This paper builds the clustering model of measures of market microstructure features which are popular in predicting stock returns. In a 10-second time-frequency, we study the clustering structure of different measures to find out the best ones for predicting. In this way, we can predict more accurately with a limited number of predictors, which removes the noise and makes the model more interpretable.

翻译：本文构建了市场微观结构特征计量的集群模型,这些特征在预测股票回报方面很受欢迎。 在10秒的时间频度中,我们研究了不同计量的集群结构,以找出最佳的预测方法。 这样,我们可以用数量有限的预测器进行更准确的预测,这些预测器可以消除噪音,使模型更容易解释。