Artificial intelligence approach to momentum risk-taking

TL;DR

This paper introduces a mathematical model and an automated trading system for momentum risk-taking in stock markets, combining advanced algebraic theory, machine learning, and real-time experiments to improve short-term volatility management.

Contribution

It presents a novel algebraic theory of news impact on prices and integrates it into a fully automated momentum trading system with extensive empirical validation.

Findings

Successful implementation in historical and real-time experiments

Development of a new algebraic theory involving Bessel and hypergeometric functions

Introduction of a novel trading system based on expected returns and neural network techniques

Abstract

We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management focused on short-term volatility of stock markets. Its implementation, our fully automated momentum equity trading system presented systematically, proved to be successful in extensive historical and real-time experiments. Momentum risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning with deep roots in cognitive science; its variants beyond stock markets are discussed. We begin with a new algebraic-type theory of news impact on share-prices, which describes well their power growth, periodicity, and the market phenomena like price targets and profit-taking. This theory generally requires Bessel and hypergeometric functions. Its discretization results in some tables of bids, which are basically expected returns for main investment horizons, the key in our trading system. The ML procedures we use are similar to those in neural networking. A preimage of our approach is the new contract card game provided at the end, a combination of bridge and poker. Relations to random processes and the fractional Brownian motion are outlined.