Impact of uncertainty on high frequency response of the U.S. stock markets to the Fed's policy surprises

This paper examines the impact of uncertainty on estimated response of stock returns to U.S. monetary policy surprise. This is motivated by the Lucas island model which suggests an inverse relationship between the effectiveness of a policy and the level of uncertainty in the economy. Using high frequency daily data from the Federal funds futures market, we first estimate the response of S&P 500 stock returns to monetary policy surprises within the time varying parameter (TVP) model. We then analyze the relationship of these time varying estimates with the benchmark VIX index and alternative measures of uncertainty. Evidence suggests a significant negative relationship between the level of uncertainty and the time varying response of S&P 500 stock returns to unanticipated changes in the interest rate. Thus, at higher levels of uncertainty the impact of monetary policy shocks on stock markets is lower. The results are robust to different measures of uncertainty.     

Evolving United States stock market volatility: The role of conventional and unconventional monetary policies

Despite the econometric advances of the last 30 years, the effects of monetary policy stance during the boom and busts of the stock market are not clearly defined. In this paper, we use a structural heterogeneous vector autoregressive (SHVAR) model with identified structural breaks to analyse the impact of both conventional and unconventional monetary policies on U.S. stock market volatility. We find that contractionary monetary policy enhances stock market volatility, but the importance of monetary policy shocks in explaining volatility evolves across different regimes and is relative to supply shocks (and shocks to volatility itself). In comparison to business cycle fluctuations, monetary policy shocks explain a greater fraction of the variance of stock market volatility at shorter horizons, as in medium to longer horizons. Our basic findings of a positive impact of monetary policy on equity market volatility (being relatively stronger during calmer stock market periods) are also corroborated by analyses conducted at the daily frequency based on an augmented heterogeneous autoregressive model of realised volatility (HAR-RV) and a multivariate k-th order nonparametric causality-in-quantiles framework. Our results have important implications both for investors and policymakers.     

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.     

The impacts of investor sentiment on returns and conditional volatility of international stock markets

One of the main arguments of behavioral finance is that some properties of asset prices are most probably regarded as deviations from fundamental value and they are generated by the participation of traders who are not fully rational, thus called noise traders. Noise trader theory postulates that sentiment traders have greater impact during high-sentiment periods than during low-sentiment periods, and sentiment traders miscalculate the variance of returns undermining the mean-variance relation. The main objective of this research is to construct a model to evaluate the returns and conditional volatility of various stock market indexes considering the changes in the investor sentiment by measuring the effects of noise trader demand shocks on returns and volatility. EGARCH model is used to determine whether earning shocks have more influence on the conditional volatility in high sentiment periods weakening the mean–variance relation. This paper takes an international approach using weekly market index returns of U.S., Japan, Hong Kong, U.K., France, Germany, and Turkey. Weekly trading volumes of these indexes are regressed against a group of macroeconomic variables and the residuals are used as proxies for investor sentiment and significant evidence is found that there is asymmetric volatility in these market indexes and earning shocks have more influence on conditional volatility when the sentiment is high.     

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.     

Predicting stock market crises using daily stock market valuation and investor sentiment indicators

The purpose of this paper is to develop a daily early warning system for stock market crises using daily stock market valuation and investor sentiment indicators. To achieve this goal, we use principal components analysis to propose a comprehensive index of daily market indicators that reflects stock market valuation and investor sentiment. Based on the comprehensive index, we employ a logit model with Ensemble Empirical Mode Decomposition to develop a daily early warning system for stock market crises. Finally, we apply the proposed system to the early warning for stock market crises in China. The in-sample forecasting results show that investor sentiment and the forecast horizon by Ensemble Empirical Mode Decomposition improve the forecasting performance of conventional early warning systems. The out-of-sample forecasting results indicate that the proposed warning system still has a good performance.     

Investor co-attention and stock return co-movement: Evidence from China’s A-share stock market

This study attempts to link investor co-attention to stock return co-movement in China's A-share stock market. On the one hand, stock price will co-move for stocks within the same industry and within the same market, which is labelled “return co-movement”. On the other hand, investor attention will also co-move as investors systematically search for relevant information for stocks of similar characteristics or as the stocks experience common information shocks, which is termed “investor co-attention”. The empirical evidence suggests that stock return co-movement can be explained by investor co-attention to a great extent, even after controlling for stock fundamentals and firm characteristics, and this effect is more salient for stocks with lower institutional ownership. Moreover, we employ large national lottery jackpots as exogenous shocks to investor attention. The empirical findings show that the co-movement of both investor attention and stock return increase on large lottery jackpot days, while investor co-attention contributes less to return co-movement on large lottery jackpot days. In summary, we offer an alternative explanation for return co-movement by observing the causal relationship between investor co-attention and stock return co-movement.     

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.     

Monetary policy and stock prices in small open economies: Empirical evidence for the new EU member states

This study presents evidence on the effect of domestic and Euro Area monetary policy on stock prices in four new EU member states of Central Europe and the main determinants of stock price volatility, estimating structural vector autoregressive models identified with short-run restrictions. We find that stock prices in the considered new EU member states are more sensitive to changes in the Euro Area interest rate than to the domestic one. Moreover, the bulk of stock price volatility in these countries is due to shocks related to exchange rate and Euro Area monetary policy. Overall, we find that local stock markets are more sensitive to external shocks than to domestic ones.     

The 1986–88 stock market: Investor sentiment or fundamentals?

This paper tests the widely held proposition that investor sentiment contributed to the stock market crash of 1987. Using weekly data during the 1986–8 period and conventional measures of stock fundamentals, changes in fundamentals are found to have a statistically significant influence on the movement of stock prices. In addition, a much-discussed measure of investor sentiment is used to test the proposition that investor sentiment contributed to the stock market crash of 1987. However, insignificant results regarding the investor sentiment index suggest that either the recently proposed sentiment index is faulty or investor sentiment did not significantly influence stock prices in the period surrounding the 1987 crash.     

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.     

Assessing the reversal of investor sentiment

Assessing the reversal of sentiment in stock markets is needed because, according to the social mood cycle, the change of social mood over time is an antecedent of price movements. The purpose of this study is to empirically assess reversal of investor sentiment, to show the phases of social mood cycle from increasing mood to decreasing mood, and to explain the dynamic change in market inefficiency from increasing to decreasing. Growth modeling, developed particularly for dealing with the change over time, is used in this study for assessing the reversal of investor sentiment. The autocovariance structure of errors and the variances/covariances of the random coefficients are all taken into account in the model. The results have indicated that the change in investor sentiment over time is inverted U-shaped for the entire market. Moreover, arbitrage constraint and stock characteristics exert a joint moderating effect on sentiment reversal. Less arbitrage constraint can strengthen sentiment reversal only when the market for individual stocks is dominated by noise traders. Based on the results obtained, we discuss asset pricing, liquidity management, and market intervention.     

Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search

We examine the ability of online ticker searches (e.g. XOM for Exxon Mobil) to forecast abnormal stock returns and trading volumes. Specifically, we argue that online ticker searches serve as a valid proxy for investor sentiment — a set of beliefs about cash flows and investment risks that are not necessarily justified by the facts at hand — which is generally associated with less sophisticated, retail investors. Based on prior research on investor sentiment, we expect online search intensity to forecast stock returns and trading volume, and also expect that highly volatile stocks, which are more difficult to arbitrage, will be more sensitive to search intensity than less volatile stocks. In a sample of S&P 500 firms over the period 2005-2008, we find that, over a weekly horizon, online search intensity reliably predicts abnormal stock returns and trading volumes, and that the sensitivity of returns to search intensity is positively related to the difficulty of a stock being arbitraged. More broadly, our study highlights the potential of employing online search data for other forecasting applications.     

Role of credit and monetary policy in determining asset prices: Evidence from emerging market economies

We examine the relative dominance of credit and monetary policy shocks in influencing asset prices in emerging markets. Estimates from panel VAR models for 22 EMEs provide evidence of a significant impact of bank credit on house prices in contrast to trivial impact on stock prices, possibly due to prudential regulations on banks’ exposure to stock markets. Contractionary monetary policy triggers sizeable and persistent decline in stock than housing prices as higher interest rates may render the funding of leverage costlier. Global shocks play an important role in explaining fluctuations in domestic stock prices rather than house prices since the latter class of asset is largely non-tradable across countries.     

行业资产的Black-Litterman模型配置研究——基于社交网络情绪文本挖掘算法

研究目标:构建反映行业股价走势的基于社交网络文本挖掘算法的行业投资者情绪指标，并改善嵌入行业投资者情绪指标的Black-Litterman模型对资产的配置结果。研究方法:基于社交网络文本挖掘算法度量投资者情绪，运用主成分分析法构建行业投资者情绪指标，并嵌入Black-Litterman模型中构建投资者观点矩阵，确定行业资产配置比。研究发现:基于行业投资者情绪的BL模型有效提高了资产配置的日均收益率和夏普比率。实证结果在样本外验证(除受新冠疫情影响阶段)、暴涨暴跌阶段以及经过允许卖空和交易成本调整后仍稳健，进而证实了投资者情绪对资产组合有显著影响。研究创新:基于社交网络文本挖掘算法构建投资者情绪指数，解决了仅依赖于预期收益或历史数据的预测模型无法直观揭示投资者心理认知和行为的局限性问题，从一个崭新的视角科学地解决Black-Litterman模型中投资者观点的生成问题。研究价值:扩展了Black-Litterman模型理论体系研究，并推动了行为金融理论在资产配置中的应用。     

The effects of foreign exchange and monetary policies in Russia

This paper examines the effects of Russian foreign exchange and monetary policies under conditions of abundant natural resources during the period 1999–2011 using structural VAR models. The results suggest that monetary policy shocks, which are identified as money supply disturbances, have a persistent effect on real output, and more than half of the volatility in real output can be explained by changes in the money supply. Furthermore, the analysis reveals that stock prices are a more significant transmission channel of monetary policy than bank loans.     

Information asymmetry,sentiment interactions,and asset price

We develop an asset pricing model with sentiment interactions between institutional and individual investors under the condition of information asymmetry. Our model considers private information and investor sentiment, two imperfections in securities markets, and integrates them into a theoretical model to investigate the role of the interaction between information asymmetry and investor sentiment in asset pricing. We show that the joint effect of private information and investor sentiment deviate the price of risky assets and efficiently explains anomalies in the stock market. Investor sentiment changes the effect of information on the equilibrium price relative to a world where all investors are completely rational. Private information changes the effect of investor sentiment on the equilibrium price in comparison with a scenario with symmetric market information. In addition, the individual investors’ learning and the disclosure of information both allow private information to be better integrated into the price and simultaneously changes the effect of investor sentiment on the equilibrium price.     

Does investor sentiment affect fund crashes? Evidence from Chinese open-end funds

This paper aims to detect the impact of investor sentiment on the open-end fund crashes, drawing on the open-end stock funds and partial stock funds of China for the 2009–2019 period. The results show that the rise of investor sentiment will significantly increase the risk of the open-end fund crashes, which remains valid after robustness tests. Further researches indicate that the market timing and stock selection abilities of fund managers weaken the positive impact of investor sentiment on the open-end fund crashes, and the market illiquidity promotes the positive impact of investor sentiment on the open-end fund crashes.     

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.

     

Stock prices and the location of trade: Evidence from China-backed ADRs

This study examines whether the trading location affects equity returns of China-backed American Depository Receipts (ADRs) traded in the US. If International Financial Markets are integrated, stock prices should be affected only by their fundamentals; otherwise, stock prices may also be affected by their trading locations/investor sentiment. We find that China ADRs’ returns are affected more by the US market fluctuations than by Chinese market returns. We interpret the results as suggesting that International Financial Markets are at least partially segmented and country-specific investor sentiment affects stock prices.