Stock Index Prediction using Cointegration test and Quantile Loss

09/29/2021
by   Jaeyoung Cheong, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/17/2021

Design and Analysis of Robust Deep Learning Models for Stock Price Prediction

Building predictive models for robust and accurate prediction of stock p...
research
01/26/2022

Machine Learning for Stock Prediction Based on Fundamental Analysis

Application of machine learning for stock prediction is attracting a lot...
research
02/15/2023

A Comparative Predicting Stock Prices using Heston and Geometric Brownian Motion Models

This paper presents a novel approach to predicting stock prices using te...
research
04/17/2020

A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models

Prediction of future movement of stock prices has always been a challeng...
research
07/30/2020

Prediction of stock movement using phase space reconstruction and extreme learning machines

Stock movement prediction is regarded as one of the most difficult, mean...
research
08/07/2019

HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction

Many researchers both in academia and industry have long been interested...
research
08/05/2021

Two-Stage Sector Rotation Methodology Using Machine Learning and Deep Learning Techniques

Market indicators such as CPI and GDP have been widely used over decades...

Please sign up or login with your details

Forgot password? Click here to reset