Forecasting high‐frequency excess stock returns via data analytics and machine learning
研究利用伊斯坦布尔证券交易所提供的数据分析产品，结合多种机器学习方法预测日内超额收益，发现预测准确率超过50%，最高利润率达33%，其中XGBoost在长期分析中表现最佳，逻辑回归在短期分析中更优。
Abstract Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long‐term analysis (short‐term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.