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http://dx.doi.org/10.5762/KAIS.2020.21.8.572

A Study on the stock price prediction and influence factors through NARX neural network optimization  

Cheon, Min Jong (Division of Information System, Hanyang University)
Lee, Ook (Division of Information System, Hanyang University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.21, no.8, 2020 , pp. 572-578 More about this Journal
Abstract
The stock market is affected by unexpected factors, such as politics, society, and natural disasters, as well as by corporate performance and economic conditions. In recent days, artificial intelligence has become popular, and many researchers have tried to conduct experiments with that. Our study proposes an experiment using not only stock-related data but also other various economic data. We acquired a year's worth of data on stock prices, the percentage of foreigners, interest rates, and exchange rates, and combined them in various ways. Thus, our input data became diversified, and we put the combined input data into a nonlinear autoregressive network with exogenous inputs (NARX) model. With the input data in the NARX model, we analyze and compare them to the original data. As a result, the model exhibits a root mean square error (RMSE) of 0.08 as being the most accurate when we set 10 neurons and two delays with a combination of stock prices and exchange rates from the U.S., China, Europe, and Japan. This study is meaningful in that the exchange rate has the greatest influence on stock prices, lowering the error from RMSE 0.589 when only closing data are used.
Keywords
Deep Learning; Artificial Intelligence; Stock Prediction; NARX; MATLA;
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Times Cited By KSCI : 2  (Citation Analysis)
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