Stock Price Prediction Using Limit Order Book Data with Data Stratification and Multi-Phase Pre-training

Abstract

Predicting the movement of stock price is an important issue for market participants. Recently, there have been many attempts applying machine learning techniques in financial time series prediction. However, overfitting presents a huge challenge when machine learning approaches are used in financial time series prediction. In this paper, we propose a stock price prediction method utilizing limit order book data from stocks other than target stocks by stratifying the data and holding a multi-phase pre-training considering market liquidity. Experimental results shows that the proposed approach enhances prediction performance.