Clustering Structure of Microstructure Measures
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 48 references that are in the map. First posted on arXiv 5 Jul 2021.
Builds on
8 of 48 references.
References · 8
- 2015Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Controlcited by 34
- 2020Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copulacited by 10
- 2021Form 10-Q Itemizationcited by 10
- 2018High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Modelcited by 8
- 2020Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Modelcited by 7
- 2021A News-based Machine Learning Model for Adaptive Asset Pricingcited by 7
- 2020Efficient Clustering for Stretched Mixtures: Landscape and Optimalitycited by 6
- 2020FrequentNet: A Novel Interpretable Deep Learning Model for Image Classificationcited by 5
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 1.
3 papers.