Augmenting transferred representations for stock classification

TL;DR

This paper demonstrates that transfer learning combined with feature space data augmentation significantly improves stock classification performance, doubling risk-adjusted returns compared to baseline models.

Contribution

It introduces a novel approach of augmenting features in the transfer learning framework for stock classification, leading to substantial performance gains.

Findings

Transfer learning more than doubles risk-adjusted returns.

Feature space augmentation outperforms input space augmentation by 20%.

Proposed method enhances stock classification accuracy.

Abstract

Stock classification is a challenging task due to high levels of noise and volatility of stocks returns. In this paper we show that using transfer learning can help with this task, by pre-training a model to extract universal features on the full universe of stocks of the S$\&$P500 index and then transferring it to another model to directly learn a trading rule. Transferred models present more than double the risk-adjusted returns than their counterparts trained from zero. In addition, we propose the use of data augmentation on the feature space defined as the output of a pre-trained model (i.e. augmenting the aggregated time-series representation). We compare this augmentation approach with the standard one, i.e. augmenting the time-series in the input space. We show that augmentation methods on the feature space leads to $20\%$ increase in risk-adjusted return compared to a model trained with transfer learning but without augmentation.