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Predicting The Direction of The Daily KOSPI Movement Using Neural Networks For ETF Trades

신경회로망을 이용한 일별 KOSPI 이동 방향 예측에 의한 ETF 매매

Hwang, Heesoo
황희수

  • Received : 2019.02.20
  • Accepted : 2019.04.20
  • Published : 2019.04.28

Abstract

Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

Keywords

Convergence;Stock Movement Direction Prediction;Neural Network;ETF Trading;Pattern Classification;KOSPI

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