Daily Stock Price Regime Model Detection using Markov Switching Model

被引：0
|
作者：
Prihartanti, Wiwik [1 ]
Rasyid, Dwilaksana Abdullah [2 ]
Iriawan, Nur [2 ]
机构：
[1] Univ WR Supratman, Dept Business Adm, Fac Social & Polit Sci, Surabaya 60111, Indonesia
[2] Inst Teknol Sepuluh Nopember, Fac Social & Data Anal, Dept Stat, Surabaya 60111, Indonesia
来源：
MATEMATIKA | 2020年 / 36卷 / 02期
关键词：
Stock price; EM Algorithm; Regime model; Markov Switching Model; AIC; Transition probability; TIME-SERIES;
D O I：
暂无
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
Changes in stock prices randomly occur due to market forces with reoccurrence possibilities. This process, also known as the structural break model, is captured through changes in the linear model parameters among periods with the Markov Switching Model (MSwM) used for detection. Furthermore, using the smallest Akaike Information Criterion (AIC) value on all feasible MSwM alternatives formed for a daily stock price, the complete MSwM model with its Markov transition is determined. This method has been tested and applied to daily stock price data in several sectors. The result showed that the number of regime models coupled with its transition probability helped investors make investment decisions.
引用
收藏
页码：127 / 140
页数：14
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