Centralizing-Unitizing Standardized High-Dimensional Directional Statistics and Its Applications in Finance

TL;DR

This paper introduces a novel high-dimensional directional statistical framework for analyzing the Information Coefficient in finance, providing explicit formulas, optimization methods, and empirical insights into stock market data.

Contribution

It defines IC within high-dimensional directional statistics, derives its moments and covariance, and explores its optimization and empirical applications in finance.

Findings

Derived closed-form covariance matrix of directional statistics

Optimized IC for maximum expectation and minimum variance

Empirical analysis reveals IC dynamics in Chinese stock market

Abstract

Cross-sectional "Information Coefficient" (IC) is a widely and deeply accepted measure in portfolio management. The paper gives an insight into IC in view of high-dimensional directional statistics: IC is a linear operator on the components of a centralizing-unitizing standardized random vector of next-period cross-sectional returns. Our primary research first clearly defines IC with the high-dimensional directional statistics, discussing its first two moments. We derive the closed-form expressions of the directional statistics' covariance matrix and IC's variance in a homoscedastic condition. Also, we solve the optimization of IC's maximum expectation and minimum variance. Simulation intuitively characterizes the standardized directional statistics and IC's p.d.f.. The empirical analysis of the Chinese stock market uncovers interesting facts about the standardized vectors of cross-sectional returns and helps obtain the time series of the measure in the real market. The paper discovers a potential application of directional statistics in finance, proves explicit results of the projected normal distribution, and reveals IC's nature.