Supervised Autoencoder MLP for Financial Time Series Forecasting

Bartosz Bieganowski and Robert Ślepaczuk
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Bartosz Bieganowski: University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group

No 2024-03, Working Papers from Faculty of Economic Sciences, University of Warsaw

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: machine learning; algorithmic investment strategy; supervised autoencoders; financial time series; trading strategy; risk-adjusted return (search for similar items in EconPapers)
JEL-codes: C14 C4 C45 C53 C58 G13 (search for similar items in EconPapers)
Pages: 61 pages
Date: 2024
New Economics Papers: this item is included in nep-big and nep-cmp
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https://www.wne.uw.edu.pl/download_file/3896/0 First version, 2024 (application/pdf)

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Working Paper: Supervised Autoencoder MLP for Financial Time Series Forecasting (2024)
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