Economic policy uncertainty,investor sentiment and financial stability—an empirical study based on the time varying parameter-vector autoregression model

This paper applies the time varying parameter-vector autoregression model to explore the dynamic relationship between economic policy uncertainty, investor sentiment and financial stability in China in different periods and at different time points. The empirical results show that economic policy uncertainty has an obvious negative impact on investor sentiment before 2012 and financial stability in the short term, and the influence of economic policy uncertainty on investor sentiment is greater than that of economic policy uncertainty on financial stability. These influences were more significant during the period of the global financial crisis in 2008. Moreover, investor sentiment had a positive and gradually increasing effect on financial stability, while after 2010, the positive impact gradually weakened. Furthermore, economic policy uncertainty is negatively affected by financial stability, and the effect of financial stability on investor sentiment is positive. In terms of mediating effects, economic policy uncertainty has an indirect impact on financial stability through investor sentiment and vice versa. This paper provides a new solution to economic problems explored in behavioral finance research. Additionally, Chinese government agencies can achieve the goal of preventing financial crises and maintaining financial stability by monitoring investor sentiment and implementing targeted economic policies.

     

Do natural disasters and geopolitical risks matter for cross-border country exchange-traded fund returns?

This paper examines whether U.S. and home country geopolitical risks (GPRs) and disasters matter for the returns from cross-border trading of country exchange-traded funds (ETFs) by employing a quantile regression approach. Using monthly returns of 125 country-specific ETFs traded in the U.S. from 38 countries over the period 2004–2018, we find that the highest averages of total deaths, total damage values, total affected, and GPRs are all in developing countries. United States disasters have comparatively more significant impacts than home country disasters on ETF returns as does the salient influences of U.S. investor sentiments, supporting the market sentiment hypothesis. Moreover, U.S. and home country GPRs and disasters also have predictive power on returns. The contemptuous effects and predictive powers of GPRs and disasters are asymmetric across quantiles. The influences of home country GPRs are more salient than the GRPS of U.S., implying that ETFs can be a safe haven during U.S. geopolitical risks. Additionally, our results show that the impacts of disasters on returns can be negative and/or positive, implying the possibility of disasters exuding an impetus and/or risk to country ETFs.     

创业板投资者情绪及其收益率相关性研究

本文采用向量自回归模型VAR方法分别研究了我国主板市场与创业板市场投资者情绪与收益率之间影响是系。研究发现我国主板市场中投资者情绪对收益率有显著影响,而收益率对投资者情绪无显著影响;而创业板市场表现不同,投资者情绪对收益率无显著相关影响。研究表明:出现上述现象的原因是由于两市场的投资者结构存在差异,创业板市场投资者更成熟、创业板政策及监管更为全面等原因。     

Time-frequency transmission mechanism of EPU,investor sentiment and financial assets: A multiscale TVP-VAR connectedness analysis

This article examines the transmission mechanism of economic policy uncertainty (EPU), investor sentiment and Chinese financial assets from time-frequency and static-dynamic perspectives. The multiscale connectedness method based on time-varying parameter vector autoregression (TVP-VAR) is introduced to explore the time–frequency and static-dynamic spillovers. The empirical results are as follows: First, there is an interdependence between EPU and high-risk assets. Additionally, EPU and high-risk assets spillover risk to investor sentiment individually or in chains, ultimately affecting low-risk assets. Second, high-risk assets spill to low-risk assets in the short term but reverse in the long term. Third, EPU spills over to the system the most around 2008, especially in the long term. In addition, high-risk assets are the largest risk spillover and recipient at each frequency over the last decade. Overall, investors and regulators should consider real-time financial monitoring solutions in China based on economic policy uncertainty and investor sentiment factors.     

Investor sentiment and country exchange traded funds: Does economic freedom matter?

Using unbalanced panel data of 27 iShares MSCI country-specific exchange traded funds (ETFs) over the period 1996–2014, this paper applies quantile regression to examine the impacts of global, foreign, and U.S. investor sentiments on the returns of the ETFs traded in the U.S. markets. We further investigate whether a country’s economic freedom affects the relationship between investor sentiments and ETF returns. We find that ETF returns are strongly determined by investor sentiments and the ETF expense ratio. The quantile regression approach reveals that high-return ETFs are positively sensitive to changes in global sentiment (measured by market turnover, VIX, U.S. federal funds rate), foreign sentiment (measured by current account balance, inflation, market turnover, public debt), U.S. sentiment, currency exchange ratio, and expense ratio, while negatively influenced by economic freedom and Asian proxy. The effects of VIX and foreign inflation are a reversal; that is, returns from lower (higher) quantiles have a negative (positive) relation with VIX and foreign inflation. Not all components of economic freedom affect returns equally.     

Impact of network investor sentiment and news arrival on jumps

This paper studied the influence of news announcements and network investor sentiment on Chinese stock index and index futures market jumps. A machine learning text analysis algorithm was employed to measure investor forum sentiment. It was found that news arrivals were an important reason for jump occurrences, jumps were significantly associated with network investor sentiment, and while occasionally the news and network investor sentiment resulted in simultaneous market jumps, they appeared to be relatively independent. The network investor sentiment time-lag and asymmetric effects were also tested, from which it was found that network investor sentiment had a significant asymmetric effect on the jumps, but time-lag effects had little influence. News announcements and the top 25% of the extreme network sentiments were found to explain more than 50% of the jumps, with extreme sentiments tending to increase the volatility of the news-related jumps and persistently influencing returns after the news-related jumps.     

Excess co-movement of agricultural futures prices: Perspective from contagious investor sentiment

This study investigates the excess co-movement of agricultural futures prices from a new perspective of contagious investor sentiment. This study shows that contagious investor sentiment is a key determinant of excess co-movement of agricultural futures prices, by using contagious investor sentiment among different agricultural futures. Further, this study decomposes contagious investor sentiment into expected and unexpected contagious investor sentiment. Results show that both of them can positively affect excess co-movement of agricultural futures prices. More interestingly, expected contagious investor sentiment outperforms unexpected contagious investor sentiment in soybean 1 future, soymeal future, and strong wheat future. In general, the results of this study can provide strong support for the significant roles of contagious investor sentiment in asset pricing applications.     

Investor sentiment and Bitcoin relationship: A quantile-based analysis

This paper applies a quantile-based analysis to investigate the causal relationships between Bitcoin and investor sentiment by considering the possible effects of the ongoing COVID-19 pandemic. Such an analysis allows investigating the predictive power of investor sentiment (Bitcoin) on Bitcoin (investor sentiment) at different levels of the distributions. Results emphasize that only Bitcoin returns/volatility have significant predictive power on the investor sentiment whether investors are fear or greed before and over the COVID-19 period. Moreover, the COVID-19 crisis has no effect on the causal relationship between the two variables. Further analysis shows an asymmetric causality observed only during the pandemic period. Furthermore, the quantile autoregressive regression model shows a significant positive relationship between investor sentiment and Bitcoin returns.     

The momentum and reversal effects of investor sentiment on stock prices

In this paper, we illustrate the real function relationship between the stock returns and change of investor sentiment based on the nonparametric regression model. The empirical results show that when the change of investor sentiment is moderate, the stock return is positively correlated with the change of investor sentiment, presenting an obvious momentum effect. However, the stock return is negatively correlated with the change of investor sentiment if the change of investor sentiment is dramatic, presenting significant reversal effects. Moreover, the degree of reversal effect caused by extremely optimistic sentiment is greater than that driven by extremely pessimistic sentiment, which shows a significant asymmetry. Our findings offer a partial explanation for financial anomalies such as the mean reversion of stock returns, the characteristic of slow rise and steep fall in China's stock market and so on.     

New insights into the role of global factors in BRICS stock markets: A quantile cointegration approach

Because of the acceleration in marketization and globalization, stock markets in the BRICS (Brazil, Russia, India, China, and South Africa) countries are affected by various global factors, for example, oil prices, gold prices, global stock market volatility, global economic policy uncertainty, financial stress, and investor sentiment. This paper offers new insights into the short- and long-run linkages between global factors and BRICS stock markets by applying the quantile autoregressive distributed lags (QARDL) approach. This novel methodology enables us to test short- and long-run linkages accounting for distributional asymmetry. That is, the nonlinear dynamic relationship between the global factors and BRICS stock prices depends on market conditions. Our empirical results show that the effects of gold prices and global stock market volatility on BRICS stock prices are more significant in the long run than in the short run. A decrease in global stock market volatility is associated with higher stock prices, while gold prices demonstrate upward co-movement in dynamic correlations with stock markets. Irrational factors, such as economic policy uncertainty, financial stress, and investor sentiment, play a critical role in the short term, and negative interdependence is dominant. Finally, the rolling-window estimation technique is used to examine time-varying patterns between major global factors and BRICS stock markets.     

Geopolitical risk and stock market volatility in emerging markets: A GARCH – MIDAS approach

In this study, we examine the connection between geopolitical risk (GPR) and stock market volatility in emerging economies. Our motivation for this study is premised on the need to assess both the predictability and the associated economic gains in relation to the subject in order to offer more useful insights to investors and practitioners. To the best of our knowledge, this is the first study that jointly considers these objectives. Consequently, we employ the GARCH-MIDAS framework which accommodates mixed data frequencies thereby circumventing information loss or any associated bias. We find that emerging stock market volatility responds more positively to geopolitical risks although the act-related GPR index offers better out-of-sample forecasts than the threat-related GPR. We also find that accounting for global economic factors in the predictability analysis is crucial for robust outcomes. Finally, we provide some utility gains of including GPR in the predictive model of stock market volatility while also highlighting some useful implications of our findings for investment and policy decisions.     

Re-examining risk premiums in the Fama–French model: The role of investor sentiment

This paper reconstructs the Fama–French three-factor (F–F) model as a panel smooth transition regression (PSTR) framework to investigate the differentiated effects of investor sentiment proxies-the volatility index (VIX), credit default swap (CDS), and TED spread-on the three risk premiums. Sample period spans from 2003: 1Q to 2013: 4Q. Sample objects are 58 semiconductor companies listed on Taiwan Security Exchange Corporation. The empirical results report that stock returns display a nonlinear path, and the three risk premiums are time-varying, depending on different proxies of investor sentiment in different regimes. Market premiums fall as investors in stock markets show extreme optimism or extreme pessimism. Except in rare situations, the size premium is significant and decreases with the increase in the VIX. Returns in holding growth stocks dominate holding value stocks when the investors show extreme pessimism or optimism. However, in normal sentiment of investment, value stocks earn more returns than growth stocks.     

截面特征差异、投资者情绪与企业投资

投资者情绪影响企业投资决策,在此基础上进一步检验企业截面特征差异对投资者情绪与企业投资关系的影响作用。以2004～2009年沪深两市上市公司为研究样本,采用主成分分析法构建投资者情绪综合指数,使用动态面板回归方法,研究发现换手率越高、规模越小、成长性越高、收益波动程度越大、现金持有量越多以及资产负债率越低的上市公司,其投资行为对投资者情绪越敏感。     

Risk-neutral skewness and market returns: The role of institutional investor sentiment in the futures market

This paper investigates the effect of index risk-neutral skewness on subsequent market returns and explores whether this effect will vary with various types of institutional investor sentiment in the futures market. Using index futures returns as the proxy of market returns, the empirical results show that the index risk-neutral skewness has a significantly negative effect on subsequent index futures returns. Moreover, the effect of institutional investor sentiment on subsequent index futures returns varies with various types of institutional investor sentiment. Finally, the effect of index risk-neutral skewness on subsequent index futures returns relies on various types of institutional investor sentiment.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.     

Individual stock crowded trades,individual stock investor sentiment and excess returns

Recent behavioral asset pricing models and the popular press suggest that investors may follow similar strategies resulting in crowded equity positions to push prices further away from fundamentals. This paper develops a new approach to measure individual stock crowded trades, and further investigates the joint effects of individual stock crowded trades and individual stock investor sentiment on excess returns. Specifically, our results show that the combined effect of individual stock crowded trades and individual stock investor sentiment on excess returns is positive and significant, which reveals the importance of “anomaly factors” in asset pricing. Furthermore, our results suggest that increasing individual stock buyer-initiated crowded trades will increase excess returns simultaneously; however, increasing individual stock seller-initiated crowded trades will decrease excess returns simultaneously. Collectively, our results highlight the importance of individual stock crowded trades and individual stock investor sentiment on the formation of stock prices.     

The fluctuation correlation between investor sentiment and stock index using VMD-LSTM: Evidence from China stock market

The impact of the investor sentiment on China’s capital market price volatility is concerned under the perspective of the behavioral finance. Firstly, in terms of the existing methods of establishing the investor sentiment index, the composite investor sentiment index which include six indicators (five objective indicators and a subjective indicator) are obtained. Secondly, VMD-LSTM (Variational Mode Decomposition and Long Short Term Memory) hybrid neural network model is used to decompose and restructure the investor sentiment index and the Shanghai Security Exchange Composite Index (SSEC) into the short-term, medium-term and long-term trend. Each trend is trained to obtain the forecasting results in three different time scales, and then to achieve the final predicting results by superimposing the output of each trend. Furthermore, compare with other prediction methods, the model can indeed improve the overall predicting accuracy. Finally, GARCH model and the co-integration error regression model are used to discuss the fluctuation correlation and VAR (Vector Auto-regression) models are established to analyze the causality between the stock market indices and the investor sentiment index.     

Does investor sentiment impact the returns and volatility of Islamic equities?

In this paper, we estimate generalized autoregressive conditional heteroskedasticity (GARCH) and vector autoregressive (VAR) models to examine whether investor sentiment impacts the returns and volatility of various U.S. Dow Jones Islamic equity indices. The results from GARCH estimations show that changes in investor sentiment are positively correlated with the returns of the Shari’ah-compliant market portfolio. In addition, we find similar results for the three Shari’ah-compliant firm-size portfolios (i.e., large-, medium-, and small-cap). However, this relationship is stronger for harder to arbitrage Shari’ah-compliant stocks; that is, investor sentiment has a greater influence on small-cap equities. Additionally, estimations from the vector autoregressive model confirm the aforementioned results. In terms of volatility, GARCH estimations suggest that bullish shifts in investor sentiment in the current period are accompanied by lower conditional volatility in the ensuing period. In general, our findings suggest that as noise traders create more risk the market seems to reward them with higher expected returns.     

Bounded rationality,anchoring-and-adjustment sentiment,and asset pricing

This paper presents a framework that incorporates an investor’s limited attention and anchoring and adjustment sentiment and their joint effects on asset pricing, with endogenous cost of neglecting part of the dividends and the asymmetric rationality levels of investors. We find that the combined effect of the two bounded rationality factors is often embodied in the “loss”, and the retail investors are insensitive to market sentiment and forced to pay more cognitive loss. A higher level of investor rationality and bullish market sentiment will jointly increase demand and then prices, while the effects of different bounded rationality factors are asymmetric.     

Time-varying impact of monetary policy shocks on US stock returns: The role of investor sentiment

This paper investigates how monetary policy shock affects the stock market of the United States (US) conditional on states of investor sentiment. In this regard, we use a recently developed estimator that uses high-frequency surprises as a proxy for the structural monetary policy shocks, which in turn is achieved by integrating the current short-term rate surprises, which are least affected by an information effect, into a vector autoregressive (VAR) model as an exogenous variable. When allowing for time-varying model parameters, we find that, compared to the low investor sentiment regime, the negative reaction of stock returns to contractionary monetary policy shocks is stronger in the state associated with relatively higher investor sentiment. Our results are robust to alternative sample period (which excludes the zero lower bound) and model specification and also have important implications for academicians, investors, and policymakers.