Clustering Structure of Microstructure Measures
This work addresses stock return prediction for financial analysts, but it is incremental as it focuses on optimizing existing measures rather than introducing new methods.
The paper tackled the problem of predicting stock returns by analyzing the clustering structure of market microstructure measures at a 10-second frequency to identify the best predictors, resulting in more accurate predictions with fewer predictors, reducing noise and improving model interpretability.
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.