A Mathematical Framework for Momentum Risk-Taking in Equities

Summary

The document presents momentum risk-taking as real-time management of short-term equity volatility. Its proposed system turns a theory of how news affects share prices into discrete tables of expected returns across investment horizons, which then guide automated trading decisions. It also relates the approach to machine learning, random processes, fractional Brownian motion, and decision-making beyond markets.

The authors report successful historical and real-time experiments, but provide no sample details, performance figures, or comparison methods in this description. The underlying price-impact theory is described as using specialized mathematical functions, and the machine-learning procedures are only briefly characterized. The summary therefore conveys a proposed framework rather than enough information to assess its assumptions, implementation, or robustness.

Key ideas

• The approach treats momentum risk-taking as real-time control of short-term equity volatility.
• A theory of news impact is discretized into expected-return tables for several investment horizons.
• Those tables are used as inputs to an automated equity trading system.
• The authors report historical and real-time experiments but give no quantitative results here.

Tags

# Artificial intelligence approach to momentum risk-taking


# Artificial intelligence approach to momentum risk-taking









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.