The asymmetric effects of investor sentiment and monetary policy on stock prices

We examine the impact of investor sentiment and monetary policy on the stock prices under different market states based on the Markov-switching vector autoregression (MS-VAR) model. The results show that the sentiment shocks, more than monetary policy shocks, lead to not only much larger fluctuations of stock prices but also much longer duration in the stock market downturn than in the stock market expansion, which shows obvious asymmetric effect. Moreover, the responses of stock prices to the sentiment shocks present an immediate effect, while the responses of stock prices to the monetary policy shocks show one-period lag 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.     

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.     

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.     

Oil prices and UK industry-level stock returns

In this article, we study whether the behaviour of oil prices can be used as a reliable predictor for the disaggregated industry-level stock market indices. We find strong evidence for the relevance of changes in oil price as a predictor for the returns of UK industry portfolios, while this relevance is heterogeneous across industries. In an out-of-sample framework, we find that both the contemporaneous and lagged oil price changes do predict UK industry stock market returns. The predictive power is more transient for the latter case, and mostly appearing after allowing for time variation in the relative performance. In addition, we find some evidence of asymmetry in the oil–stock price relationships.     

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.     

The effect of oil prices on stock prices: fresh evidence from asymmetric causality tests

This article investigates the causal impact of oil prices on stock prices in each G7 market as well as in the world market. An asymmetric causality test developed by Hatemi-J is used for this purpose. Since the underlying data appears to be non-normal with time-varying volatility, we use bootstrap simulations with leverage adjustments in order to produce more reliable critical values than the asymptotic ones. Based on symmetric causality tests, we find no causal effect of oil prices on the stock prices of the world market or any of the G7 countries. However, when we apply an asymmetric causality test, we find that increasing oil prices cause stock prices to rise in the world, the U.S. and Japan while decreasing oil prices cause stock prices to fall in Germany. This may imply that the world, the U.S. and Japanese stock markets consider increases in oil prices as an indicator of good news as this may mean that there is an increase in oil demand due to an expected growth in the economy while the German stock market treats decreasing oil prices as a signal of an expected contraction in the economy.     

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 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.     

Return threshold model analysis of two stock markets： Evidence study of Italy and Germany＇s stock returns

This paper discusses the model construction and the association between the Italy and the Germany＇s stock markets. The period of study data is from January 3, 2000 to June 30, 2008. This paper also utilizes Student＇s t distribution to analyze the proposed model. The empirical results show that the two stock markets are mutually affected each other, and the dynamic conditional correlation （DCC） and the bivariate asymmetric-GARCH （1, 2） model is appropriate in evaluating the relation between them. The empirical result also indicates that Italy and Germany＇s stock markets show a positive relationship. The average value of correlation coefficient equals to 0.8424, which implies that the two stock markets return volatility have a synchronized influence on each other. In addition, the empirical result also shows that there is an asymmetrical effect between Italy and the Germany＇s stock markets, and demonstrates that the good news and bad news of the stock returns＇ volatility will produce the different variation risks for Italy and the Germany＇s stock price markets.     

Cross-country evidence on the relation between stock prices and the current account

This article explores the relation between stock prices and the current account for 17 Organization for Economic Co-operation and Development (OECD) countries in 1980–2007. A panel Vector Autoregressive (VAR) model is used to compare the effects of stock price shocks to those originating from monetary policy and exchange rates. While monetary policy shocks have little effects, shocks to stock prices and exchange rates have sizeable effects. A 10% contraction in stock prices improves the current account by 0.3% after 2 years. Hence a channel – in addition to the traditional exchange rate channel – is found through which external balance for an OECD country with a current account imbalance can be restored.     

Stochastic investor sentiment,crowdedness and deviation of asset prices from fundamentals

This study constructs a theoretical model to address how stochastic investor sentiment affects investor's crowdedness, and how stochastic investor sentiment and crowdedness affect asset prices. An asset pricing model incorporating stochastic investor sentiment and crowdedness is developed, which can provide efficient explanations for the deviations of asset prices from fundamentals and the maverick risk of investors. This model indicates that the optimistic (pessimistic) investor sentiment and the long (short) crowdedness caused by optimistic (pessimistic) sentimental investors can push asset price above (below) fundamental value. Also, the sentimental investors who are wrong and alone would take the maverick risk. Our results are consistent with the idea that investor sentiment and investor behavior matter for the asset prices and the deviations of asset prices from fundamentals.     

The impacts of investor sentiment and price uncertainty on the effects of open-market share repurchase announcements

This study probes into relationship between investor sentiment and cumulative abnormal returns (CAR) of share repurchase announcements, and it treats market return as threshold variable. By threshold regression model, it tries to find the effect of market situations on relation between investor sentiment and CAR. According to empirical result, in share market of Taiwan, investor sentiment can explain CAR. When share market is extremely pessimistic (market return lower than ?16.0053%), relation between investor sentiment and CAR will change to some degree. In addition, relation between price risk of announcement company and CAR will disappear with the extremely pessimistic situation of market.     

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.     

Predicting stock realized variance based on an asymmetric robust regression approach

This paper introduces an asymmetric robust weighted least squares (ARLS) approach to improve the forecasting performance of the heterogeneous autoregressive model for realized volatility. The ARLS approach down-weights extreme observations to limit the bad influence of outliers on the estimated parameters. Compared with existing robust regression methods, our model further takes into account the asymmetry of outliers using a class of kernel functions. Out-of-sample results show the ARLS approach can generate more accurate forecasts of the S&P 500 index realized volatility in the statistical and economic senses. The model that considers the asymmetry of outliers gains superior performance among various robust regression competitors. The forecasting improvements also hold in other international stock markets. More importantly, the source of the predictive ability of the ARLS model comes from the less biased and more efficient parameter estimation.     

The impact of 11/13 Paris terrorist attacks on stock prices: evidence from the international defence industry

This study explores the effect of the Paris terrorist attacks on the stock returns and the volatility for the most important companies in the global defence industry. To this end, it employs the General Autoregressive Conditional Heteroscedasticity methodology. The findings clearly indicate that this terrorist event has a positive impact on both the returns and the volatility of these stocks.     

The effect of quantitative easing on stock prices: a structural time series approach

A structural time series model is estimated and tested to examine the effect of quantitative easing (QE) on US stock prices. The model is estimated by maximum likelihood in a Time-varying parametric (TVP) framework, using the S&P 500 index as the dependent variable and the Fed’s balance as an explanatory variable in addition to the unobserved components accounting for the behaviour of variables that do not appear explicitly in the equation. The results show that QE had a sizeable, but not exclusive, effect on stock prices and that stock prices were also affected by other missing variables and cyclical movements. Several explanations are presented for the rise of the US stock market in the post-QE period, particularly since the election of Donald Trump.     

The effects of haze pollution on stock performances: evidence from China

Haze pollution has become the most important environmental issue in China in recent years. Using the data of PM_2.5 concentration and stocks of listed companies located in Beijing between 2010 and 2014, this article investigates the effects of haze pollution on stock performances. Empirical results indicate that haze pollution has significant negative effects on stock returns and significant positive effects on stock volatilities, through the channel of investors’ mood. Furthermore, the effects of haze pollution on stock returns emerge gradually and the effects of haze pollution on stock volatilities weaken gradually over time during a trading day.     

Asymmetric effect of advertising on the Chinese stock market

In this paper, we investigate whether investor attention to advertising has an asymmetric effect on Chinese stock returns by using a multivariate Markov switching model with time-varying regime transition probabilities. Using the Chinese stock market as a setting, we obtain lagged conditional volatility from generalized autoregressive conditional heteroskedasticity (GARCH) for modelling the time-varying transition probabilities of the regime-switching process to capture changes in the market regime. Our evidence documents that the high advertising portfolio does earn higher abnormal return than the low advertising portfolio in low-volatility periods. In high-volatility periods, however, the abnormal return is insignificant when the firm increases advertising spending. Our results support the behavioural model argument that in high-volatility period, advertising information diffuses slowly due to cognitive dissonance. Thus, the effect of advertising on stock returns is asymmetric, and it shows statistical significance in low-volatility periods.     

人力资本对产业结构优化升级的影响——基于空间面板数据模型的研究

产业结构优化升级主要包含产业结构合理化与产业结构高级化两个方面. 本文基于2002—2013年间的中国30个省域的数据, 采用空间面板数据模型, 经验分析了人力资本对于产业结构合理化与产业结构高级化的影响作用. 分析得出: 当前中国的人力资本积累水平对于产业结构合理化与产业结构高级化均呈现出正向促进作用; 人力资本分布结构并不利于产业结构合理化与产业结构高级化; 与此同时, 从人力资本对产业结构合理化与产业结构高级化的空间效应来看, 人力资本积累水平具有正的空间溢出效应.