPI-based returns models versus competing accounts.     

Googling Investor’s Sentiment: Powerful Measure in Conventional and Islamic MENA Financial Markets

ABSTRACT

The objective of this paper is to investigate whether investors' sentiment measured by the Internet search behavior constitutes a valid measure of investor’s sentiment on Islamic and conventional indexes of emerging and frontier financial markets in MENA countries. In fact, we examine the relation between googling investor’s sentiment and monthly Islamic and conventional index returns during the period 2004–2016. Using the Dynamic Conditional Correlation, the BEKK-GARCH and the wavelet coherence models, we confirm that googling investor’s sentiment is a perfect indicator of investor’s sentiment measure. Indeed, we find that this measure has the ability to reflect major events such as subprime financial crisis, oil crisis and Arab spring revolution affecting MENA Islamic and conventional index markets. Our finding indicates that investors can use googling investor’s sentiment as an indicator to predict returns and volatility of emerging and frontier markets since it reflects the behavior and emotions of investors in MENA financial markets.     

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.     

A nonlinear Granger causality test between stock returns and investor sentiment for Chinese stock market: a wavelet-based approach

In this article, we re-examine the causality between the stock returns and investor sentiment in China. The number of net added accounts is used as a proxy for investor sentiment. To mimic the different investment horizons of market participants, we use the wavelet method to decompose stock returns and investor sentiment into time series with different frequencies. Additionally, we test for nonlinear causal relationships based on Taylor series approximation. Our results indicate that there is a one-directional linear causality from stock returns to investor sentiment on the original series, while there is a strong bi-directional nonlinear causality between stock returns and investor sentiment at different timescales.     

Sentiment dynamics and stock returns: the case of the German stock market

We use weekly survey data on short-term and medium-term sentiment of German investors in order to study the causal relationship between investors’ mood and subsequent stock price changes. In contrast to extant literature for other countries, a trivariate vector autoregression for short-run sentiment, medium-run sentiment, and stock index returns allows to reject exogeneity of returns. Depending on the chosen VAR specification, returns are found to either follow a feedback process caused by medium-run sentiment, or returns form a simultaneous systems together with the two sentiment measures. An out-of-sample forecasting experiment on the base of estimated subset VAR models shows significant exploitable linear structure. However, trading experiments do not yield convincing evidence of significant economic gains from the VAR forecasts, and it appears that predictability of returns from sentiment decreases during the recent market gyrations.     

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.     

South African stock return predictability in the context data mining: The role of financial variables and international stock returns

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-of-sample tests of predictive ability to further analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon. The model also highlights the role of multiple variables in predicting stock returns at medium- to long run horizons.     

Are exchange rates interdependent? Evidence using wavelet analysis

We examine the co-movement in daily returns of USD–INR, EUR–INR, GBP–INR, and JPY–INR currency pair futures contracts traded on the National Stock Exchange of India (NSE) using the wavelet cohesion approach. This study contributes to the literature by examining the scantly studied area of co-movement in exchange rates and using the wavelet approach, which allows us to analyse time–frequency-wise co-movement of the time series. The empirical results indicate that the currency futures markets are nearly perfectly integrated in the long run (monthly, quarterly and biannual scales) offering little potential gains from international portfolio diversification. The discrepancies between currency futures markets are small and almost fade away within 3–6 months. Moreover, international currency diversification might offer relatively higher potential gains at intraweek, weekly, and fortnightly time horizons owing to lower correlations among the currencies under consideration. Finally, our multiple-wavelet correlation and cross-correlation analysis shows that GBP acts as a potential leader/follower across scales. The results of our analysis indicate the dynamic pattern of co-movement among the major currency futures contracts, which provides several implications for portfolio managers and international investors participating in the Indian market.     

Does investor sentiment dynamically impact stock returns from different investor horizons? Evidence from the US stock market using a multi-scale method

This article uses the investor sentiment index to investigate the Granger causality between investor sentiment and stock returns for the US economy using a multi-scale method. To focus on the local analysis of different investor horizons, bivariate empirical mode decomposition is used to decompose time series of investor sentiment and stock returns at different timescales. We employ the linear and nonlinear integrated Granger causality method to examine the causal relationship of decomposed series on similar timescales. The results indicate both strong bilateral linear and nonlinear causality between longer-term investor sentiment and stock returns. However, there is no strong evidence for correlation of stock returns and investor sentiment on shorter timescales.     

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.     

心理还是实质:汶川地震对中国资本市场的影响

本文通过汶川地震这一独特自然事件,用公司与震中距离来衡量地震导致的投资者负面情绪(如焦虑和恐惧),研究汶川地震对中国资本市场造成的影响。与现有文献关于投资者情绪能够影响股票收益率的研究相一致,本文发现地震后12个月内(2008.6—2009.5),距离震中越近的公司,股票收益率越低。在控制了风险因素后,震中500公里以内的股票收益率显著为负,平均为每月-3%左右,而500公里以外的股票收益率不显著。并且公司与震中距离每增加1000公里,其年收益率平均升高3%。进一步分析发现,该现象地震前不存在,与系统风险的变化无关,并且不能由地震造成的实质经济损失来解释。总之,本文的研究表明汶川地震导致的投资者负面情绪能够影响股票收益率。     

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.     

A non-linear approach for predicting stock returns and volatility with the use of investor sentiment indices

The popular sentiment-based investor index S^BW introduced by Baker and Wurgler (2006, 2007) is shown to have no predictive ability for stock returns. However, Huang et al. (2015) developed a new investor sentiment index, S^PLS, which can predict monthly stock returns based on a linear framework. However, the linear model may lead to misspecification and lack of robustness. We provide statistical evidence that the relationship between stock returns, S^BW and S^PLS is characterized by structural instability and inherent nonlinearity. Given this, using a nonparametric causality approach, we show that neither S^BW nor S^PLS predicts stock market returns or even its volatility, as opposed to previous empirical evidence.     

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.     

Sign and phase asymmetry: News, economic activity and the stock market

In this paper, we present a general model of the joint data generating process underlying economic activity and stock market returns allowing for complex nonlinear feedbacks and interdependencies between the conditional means and conditional volatilities of the variables. We propose statistics that capture the long and short run responses of the system to the arrival of news, conditioning on the sign and time of arrival of the news. The model is applied to US data. We find that there are significant differences between the short and long run responses of economic activity and stock returns to the arrival of news. Moreover, for certain classifications of news, the respective responses of economic activity and stock returns vary according to the nature of the news and the phase of the business cycle at which the news arrives.     

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.     

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.     

Growth-returns nexus: Evidence from three Central and Eastern European countries

Using a sample of monthly data from January 1996 to December 2012, we provide new evidence on the unidirectional Granger causality from real stock market returns to real economic activity in three Central and Eastern European countries: the Czech Republic, Hungary, and Poland. By employing the Granger causality tests of Cheung and Ng (1996) and Hong (2001), we show evidence of short-term (up to 6 months), medium-term (up to 12 months) and long-term (up to 24 months) causality for the Czech Republic and Hungary. In the case of Poland, only medium-term and long-term causality is found. Using rolling-correlation analysis, we find that although the growth–returns relationship is positive during the examined period, there is an apparent variability in the strength of this relationship that is particularly visible during the period of the financial crisis in the late 2000s. Consequently, we find that the predictive power of stock markets in the CEE-3 countries increases during periods of high market volatility and decreases during periods of economic recovery.     

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”.