Supervised Autoencoder MLP for Financial Time Series Forecasting

arXiv:2404.01866v1 Announce Type: cross
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 …

abstract arxiv augmentation autoencoder autoencoders financial forecasting impact information investment investment strategy labeling mlp networks neural networks noise paper performance q-fin.tr returns risk series stat.ml strategy through time series time series forecasting type