  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.
