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Journal Title
Readers/Advisors
Adriamanalimanana, Bruno Dr., Chiang, Chen-Fu Dr., Spetka, Scott Dr.
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Term and Year
Fall 2021
Publication Date
2021-12
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Abstract
This project focuses on using Probabilistic Programming and more specifically
using the Bayesian approach to devise an effective strategy to trade. This project
does so by implementing a novel model on co-integration for pairs-trading using
probabilistic programming. As opposed to using the traditional and simpler
frequentist approach for pair determination I have implemented a more
sophisticated Bayesian approach for pair trading using probabilistic programming.
Pair trading is a market neutral strategy that enables traders to profit from virtually
any market conditions be it uptrend, downtrend, or sideways movement. It is
characterized as statistical arbitrage and convergence trading strategy. Pair Trading
combined with co-integration as criteria makes for a successful and reliable trading
strategy. Unlike simpler frequentist cointegration tests, the Bayesian approach
allows to monitor the relationship between a pair of equities over time, which
further allows to follow pairs whose cointegration parameters change steadily or
abruptly. Bayesian statistics also accounts for uncertainty in in making predictions.
It provides with mathematical tools to update beliefs about random events
considering seeing new data or evidence about those events and it can do without
having the need for a large dataset. It interprets probability as a measure of
believability or confidence that an individual might possess about the occurrence of
a particular event while including uncertainty in the equation. Along with a mean reversion trading algorithm, this approach can be effectively used as a viable
trading strategy, open for further evaluation and risk management.