Online Learning Algorithms for Statistical Arbitrage
This addresses the challenge of non-stationary processes in finance for traders and researchers, though it appears incremental as it adapts existing online learning methods to this domain.
The paper tackles the problem of statistical arbitrage in financial trading by proposing an online learning technique that avoids reliance on assumptions like mean reversion, achieving strong learning guarantees.
Statistical arbitrage is a class of financial trading strategies using mean reversion models. The corresponding techniques rely on a number of assumptions which may not hold for general non-stationary stochastic processes. This paper presents an alternative technique for statistical arbitrage based on online learning which does not require such assumptions and which benefits from strong learning guarantees.