A hidden Markov model for predicting global stock market index

被引：1

作者：

Kang, Hajin ^{[1
]}

Hwang, Beom Seuk ^{[1
]}

机构：

[1] Chung Ang Univ, Dept Appl Stat, 84 Heukseok Ro, Seoul 06974, South Korea

来源：

KOREAN JOURNAL OF APPLIED STATISTICS | 2021年 / 34卷 / 03期

关键词：

global stock market index; hidden Markov model; KOSPI200; stock index prediction; support vector regression; PROBABILISTIC FUNCTIONS;

D O I：

10.5351/KJAS.2021.34.3.461

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Hidden Markov model (HMM) is a statistical model in which the system consists of two elements, hidden states and observable results. HMM has been actively used in various fields, especially for time series data in the financial sector, since it has a variety of mathematical structures. Based on the HMM theory, this research is intended to apply the domestic KOSPI200 stock index as well as the prediction of global stock indexes such as NIKKEI225, HSI, S&P500 and FTSE100. In addition, we would like to compare and examine the differences in results between the HMM and support vector regression (SVR), which is frequently used to predict the stock price, due to recent developments in the artificial intelligence sector.

引用

页码：461 / 475

页数：15

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