Trading via Selective Classification

TL;DR

This paper explores how selective classification can improve trading strategies by allowing models to abstain from predictions, thereby managing risk and potentially enhancing trading performance in commodity futures markets.

Contribution

It introduces the application of selective classification to trading, comparing binary and ternary models, and evaluates their effectiveness through comprehensive backtesting.

Findings

Selective classifiers can reduce trading risk by abstaining from uncertain predictions.

Ternary classification with a 'small move' class improves abstention decisions.

Empirical results show potential benefits of selective classification in trading strategies.

Abstract

A binary classifier that tries to predict if the price of an asset will increase or decrease naturally gives rise to a trading strategy that follows the prediction and thus always has a position in the market. Selective classification extends a binary or many-class classifier to allow it to abstain from making a prediction for certain inputs, thereby allowing a trade-off between the accuracy of the resulting selective classifier against coverage of the input feature space. Selective classifiers give rise to trading strategies that do not take a trading position when the classifier abstains. We investigate the application of binary and ternary selective classification to trading strategy design. For ternary classification, in addition to classes for the price going up or down, we include a third class that corresponds to relatively small price moves in either direction, and gives the classifier another way to avoid making a directional prediction. We use a walk-forward train-validate-test approach to evaluate and compare binary and ternary, selective and non-selective classifiers across several different feature sets based on four classification approaches: logistic regression, random forests, feed-forward, and recurrent neural networks. We then turn these classifiers into trading strategies for which we perform backtests on commodity futures markets. Our empirical results demonstrate the potential of selective classification for trading.