Stock Index Prediction using Cointegration test and Quantile Loss
- URL: http://arxiv.org/abs/2109.15045v1
- Date: Wed, 29 Sep 2021 16:20:29 GMT
- Title: Stock Index Prediction using Cointegration test and Quantile Loss
- Authors: Jaeyoung Cheong, Heejoon Lee, Minjung Kang
- Abstract summary: We propose a method that can get better performance in terms of returns when selecting informative factors.
We compare the two RNN variants with quantile loss with only five factors obtained through the cointegration test.
Our experimental results show that our proposed method outperforms the other conventional approaches.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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