Optimal Trading Strategies as Measures of Market Disequilibrium

TL;DR

This paper analyzes high-frequency trading data to identify optimal trading strategies as indicators of market disequilibrium, using statistical tests and distribution models to understand market dynamics and dependencies.

Contribution

It introduces a novel approach to measure market disequilibrium through maximum profit strategies and analyzes high-frequency trading increments with advanced statistical models.

Findings

Optimal profit strategies effectively measure market disequilibrium.

Distribution models fit high-frequency trading increments well.

Dependencies between trading increments are statistically significant.

Abstract

For classification of the high frequency trading quantities, waiting times, price increments within and between sessions are referred to as the a-, b-, and c-increments. Statistics of the a-b-c-increments are computed for the Time & Sales records posted by the Chicago Mercantile Exchange Group for the futures traded on Globex. The Weibull, Kumaraswamy, Riemann and Hurwitz Zeta, parabolic, Zipf-Mandelbrot distributions are tested for the a- and b-increments. A discrete version of the Fisher-Tippett distribution is suggested for approximating the extreme b-increments. Kolmogorov and Uspenskii classification of stochastic, typical, and chaotic random sequences is reviewed with regard to the futures price limits. Non-parametric L1 and log-likelihood tests are applied to check dependencies between the a- and b-increments. The maximum profit strategies and optimal trading elements are suggested as measures of frequency and magnitude of the market offers and disequilibrium. Empirical cumulative distribution functions of optimal profits are reported. A few classical papers are reviewed with more details in order to trace the origin and foundation of modern finance.