“Supervised Autoencoder MLP in Financial Time Series Forecasting” - seminar by QFRG & DSLab [04.03.2024]
We kindly invite you to join the coming seminar organised jointly by the Quantitative Finance Research Group and Data Science Lab.
During the coming meeting Bartosz Bieganowski – student of the Quantitative Finance program on our Faculty and prof. Robert Ślepaczuk – Head of the Department of Quantitative Finance and Machine Learning of our Faculty will present results of their research.
The meeting will take place on March 4th 2024 at 6:30 p.m. in hybrid mode: in room B002 at the Faculty of Economic Sciences, University of Warsaw (Długa 44/50) and via Zoom platform. Meeting ID: 642 899 6565, Passcode: xvx1Vh, the direct link: https://uw-edu-pl.zoom.us/j/6428996565?pwd=cEcwemFXOVRLUDVLYU8xRVUwZDhEdz09.
The meeting will be conducted in English. Please log in the latest at 6:20 p.m.
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.
