Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257803 
Year of Publication: 
2021
Citation: 
[Journal:] International Journal of Financial Studies [ISSN:] 2227-7072 [Volume:] 9 [Issue:] 4 [Article No.:] 58 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-42
Publisher: 
MDPI, Basel
Abstract: 
We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management focused on short-term volatility. Its implementation, a fully automated momentum equity trading system, is systematically discussed in this paper. It 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. We begin with a new mathematical approach to news impact on share prices, which models well their power-type 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, basically, expected returns for main investment horizons, the key in our trading system. A preimage of our approach is a new contract card game. There are relations to random processes and the fractional Brownian motion. The ODE we obtained, especially those of Bessel-type, appeared to give surprisingly accurate modeling of the spread of COVID-19.
Subjects: 
artificial intelligence
behavioral finance
Bessel functions
cognitive theory
decision-making
econophysics
momentum trading
news impact
power laws
risk management
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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