Self-FTS: A Self-Supervised Learning Method for Financial Time Series Representation in Stock Intraday Trading

Abstract

The stock price’s highly unstable fluctuation pattern makes learning efficient representation challenging to model the stock movement. The common deep learning often overfits after a few epochs of training and performs poorly in the validation set because the optimization objective is insufficient to characterize the stock adequately. In this paper, we propose Self-FTS, a self-supervised learning framework for financial time series representation, to learn the underlying representation and use in stock trading, affected by the fact that self-supervised learning is a promising technique for learning representation for extracting high dimensional features from unlabeled financial data to overcome the bias caused by handcrafted features. Specifically, we design several auxiliary tasks to generate samples with pseudo labels from the A-share stock price data sets and build a weight-sharing feature extraction backbone combined with a classification head to learn the pseudo labels based on the samples. Finally, We evaluate the learned representations extracted from the backbone by fine-tuning data sets labelled with stock returns to build an investment portfolio. Experimental analysis results on the Chinese stock market data show that our method significantly improves the stock trend forecasting performances and the actual investment income through backtesting compared to the current SOTA method, which strongly demonstrates our effective approach.