Stock Index Prediction using Cointegration test and Quantile Loss

TL;DR

This paper proposes a stock index prediction method that combines cointegration test-based factor selection with quantile loss training, demonstrating improved returns and risk-adjusted performance over conventional models.

Contribution

It introduces a novel approach that enhances stock prediction accuracy by selecting informative factors via cointegration tests and training models with quantile loss.

Findings

Proposed method outperforms conventional approaches in return metrics.

Using cointegration test improves factor selection for stock prediction.

Quantile loss training enhances model robustness and performance.

Abstract

Recent researches on stock prediction using deep learning methods has been actively studied. This is the task to predict the movement of stock prices in the future based on historical trends. The approach to predicting the movement based solely on the pattern of the historical movement of it on charts, not on fundamental values, is called the Technical Analysis, which can be divided into univariate and multivariate methods in the regression task. According to the latter approach, it is important to select different factors well as inputs to enhance the performance of the model. Moreover, its performance can depend on which loss is used to train the model. However, most studies tend to focus on building the structures of models, not on how to select informative factors as inputs to train them. In this paper, we propose a method that can get better performance in terms of returns when selecting informative factors using the cointegration test and learning the model using quantile loss. We compare the two RNN variants with quantile loss with only five factors obtained through the cointegration test among the entire 15 stock index factors collected in the experiment. The Cumulative return and Sharpe ratio were used to evaluate the performance of trained models. Our experimental results show that our proposed method outperforms the other conventional approaches.