PERFORMANCE OF DEEP LEARNING IN PREDICTION OF STOCK MARKET VOLATILITY

被引：34

作者：

Moon, Kyoung-Sook ^{[1
]}

Kim, Hongjoong ^{[2
]}

机构：

[1] Gachon Univ, Math Finance, Seongnam, South Korea

[2] Korea Univ, Math, Seoul, South Korea

来源：

ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH | 2019年 / 53卷 / 02期

基金：

新加坡国家研究基金会;

关键词：

volatility prediction; forecasting stock index; deep learning; long short term memory algorithm; RETURNS;

D O I：

10.24818/18423264/53.2.19.05

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Volatility forecasting is an important issue for investment analysis and risk management in finance. Based on the Long Short Term Memory (LSTM) deep learning algorithm, we propose an accurate algorithm for forecasting stock market index and its volatility. The proposed algorithm is tested on the data from 5 stock market indices including S&P500, NASDAQ, German DAX, Korean KOSPI200 and Mexico IPC over a 7-yearperiod from 2010 to 2016. The highest prediction performance is observed with hybrid momentum, the difference between the price and the moving average of the past prices, for the predictions of both market index and volatility. Unlike stock index, the prediction accuracy for the volatility does not show dependency on other financial variables such as open, low, high prices, volume, etc. except the volatility itself.

引用

页码：77 / 92

页数：16

共 21 条

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