Stock market reactions to monetary policy surprises under uncertainty

This article investigates how uncertainty impacts the effect of monetary policy surprises on stock returns. Using high-frequency US data, we demonstrate that stock markets respond more aggressively to monetary policy surprises during periods of high uncertainty. We also show that uncertainty asymmetrically influences the transmission of positive and negative monetary policy surprises to stock market prices. The amplifying effect of uncertainty is found to be stronger for expansionary shocks than for contractionary shocks. Our robustness analysis confirms that financial uncertainty has a significant role in shaping the influence of monetary policy on the stock market.     

中国家庭股市资产价值变动对居民消费的影响——来自CHFS的微观证据

近年来针对股市资产价值变动与消费关系的研究多采用宏观数据，难以揭示股市资产价值变动对居民消费影响的细节。为克服以上缺陷，文章利用中国家庭金融调查（CHFS）数据分析股市资产价值变动对中国居民消费的影响。实证结果表明：（1）股市资产价值变动对居民消费的影响总体较弱，其中市值变动的财富效应主要体现在改善性消费上，而对饮食等日常必须消费和奢侈品消费影响较小，这可能与我国居民的收入结构和投资方式有关；（2）随着年龄增长，居民的收入分布和资产配置倾向发生改变，股市资产价值变动对不同年龄居民消费的影响呈现先减小后增大的U型分布。研究结论揭示了股市资产价值变动对居民消费影响的分布特征，为今后股市改革方向提供了建议。     

Stock price crash risk and CEO power: Firm-level analysis

This study examines the impact of stock price crash risk on future CEO power. Using a large panel sample with 17,816 firm-year observations, we posit and find a significant negative impact of stock price crash risk on CEO power, suggesting that CEO power becomes smaller after stock price crashes. We also find that our results are stronger for firms with female CEOs and are largely driven by firms with shorter-tenure CEOs. In addition, we find that the significant negative impact of stock price crash risk on CEO power is diminished for firms with strong corporate governance. Our study responds to the call in Habib, Hasan, and Jiang (2018) by providing more empirical evidence on the consequences of stock price crash risk.     

Unconventional monetary policy and stock repurchases: Firm-level evidence from a comparison between the United States and Japan

This paper is the first comparative study examining the determinants of stock repurchases during the period of unconventional monetary policy. By constructing a vast firm-level dataset of the U.S. and Japan and conducting multivariate Tobit and probit analyses, this paper presents evidence that during the period of unconventional monetary policy, in both the U.S. and Japan, firms with more free cash flow and lower borrowing costs are more likely to repurchase stock, firms with higher financial leverage are more likely to abstain from stock repurchases, and firms coordinate dividends and stock repurchases to please shareholders. I also find striking contrasts between the results of U.S. and Japanese firms, and show the importance of financial structure in explaining the contrasting results. From a micro perspective, this paper provides new insight and evidence to support the view that financial structure should be thought of as an important factor determining the effects of unconventional monetary policy.     

Stock market dependence in crisis periods: Evidence from oil price shocks and the Qatar blockade

This paper examines the correlation and the dependence patterns of the Qatar stock market with other markets using copula statistical theory and exploiting new datasets covering the period August 1998 to June 2018. To examine the crisis –specific change in the average degree of dependence we decomposed the data into the time periods before and after oil price shocks and the 2017 political crisis among the Gulf Cooperation Council members (i.e. the Qatari blockade). Our findings from the static copula modelling show that the correlations between the Qatari and the other stock markets significantly change after the oil price and the blockade crisis as well. The degree of change in the correlation is time varying and differs from county-group to another. Moreover, our findings reveals that the 2008 global financial crisis has a stronger impact than the price shocks and political crisis. The findings of the paper are of interest and allow for formulating a reliable and dynamic portfolio design framework for investors and risk managers.     

Individual stock sentiment beta and stock returns

Using the data in Chinese stock market, we measure the individual stock sentiment beta, which is defined as the sensitivity of individual stock returns to the individual stock sentiment changes. We demonstrate that stocks in the highest individual stock sentiment beta portfolio have significantly higher excess returns, CAPM alpha, Fama-French three-factor alpha and Fama-French five-factor alpha. Besides, we find that the high individual stock sentiment beta stocks are smaller, younger, more volatile stocks with higher price and higher market beta. After controlling for firm characteristic, the returns of High-Low individual stock sentiment beta portfolios are still significantly positive. Moreover, we show the effect of the individual stock sentiment beta on stock returns is positive and significant in different stock markets, in different sample periods, and in bull and bear market. Besides, the results of the Bayes-Stein individual stock sentiment beta are still stable.     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

Trump’s Effect on the Chinese Stock Market

U.S. President Donald J. Trump tweets frequently to communicate his thoughts to the public. We quantitatively evaluate the impact of Trump's China-related tweets on the Chinese stock market. We find that following Trump’s inauguration, his tweets with a positive sentiment significantly increase abnormal returns for the manufacturing industry in the Chinese stock market. Furthermore, an increase in the absolute value of his positive sentiment increases both the trading volume and volatility of the market. The positive effect is more pronounced for those subindustries with high exposure to international trade and stronger business relations with the United States than for other subindustries. The results are robust for various sensitivity tests.