An Intelligent Statistical Arbitrage Trading System

Abstract

This paper proposes an intelligent combination of neural network theory and financial statistics for the detection of statistical arbitrage opportunities in specific pairs of stocks. The proposed intelligent methodology is based on a class of neural network-GARCH autoregressive models for the effective handling of the dynamics related to the statistical mispricing between relative stock prices. The performance of the proposed intelligent trading system is properly measured with the aid of profit & loss diagrams, for a number of different experimental settings (i.e. sampling frequencies). First results seem encouraging; nevertheless, further experimentation on the optimal sampling frequency, the forecasting horizon and the points of entry and exit is necessary, in order to achieve highest economic value when transaction costs are taken into account.

Keywords: Statistical arbitrage, Neural Networks, GARCH models

JEL Classification: C14, C22, G11