• Title/Summary/Keyword: Leveraged ETF

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An Empirical Study on the price discovery of the Leveraged ETFs Market (레버리지 ETF시장의 가격발견에 관한 연구)

  • Kim, Soo-Kyung
    • Management & Information Systems Review
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    • v.35 no.2
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    • pp.1-12
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    • 2016
  • In this study, price discovery between the KOSPI200 spot, and leveraged ETFs(Leveraged KODEX, Leveraged TIGER, Leveraged KStar) is investigated using the vector error correction model(VECM). The main findings are as follows. Leveraged KODEX(Leveraged TIGER, Leveraged KStar) and KOSPI200 spot are cointegrated in most cases. There is no interrelations between the movement of Leveraged KODEX(Leveraged TIGER, Leveraged KStar) and KOSPI200 spot markets in case of daily data. Namely, in daily data, Leveraged KODEX(Leveraged TIGER, Leveraged KStar) doesn't plays more dominant role in price discovery than the KOSPI200 spot.

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A Study on the Investment Efficiency of Korean ETFs (한국상장지수펀드(ETF)의 투자효율성에 관한 연구)

  • Jung, Hee-Seog
    • Journal of Digital Convergence
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    • v.16 no.5
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    • pp.185-197
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    • 2018
  • The purpose of this study is to analyze the Korean ETF market, which is experiencing a rapid increase in the number of stocks, to identify the degree of investment efficiency and to present investment directions. The methodology and procedure are ETF yield, change trends, correlation and regression analysis of the ETFs traded between 2010 and 2018. As a result, the total return of domestic ETFs was 3.51%, which was lower than the KOSPI growth rate and the return on equity ETFs was 4.03%, which was low. Leverage ETF yields were below 3%, which was low. The return on bond and currency ETFs was less than 1%. The most profitable ETFs were index ETFs, followed by domestic and leveraged ETFs. This study has contributed to establishing considerations when purchasing ETFs from the viewpoint of investors. Future research will present the direction of ETF investment more precisely.

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
    • Journal of the Korea Convergence Society
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    • v.10 no.4
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    • pp.1-6
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    • 2019
  • 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.