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http://dx.doi.org/10.15207/JKCS.2019.10.1.007

ETF Trading Based on Daily KOSPI Forecasting Using Neural Networks  

Hwang, Heesoo (Department of Electrical and Electronic Engineering, Halla University)
Publication Information
Journal of the Korea Convergence Society / v.10, no.1, 2019 , pp. 7-12 More about this Journal
Abstract
The application of neural networks to stock forecasting has received a great deal of attention because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from data, which is required to describe nonlinear input-output relations of stock forecasting. The paper builds neural network models to forecast daily KOrea composite Stock Price Index (KOSPI), and their performance is demonstrated. MAPEs of NN1 model show 0.427 and 0.627 in its learning and test, respectively. Based on the predicted KOSPI price, the paper proposes an alpha trading for trades in Exchange Traded Funds (ETFs) that fluctuate with the KOSPI200. The alpha trading is tested with data from 125 trade days, and its trade return of 7.16 ~ 15.29 % suggests that the proposed alpha trading is effective.
Keywords
Convergence; Stock Price Forecasting; Stock Price Modeling; Neural Network; ETF; Trading; Alpha Trading; Time Series Forecasting;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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