This chapter explains why empirical covariance matrices are \"noisy\" and \"ill-conditioned\" and presents a method based on Random Matrix Theory (RMT) to clean them.
Research Topics:
- This document outlines methods for processing and structuring financial data for quantitative analysis, moving from raw data types to structured bars, handling multi-product series, and sampling features for machine learning.
- This chapter introduces fractional differentiation as a method to solve the \"Stationarity vs.
- This document discusses the critical process of labeling financial data for supervised machine learning.
- This chapter addresses a critical problem in financial machine learning: observations are not Independent and Identically Distributed (IID).