Supervised Autoencoder MLP for Financial Time Series Forecasting

ArXiv ID: 2404.01866 “View on arXiv”

Authors: Unknown

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.

Keywords: Supervised Autoencoders, Triple Barrier Labeling, Time Series Forecasting, Noise Augmentation, Sharpe Ratio, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper employs well-defined neural network architectures and hyperparameter tuning with a clear walk-forward backtesting protocol on specific assets, demonstrating strong empirical rigor. However, the mathematical complexity is moderate, primarily involving standard deep learning concepts and statistical performance metrics rather than dense theoretical derivations.

  flowchart TD
    A["Research Goal:<br/>Enhance Financial Time Series<br/>Forecasting via Supervised Autoencoders"] --> B["Data & Inputs:<br/>S&P 500, EUR/USD, BTC/USD<br/>Jan 2010 - Apr 2022"]
    B --> C["Methodology:<br/>Triple Barrier Labeling<br/>& Noise Augmentation"]
    C --> D["Computational Process:<br/>Supervised Autoencoder MLP<br/>(Tuning Bottleneck/Noise)"]
    D --> E["Evaluation:<br/>Sharpe & Information Ratios"]
    E --> F["Key Findings/Outcomes:<br/>Optimal Noise/Bottleneck Boosts Returns<br/>Excessive Noise Impairs Performance<br/>Novel Optimization Metric Derivation"]