Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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ETF Trading Based on Daily KOSPI Forecasting Using Neural Networks

신경회로망을 이용한 KOSPI 예측 기반의 ETF 매매

  • Hwang, Heesoo (Department of Electrical and Electronic Engineering, Halla University)
  • 황희수 (한라대학교 전기전자공학과)
  • Received : 2018.11.02
  • Accepted : 2019.01.20
  • Published : 2019.01.28
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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.

신경회로망은 적합한 수학적 모델에 대한 가정 없이 데이터로부터 유용한 정보를 추출해서 예측에 필요한 입출력 관계를 정의할 수 있어서 주가 예측에 널리 사용되어 왔다. 본 논문에서는 신경회로망 모델을 사용하여 일별 KOrea composite Stock Price Index (KOSPI) 종가를 예측한다. 예측된 종가를 기반으로 KOSPI에 연동해 변동하는 Exchange Traded Funds (ETFs)의 거래를 위한 알파 매매를 제안한다. 본 논문에 제안된 방법으로 KOSPI 예측 신경회로망 모델들을 구현하고 예측 정확도를 평가한다. 구현된 신경회로망 모델(NN1)의 학습 오차(MAPE)는 0.427, 평가 오차는 0.627이다. 평가용 데이터를 사용해 알파 매매를 시뮬레이션하면 수익률은 7.16 ~ 15.29 %를 보인다. 이는 125 거래일 데이터로 거둔 수익률로 제안된 알파 매매가 효과적임을 보인다.

Keywords

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Fig. 1. Architecture of the neural network

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Fig. 2. Trading interval for buying leverage and inverse ETFs

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Fig. 3. Output comparison of KOSPI daily close prices in the test

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Fig. 4. Profit rate per trade in bull market

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Fig. 5. Profit rate per trade in bear market

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Fig. 6. Cumulative profit rate in the test

Table 1. Input variables of the neural network model

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Table 2. The correlation coefficients of KOSPI, KOSPI200 and F-KOSPI200

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Table 3. Neural network parameters for the KOSPI prediction models

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Table 4. KOSPI prediction errors

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Table 5. Trade performance of the neural network models

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Table 6. Trade profit of the neural network models

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