# Clustering Structure of Microstructure Measures

arXiv: https://arxiv.org/abs/2107.02283 · HTML: https://mlsanity.com/paper/2107.02283/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control](https://mlsanity.com/paper/1506.02632/) (2015)
- [Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula](https://mlsanity.com/paper/2006.10829/) (2020)
- [Form 10-Q Itemization](https://mlsanity.com/paper/2104.11783/) (2021)
- [High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model](https://mlsanity.com/paper/1804.08472/) (2018)
- [Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model](https://mlsanity.com/paper/2011.04171/) (2020)
- [A News-based Machine Learning Model for Adaptive Asset Pricing](https://mlsanity.com/paper/2106.07103/) (2021)
- [Efficient Clustering for Stretched Mixtures: Landscape and Optimality](https://mlsanity.com/paper/2003.09960/) (2020)
- [FrequentNet: A Novel Interpretable Deep Learning Model for Image Classification](https://mlsanity.com/paper/2001.01034/) (2020)

## Built on by (most cited first, 3 of 3)

- [The Adaptive Multi-Factor Model and the Financial Market](https://mlsanity.com/paper/2107.14410/) (2021)
- [Deep Learning Based Page Creation for Improving E-Commerce Organic Search Traffic](https://mlsanity.com/paper/2209.10792/) (2022)
- [Finding Lookalike Customers for E-Commerce Marketing](https://mlsanity.com/paper/2301.03147/) (2023)

## 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.