投资者情绪、股票市场流动性和经济增长

采用2000—2009年的GDP、股票市场流通市值以及交易值的季度数据,从实证角度对此时段的投资者情绪、股票市场流动性和资本化率进行计量分析,通过ADF单位根检验、Johansen协整检验及Granger因果检验研究投资者情绪、股票市场流动性和股票市场发展之间的关系,进而揭示了投资者情绪影响经济增长的途径。结果显示,投资者情绪和股票市场流动性是经济增长的格兰杰原因。     

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.     

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 and the financial crisis: a sentiment-based portfolio theory perspective

The article proposes a portfolio model subjected to a constraint that captures the investor’s goal, with maximum estimation of expected return that is affected by investor sentiment. And we give a solution of the portfolio model by exploring the geometric features. Furthermore, we discuss the relationship between investor sentiment and the financial crisis by analysing the optimal allocation. The results show that: when investor sentiment is low enough, the investor should reject the investment, this condition leads the depression financial market to prevail, then the financial crisis erupts; when investor sentiment is modest, the financial crisis is difficult to erupt unless the decline of investor sentiment is quick and deep; but there is a special status that the financial crisis is caused by other factors rather than by investor sentiment; and only improving investor sentiment cannot move away from the financial crisis.     

Does Mutual Fund Flow Reflect Investor Sentiment?

This paper examines the relationship between net aggregate equity fund flow and investor sentiment using weekly flow data. Using sentiment indicators from the American Association of Individual Investors and Investors Intelligence, I find that net aggregate equity fund flow in the current week is higher when individual investors became more bullish in the previous and current weeks. Moreover, higher net aggregate equity fund flow in the current week induces newsletter writers to become more bullish in the subsequent week. The relationship between net aggregate equity fund flow and investor sentiment remains strong even after accounting for the effects of risk premium and inflation. Overall, the evidence suggests that the behavior of equity fund investors is influenced not only by economic fundamentals, but also by investor sentiment.     

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.     

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.     

私募股权投资、投资者情绪与科技型企业R&D投入

根据2009-2017年深圳证券交易所科技型上市企业相关面板数据,研究私募股权投资、投资者情绪与企业R&D投入之间的关系。研究发现：存在私募股权投资的科技型企业R&D投资强度更大;投资者情绪在私募股权投资与企业R&D投入之间起中介效应,即存在私募股权投资的科技型企业的投资者情绪更为高涨,高涨的投资者情绪会促使企业加大R&D投入。企业异质性检验发现,不同于国有企业和外资企业,民营企业的投资者情绪在私募股权投资与研发投入中的中介效应更加显著,并且处于不同生命周期的企业,无论是年轻企业还是成熟企业,投资者情绪的中介效应均不显著。将投资者情绪纳入分析框架,深入研究私募股权投资对企业研发活动的影响,对私募股权投资机构、企业、政府等进行科学决策具有重要理论价值和实践意义。     

投资者情绪、股票错误估值与上市公司投资简

以剩余收益估值模型衡量股票错误估值程度,基于公司层面测度投资者情绪,利用非平衡面板数据模型检验投资投资者情绪如何通过证券市场传导到上市公司从而影响其实际投资行为。实证研究发现,投资者情绪主要通过直接的迎合渠道而非间接的股权融资渠道影响上市公司投资行为,且公司投资在迎合渠道上表现出对投资者情绪的“反迎合机制”,此外不同特征的公司其投资对投资者情绪的敏感度存在横截面差异。     

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.     

投资者情绪对科技型企业R & D投入不足的校正效应

从自由现金流和股权融资约束出发,基于中国科技型上市企业2009-2016年面板数据分析投资者情绪对R&D投入不足的校正效应。研究结果表明：投资者情绪对科技型企业R&D投入不足具有校正效应;股权融资约束越小,投资者情绪对科技型企业R&D投入不足的校正效应越明显;自由现金流对校正效应的影响显著。通过企业异质性检验发现,国有、成熟、大型科技型企业的自由现金流会削弱投资者情绪对R&D投入不足的校正效应;非国有、年轻、中小科技型企业受到的股权融资约束越小,投资者情绪对R&D投入不足的校正效应越显著。     

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.     

Price Clustering and Investor Sentiment

Among the anomalous findings in the finance literature, perhaps the most persistent is the finding that security prices tend to cluster on round pricing increments. The author examines how investor sentiment influences the degree of price clustering. Both univariate and multivariate tests show a contemporaneous correlation between price clustering and investor sentiment. Recognizing the need to make stronger causal inferences, the author conducts 2 additional sets of tests. First, the author uses the technology bubble period as natural experiment and examine the price clustering of technology vis-à-vis nontechnology stocks. Results show that price clustering is markedly higher in tech stocks than in nontech stocks during this period of rising, sector-specific, investor sentiment. Second, the author estimates a vector autoregression process and examines the impulse responses of price clustering to exogenous shocks in investor sentiment. The results from these tests indicate that causation flows from sentiment to clustering instead of the other way around.     

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.     

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.     

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 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.     

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.     

Investor sentiment,information and asset pricing model

We present an asset pricing model with investor sentiment and information, which shows that the investor sentiment has a systematic and significant impact on the asset price. The equilibrium price's rational term drives the asset price to the rational, and the sentiment term leads to the asset price deviating from it. In our model, the proportion of sentiment investors and the information quality could amplify the sentiment shock on the asset price. Finally, the information is fully incorporated into prices when sentiment investors learn from prices. The model could offer a partial explanation of some financial anomalies: price bubbles, high volatility, asset prices' momentum effect and reversal effect.     

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.