Avoiding Backtesting Overfitting by Covariance-Penalties: An Empirical Investigation of the Ordinary and Total Least Squares Cases

26 Pages Posted: 2 Jun 2019

See all articles by Adriano Koshiyama

Adriano Koshiyama

University College London, Financial Computing and Analytics Group, Department of Computer Science, Students

Nick Firoozye

UCL - Computer Science

Date Written: November 1, 2018

Abstract

Systematic trading strategies are rule-based procedures which choose portfolios and allocate assets. In order to attain certain desired return profiles, quantitative strategists must determine a large array of trading parameters. Backtesting, the attempt to identify the appropriate parameters using historical data available, has been highly criticized due to the abundance of misleading results. Hence, there is an increasing interest in devising procedures for the assessment and comparison of strategies, that is, devising schemes for preventing what is known as backtesting overfitting. So far, many financial researchers have proposed different ways to tackle this problem that can be broadly categorised into three types: Data Snooping, Overestimated Performance, and Cross-Validation Evaluation. In this paper, we propose a new approach to dealing with financial overfitting, a Covariance-Penalty Correction, in which a risk metric is lowered given the number of parameters and data used to underpins a trading strategy. We outlined the foundation and main results behind the Covariance-Penalty correction for trading strategies. After that, we pursue an empirical investigation, comparing its performance with some other approaches in the realm of Covariance-Penalties across more than 1300 assets, using Ordinary and Total Least Squares. Our results suggest that Covariance-Penalties are a suitable procedure to avoid Backtesting Overfitting, and Total Least Squares provides superior performance when compared to Ordinary Least Squares.

Keywords: Algorithmic Trading, Overfitting, Covariance-Penalty, Total Least Squares, Ordinary Least Squares, Quantitative Finance

Suggested Citation

Soares Koshiyama, Adriano and Firoozye, Nick, Avoiding Backtesting Overfitting by Covariance-Penalties: An Empirical Investigation of the Ordinary and Total Least Squares Cases (November 1, 2018). Available at SSRN: https://ssrn.com/abstract=3385636 or http://dx.doi.org/10.2139/ssrn.3385636

Adriano Soares Koshiyama (Contact Author)

University College London, Financial Computing and Analytics Group, Department of Computer Science, Students ( email )

Gower Street
London, London WC1E 6BT
United Kingdom

Nick Firoozye

UCL - Computer Science ( email )

Gower Street
London, WC1E 6BT
United Kingdom

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