Crash forecasting in the Korean stock market based on the log-periodic structure and pattern recognition

被引：8

作者：

Ko, Bonggyun ^{[1
]}

Song, Jae Wook ^{[1
]}

Chang, Woojin ^{[2
]}

机构：

[1] Samsung Elect, Big Data Analyt Grp, Mobile Commun Business, Suwon, South Korea

[2] Seoul Natl Univ, Dept Ind Engn, Seoul 151742, South Korea

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2018年 / 492卷

基金：

新加坡国家研究基金会;

关键词：

Log-periodicity; Price forecasting; Diffusion model; Pattern recognition; Non-linear time series; Financial market; BUBBLES;

D O I：

10.1016/j.physa.2017.09.074

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

The aim of this research is to propose an alarm index to forecast the crash of the Korean financial market in extension to the idea of Johansen-Ledoit-Sornette model, which uses the log-periodic functions and pattern recognition algorithm. We discover that the crashes of the Korean financial market can be classified into domestic and global crises where each category requires different window length of fitted datasets. Therefore, we add the window length as a new parameter to enhance the performance of alarm index. Distinguishing the domestic and global crises separately, our alarm index demonstrates more robust forecasting than previous model by showing the error diagram and the results of trading performance. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：308 / 323

页数：16

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