Supervised Autoencoder MLP for Financial Time Series Forecasting

TL;DR

This study demonstrates that supervised autoencoders with noise augmentation and optimal bottleneck sizing can significantly improve financial time series forecasting and investment strategies, with careful parameter tuning being crucial.

Contribution

Introduces a novel supervised autoencoder approach with noise augmentation and a new optimization metric for triple barrier labeling in financial forecasting.

Findings

Supervised autoencoders improve risk-adjusted returns.

Balanced noise augmentation enhances strategy performance.

Excessive noise and large bottlenecks impair results.

Abstract

This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders, aiming to improve investment strategy performance. It specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns, using the Sharpe and Information Ratios. The study focuses on the S&P 500 index, EUR/USD, and BTC/USD as the traded assets from January 1, 2010, to April 30, 2022. Findings indicate that supervised autoencoders, with balanced noise augmentation and bottleneck size, significantly boost strategy effectiveness. However, excessive noise and large bottleneck sizes can impair performance, highlighting the importance of precise parameter tuning. This paper also presents a derivation of a novel optimization metric that can be used with triple barrier labeling. The results of this study have substantial policy implications, suggesting that financial institutions and regulators could leverage techniques presented to enhance market stability and investor protection, while also encouraging more informed and strategic investment approaches in various financial sectors.