Pre-training Time Series Models with Stock Data Customization

TL;DR

This paper introduces a novel pre-training approach for stock time series models, utilizing stock-specific tasks and a transformer architecture to improve stock prediction and investment returns.

Contribution

It proposes three stock-specific pre-training tasks and a transformer model tailored for financial data, addressing the unique characteristics of stock time series.

Findings

SSPT outperforms existing methods in multiple stock datasets.

Pre-training improves investment return ratios and Sharpe ratios.

Insights into stock price series mechanisms from simulated data experiments.

Abstract

Stock selection, which aims to predict stock prices and identify the most profitable ones, is a crucial task in finance. While existing methods primarily focus on developing model structures and building graphs for improved selection, pre-training strategies remain underexplored in this domain. Current stock series pre-training follows methods from other areas without adapting to the unique characteristics of financial data, particularly overlooking stock-specific contextual information and the non-stationary nature of stock prices. Consequently, the latent statistical features inherent in stock data are underutilized. In this paper, we propose three novel pre-training tasks tailored to stock data characteristics: stock code classification, stock sector classification, and moving average prediction. We develop the Stock Specialized Pre-trained Transformer (SSPT) based on a two-layer transformer architecture. Extensive experimental results validate the effectiveness of our pre-training methods and provide detailed guidance on their application. Evaluations on five stock datasets, including four markets and two time periods, demonstrate that SSPT consistently outperforms the market and existing methods in terms of both cumulative investment return ratio and Sharpe ratio. Additionally, our experiments on simulated data investigate the underlying mechanisms of our methods, providing insights into understanding price series. Our code is publicly available at: https://github.com/astudentuser/Pre-training-Time-Series-Models-with-Stock-Data-Customization.