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.     

中国股市收益、收益波动与投资者情绪

本文从我国股市的现实情况出发 ,构造理论模型证明 :投资者接受价格信号时表现出来的情绪是影响均衡价格的系统性因子。这一结论得到实际数据的支持 ,实证发现投资者情绪的变化不仅显著地影响沪深两市收益 ,而且显著地反向修正沪深两市收益波动 ,并通过风险奖励影响收益。研究结果表明 ,沪深两市不仅具有相同的投资者行为和风险收益特征 ,而且均未达到弱式有效 ,机构投资者是可能的噪声交易者风险源。     

Excessive Volatility is Also a Feature of Individual Level Forecasts

The excessive volatility of prices in financial markets is one of the most pressing puzzles in social science. It has led many to question economic theory, which attributes beneficial effects to markets in the allocation of risks and the aggregation of information. In exploring its causes, we investigated to what extent excessive volatility can be observed at the individual level. Economists claim that securities prices are forecasts of future outcomes. Here, we report on a simple experiment in which participants were rewarded to make the most accurate possible forecast of a canonical financial time series. We discovered excessive volatility in individual-level forecasts, paralleling the finding at the market level. Assuming that participants updated their beliefs based on reinforcement learning, we show that excess volatility emerged because of a combination of three factors. First, we found that submitted forecasts were noisy perturbations of participants’ revealed beliefs. Second, beliefs were updated using a prediction error based on submitted forecast rather than revealed past beliefs. Third, in updating beliefs, participants maladaptively decreased learning speed with prediction risk. Our results reveal formerly undocumented features in individual-level forecasting that may be critical to understand the inherent instability of financial markets and inform regulatory policy.     

Stock Returns and Volatility: an empirical study of Chinese stock markets

This paper uses GARCH models to analyse the relationship between returns and volatility on the Shanghai and Shenzhen Stock Exchanges in China. Empirical estimates using the sample data from 21 May 1992 to 2 February 1996 suggest that the variances of the returns in the two markets are best modeled by the GARCH-M (1,1) specification. Volatility transmission between the two markets (the volatility spill-over effect) is also found to exist. The results of one month ahead ex ante forecasts show that the conditional variances of the returns of the two stock markets exhibit a similar pattern.     

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.     

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.     

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.     

Stock return autocorrelations and predictability in the Chinese stock market—Evidence from threshold quantile autoregressive models

This paper applies the threshold quantile autoregressive model to study stock return autocorrelations and predictability in the Chinese stock market from 2005 to 2014. The results show that the Shanghai A-share stock index has significant negative autocorrelations in the lower regime and has significant positive autocorrelations in the higher regime. It attributes that Chinese investors overreact and underreact in two different states. These results are similar when we employ individual stocks. Besides, we investigate stock return autocorrelations by different stock characteristics, including liquidity, volatility, market to book ratio and investor sentiment. The results show autocorrelations are significantly large in the middle and higher regimes of market to book ratio and volatility. Psychological biases can result into return autocorrelations by using investor sentiment proxy since autocorrelations are significantly larger in the middle and higher regime of investor sentiment. The empirical results show that predictability exists in the Chinese stock market.     

股市收益率与波动性长期记忆效应的实证研究

股票市场长期记忆效应问题是近来金融实证研究的一个热点.多数的研究集中在收益率长期相关性的考察上,较少有对波动率序列的研究.然而,波动率的长期记忆性不仅会导致金融市场上的波动持久性特征,而且将对波动率的预测与衍生证券定价产生重要的影响.基于此,本文通过修正的R/S分析与ARFIMA模型对我国股市收益率及其波动性的长期相关性进行了实证研究.结果表明:中国股市具有显著的非线性特征,虽然收益率序列的自相关性较弱,但波动性序列却表现出显著的长期记忆效应.这一结论将为研究股票价格行为特征与金融经济学理论提供新的方向.     

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.     

Volatility transmission to the fine wine market

The goal of this paper is to explore volatility transmission from various markets to the fine wine market. Knowledge of these channels for transmitting volatility to the wine market allows practitioners to anticipate the future volatility and the consequences of a shock on the wine market, to develop their investment strategy and diversify their risk. We especially analyse the impact of U.S. markets (i.e. art, commodities, credit, financial and real estate) during the 2007–2017 period. We shed additional light on how the volatility of the fine wine market varies during an extended period including a financial crisis. Our results indicate that, in the short-term, volatility is transmitted with a negative effect through the financial and commodity markets and with a positive effect through the art, residential real estate, and credit default markets. In the long-term, the wine market is impacted by all other markets. We show that correlations are time-varying.     

我国股市波动的非对称性和杠杆效应研究

本文选取2006年1月4日到2008年12月31日期间上证综合价格指数日收益率和收益波动率的数据,建立二者变量指标的GARCH模型、AGARCH模型、EGARCH模型,对我国牛熊市轮替过程中股票市场波动的非对称性和杠杆效应进行实证分析。结果发现,股改后牛熊市期间我国股票市场的波动表现出显著的长记忆性、非对称性和杠杆效应,股票市场波动性对"利好"和"利空"消息呈现出不平衡性反应,我国股票市场出现了强市恒强、弱市恒弱现象。最后,从投资者心理预期、过度反应与反应不足、投资者构成和交易机制等方面对该结论进行了分析。     

Capital Market Openness and Output Volatility

In this paper, using data from 21 advanced and 81 developing countries during 1971–2010, we empirically examine the impact of capital market openness on output volatility. We find that opening of capital markets increases the output volatility of developing countries. Furthermore, we find that the main channel through which capital market openness increases volatility is currency and external‐debt crisis. Finally, we find that while Asian countries are less likely to experience a crisis, they become even more unstable than other developing countries once a crisis occurs. Our evidence strengthens the case for caution in developing countries' opening up of their capital markets.     

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.     

Dynamic sentiment asset pricing model

Conventional wisdom suggests that the equilibrium stock price is not affected by investor sentiment, and the equilibrium price at an early time is higher than the one at a later time. In contrast to this wisdom, we present a dynamic asset pricing model with investor sentiment and we find that investor sentiment has a significant impact on the equilibrium stock price. The equilibrium stock price, which is affected by pessimistic sentiment at time 0, may be lower than the one at time 1. Moreover, consistent with the reality stock market, our model shows that time varying sentiments can lead to various price changes. Finally, the model could offer a partial explanation for the financial anomaly of high volatility.     

Backtesting expected shortfall: evidence from European securitized real estate

Events such as the European sovereign debt crisis, terrorism and Brexit cause more uncertainty and volatility in capital markets. This encourages us to use both conditional and unconditional forecasts (backtests) for expected shortfall (ES) in 8 indices of listed European real estate securities and Real estate investment trusts (REITs). Using the method proposed by Du and Escanciano, we find that ES is generally superior to Value-at-Risk in describing and capturing risk during extreme events such as the financial crisis. Our results are important to regulators, risk managers and investors.     

Volatility Spillovers and Contagion from Mature to Emerging Stock Markets

This paper models volatility spillovers from mature to emerging stock markets, tests for changes in the transmission mechanism during turbulences in mature markets, and examines the implications for conditional correlations between mature and emerging market returns. Tri‐variate GARCH–BEKK models of returns in mature, regional emerging, and local emerging markets are estimated for 41 emerging market economies (EMEs). Wald tests suggest that mature market volatility affects conditional variances in many emerging markets. Moreover, spillover parameters change during turbulent episodes. In the majority of the sample EMEs, conditional correlations between local and mature markets increase during these episodes. While conditional variances in local markets rise as well, volatility in mature markets rises more, and this shift is the main factor behind the increase in conditional correlations. With few exceptions, conditional beta coefficients between mature and emerging markets tend to be unchanged or lower during turbulences.     

Tail risk in emerging markets of Southeastern Europe

This article examines the left-tail behaviour of returns on stocks in Southeastern Europe (SEE). We apply conditional extreme value theory (EVT) approach on daily returns of six stock market indices from SEE between 2004 and 2013. Predictive performance of value-at-risk (VaR) and expected shortfall (ES) based on EVT is compared against several alternatives, such as historical simulation and analytical approach based on GARCH with a single conditional distribution. Model backtesting with daily returns shows that EVT-based models provide more reliable VaR and ES forecasts than the alternative models in all six markets. Unlike the alternatives, the EVT-based models cannot be rejected as VaR confidence level is increased. This emphasizes the importance of extreme events in SEE markets and indicates that the ability of a model to capture volatility clustering accurately is not sufficient for a correct assessment of risk in these markets.     

FORECASTING GLOBAL EQUITY INDICES USING LARGE BAYESIAN VARS

This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a monthly dataset on global stock indices, the BVAR model controls for co‐movement commonly observed in global stock markets. Moreover, the time‐varying specification of the covariance structure accounts for sudden shifts in the level of volatility. In an out‐of‐sample forecasting application we show that the BVAR model with stochastic volatility significantly outperforms the random walk both in terms of point as well as density predictions. The BVAR model without stochastic volatility, on the other hand, shows some merits relative to the random walk for forecast horizons greater than six months ahead. In a portfolio allocation exercise we moreover provide evidence that it is possible to use the forecasts obtained from our model with common stochastic volatility to set up simple investment strategies. Our results indicate that these simple investment schemes outperform a naive buy‐and‐hold strategy.     

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.