We survey the nascent literature on machine learning in the study of financial markets. We
highlight the best examples of what this line of research has to offer and recommend …

Stock markets can often be one of the most volatile places to invest. Statistical analysis of
past stock performance and external factors play a major role in the decision to buy or sell …

Using a quantile-on-quantile (QQ) approach, this study examines the heterogeneous and
asymmetric effects of monetary policy uncertainty (MPU) on stock returns in Group of Seven …

This study adopts the newly constructed macroeconomic attention indices (MAI) and
category-specific economic policy uncertainty (EPU) indices to predict stock volatility …

Deep learning technology is rapidly adopted in financial market settings. Using a large data
set from the Chinese stock market, we propose a return-risk trade-off strategy via a new …

We replicate 469 anomaly variables similar to those studied by using Chinese A-share data
and a reliable testing procedure with mainboard breakpoints and value-weighted returns …

The conventional momentum strategy performs poorly overall in China, because stock
prices behave very differently when markets are open for trading versus when they are …

Using retail investor posts on the stock forum from 2008 to 2019, we study the impact of
social media on the incidence of company violations. We find that social media promotes …

We investigate the application of machine learning algorithms for predicting stock price
crash risks by employing a set of firm-specific characteristics of the Chinese stock market …

基本面量化投资是近年来金融科技和量化投资研究的新热点. 作为人工智能的代表性技术,
机器学习能够大幅度提高经济学和管理学中预测类研究的效果. 本文系统性地运用机器学习 …