Statistical arbitrage: Factor investing approach

Erdinc Akyildirim, Ahmet Goncu, Alper Hekimoglu, Duc Khuong Nguyen and Ahmet Sensoy

MPRA Paper from University Library of Munich, Germany

Abstract: We introduce a continuous time model for stock prices in a general factor representation with the noise driven by a geometric Brownian motion process. We derive the theoretical hitting probability distribution for the long-until-barrier strategies and the conditions for statistical arbitrage. We optimize our statistical arbitrage strategies with respect to the expected discounted returns and the Sharpe ratio. Bootstrapping results show that the theoretical hitting probability distribution is a realistic representation of the empirical hitting probabilities. We test the empirical performance of the long-until-barrier strategies using US equities and demonstrate that our trading rules can generate statistical arbitrage profits.

Keywords: Statistical arbitrage; factor models; trading strategies; geometric Brownian motion; Monte Carlo simulation. (search for similar items in EconPapers)
JEL-codes: G11 G12 G17 (search for similar items in EconPapers)
Date: 2021-02
New Economics Papers: this item is included in nep-ore
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Related works:
Journal Article: Statistical arbitrage: factor investing approach (2023)
Working Paper: Statistical Arbitrage: Factor Investing Approach (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:105766

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