Return and volatility spillovers between currency and bond markets in India

This paper examines the return and volatility spillovers between the foreign exchange and bond markets of India using a bivariate asymmetric BEKK-GARCH (1,1) model for the period 4 April 2005 to 31 March 2017. We find the evidence of bidirectional return and volatility spillovers with asymmetric effects between these two markets. The spillovers are evidenced even during the periods when foreign portfolio investments in the Indian bond markets were relatively low suggests the existence of strong inter-linkages between both the markets.     

WHAT CAN WE LEARN FROM THE IMPLEMENTATION OF MONETARY AND MACROPRUDENTIAL POLICIES: A SYSTEMATIC LITERATURE REVIEW

The emergence of macroprudential policies, implemented by central banks as a means of promoting financial stability, has raised many questions regarding the interaction between monetary and macroprudential policies. Given the limited number of studies available, this paper sheds light on this issue by providing a critical and systematic review of the literature. To this end, we divide the theoretical and empirical studies into two broad channels of borrowers – consisting of the cost of funds and the collateral constraint – and financial intermediaries – consisting of risk‐taking and payment systems. In spite of the existing ambiguity surrounding coordination issues between monetary and macroprudential policies, it is argued that monetary policy alone is not sufficient to maintain macroeconomic and financial stability. Hence, macroprudential policies are needed to supplement monetary. In addition, we find that the role of the exchange rate is critical in the implementation of monetary and macroprudential policies in emerging markets, while volatile capital flows pose another challenge. In so far as how the arrangement of monetary and macroprudential policies varies across countries, key theoretical and policy implications have been identified.     

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.     

我国出口汇率传递的非对称性:来自中日贸易的证据

文章扩展了Yang（2007）的厂商定价模型，对汇率传递非对称性、特点和成立条件进行理论分析，发现对称传递需要满足严格的条件，而现实中更常见的是非对称汇率传递。在一定条件下，商品需求弹性越大，越可能呈现出口国货币贬值传递率高而升值传递率低的特点。利用2000年1月至2011年12月我国出口日本的966种商品价格数据，发现汇率波动向价格传递具有非对称性：（1）人民币升值时，日元价格上涨较少；而贬值时，价格下降较多。我国出口商品的需求弹性较大，在日本市场上面对的是一个强竞争结构。（2）若月度升值超过一定幅度（测算约为2.43%），传递率又会有所上升，说明尽管日元价格易跌难涨，但在升值导致成本上涨较多、明显挤压利润时，厂商不得不适当提高日元售价。采用2000年至2018年9月日本从中国进口单位价值指数进行稳健性检验，得出类似结论。现阶段稳定的人民币名义汇率对我国出口企业是更为有利的。     

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.     

全国煤炭交易中心运营模式研究

全国煤炭交易中心的设立对规范我国煤炭交易市场规则、实施能源宏观调控、提升我国煤炭国际定价话语权具有重要意义。在分析全国煤炭交易中心功能定位和业务的基础上,设计中长期合同邀约、现货挂牌、现货竞价、现货招投标4种交易模式及业务流程,提出依托国家重大战略争取政策支持、加强各方沟通完善综合物流体系、建立银企合作机制与信用体系、完善煤炭交易中心协调机制、增强信息服务与风险防控能力等对策建议。研究成果对优化煤炭供给结构、规范煤炭交易市场和保障国家煤炭能源安全提供了支撑。     

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.     

央行货币政策报告文本信息、宏观经济与股票市场

本文利用金融情感词典和文本分析技术,分析中国人民银行货币政策执行报告的文本情绪、文本相似度和文本可读性等多维文本信息,刻画央行货币政策执行报告的文本特征,探究货币政策报告的文本信息与宏观经济和股票市场的关系。实证研究发现,货币政策报告的文本情绪的改善会引起显著为正的股票市场价格反应,报告文本相似度的增加会引起股票市场波动性的显著降低,报告可读性对公布后股票市场的波动性影响不显著。货币政策报告文本情绪还与诸多宏观经济指标显著相关。进一步研究发现,引起股票市场显著反应的是报告文本情绪中反映货币政策指引的部分,而反映宏观经济历史状态的部分对股票市场的影响不显著。本文从文本大数据分析角度证明了我国央行沟通的有效性,对国内央行沟通相关研究形成了有益补充。     

The impact of COVID-19 on the stock market crash risk in China

This study investigates the impact of the COVID-19 pandemic on the stock market crash risk in China. For this purpose, we first estimated the conditional skewness of the return distribution from a GARCH with skewness (GARCH-S) model as the proxy for the equity market crash risk of the Shanghai Stock Exchange. We then constructed a fear index for COVID-19 using data from the Baidu Index. Based on the findings, conditional skewness reacts negatively to daily growth in total confirmed cases, indicating that the pandemic increases stock market crash risk. Moreover, the fear sentiment exacerbates such risk, especially with regard to the impact of COVID-19. In other words, when the fear sentiment is high, the stock market crash risk is more strongly affected by the pandemic. Our evidence is robust for the number of daily deaths and global cases.     

Affiliated block shareholders and analyst optimism

We investigate several previously under-documented conflicts of interest that may result in analyst optimism by utilizing two unique features of brokerage firms in China, namely, the dominant ownership of large shareholders within the brokerage firms and the mandatory disclosure of brokerage firms’ commission income derived from each mutual fund client. We show that controlling shareholders of an analyst’s brokerage-firm put pressure on the analyst to report more optimistically biased earnings forecasts and recommendations to the stocks they hold larger positions in. We also find that the magnitude of analyst optimism increases with the shareholdings of the mutual funds that contribute commission fees to the analyst’s brokerage firm. These findings remain robust after incorporating a regulation change that reduces conflicts of interest in the brokerage industry and higher dimensional fixed effects, and thus are unlikely to be driven by reverse causality or omitted variable bias.