A Comparative Evaluation of Predominant Deep Learning Quantified Stock Trading Strategies

TL;DR

This paper compares three deep learning-based stock trading strategies through simulations in adverse market conditions, demonstrating that these models can mitigate losses and outperform benchmarks, with LSTM showing the best resilience.

Contribution

It reconstructs and compares three distinct deep learning trading models, providing insights into their performance during prolonged market downturns.

Findings

Deep learning strategies can prevent losses in adverse markets.

LSTM-based strategy outperforms others in sustained downturns.

Deep learning models improve portfolio resilience during crises.

Abstract

This study first reconstructs three deep learning powered stock trading models and their associated strategies that are representative of distinct approaches to the problem and established upon different aspects of the many theories evolved around deep learning. It then seeks to compare the performance of these strategies from different perspectives through trading simulations ran on three scenarios when the benchmarks are kept at historical low points for extended periods of time. The results show that in extremely adverse market climates, investment portfolios managed by deep learning powered algorithms are able to avert accumulated losses by generating return sequences that shift the constantly negative CSI 300 benchmark return upward. Among the three, the LSTM model's strategy yields the best performance when the benchmark sustains continued loss.