Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data
This work addresses the problem of noisy data in financial forecasting for cryptocurrency traders, but it is incremental as it builds on existing methods like autoencoders and labeling techniques.
The paper tackled financial time series forecasting for cryptocurrencies by using supervised autoencoders with noise augmentation and triple barrier labeling, finding that balanced noise and bottleneck size significantly boosted risk-adjusted returns, but excessive noise or large bottlenecks impaired performance.
This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders (SAE), to improve investment strategy performance. Using the Sharpe and Information Ratios, it specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns. The study focuses on Bitcoin, Litecoin, and Ethereum as the traded assets from January 1, 2016, 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.