• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.725-735
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• 2021

This study investigates whether the Islamic Exchange-Traded Funds (ETFs) provide significant benefit to investors relative to conventional ETFs. Six pairs of Islamic and conventional ETFs with 10-year daily price data from 2010 to 2019 have been selected from major market indices like MSCI World Index, MSCI Emerging Markets, MyETF Dow Jones Islamic Market Malaysia, MSCI South East Asia and Wahed FTSE Shariah USA Index for this study. For ETFs that are launched after 2010, the price data from launch date to 2019 are used. Our results show: First, Islamic ETFs are more likely to trade at a premium rather than at a discount, implying the investors are willing to pay a premium. Second, it is also found that Islamic ETFs have a relatively shorter period of price deviation from the benchmark, implying more price stability. Third, conventional ETFs have higher return and lower tracking errors relative to Islamic ETFs. These new findings add to the stylized facts of Islamic ETFs in the extant literature for investors, plan sponsors and regulators as to the differences between the ETFs. As policy suggestion, asset management companies can design new investment products to bridge the gap between conventional and Islamic finance.

• Annual Conference of KIPS
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• 2023.11a
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• pp.5-7
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• 2023

본 논문에서는 정보 이론 기반 지표의 힘을 활용하여 전체 그래프 임베딩 방법론의 한 가지인 GL2vec 을 사용하여 임베딩을 생성하고, 이를 바탕으로 상장지수펀드 (ETF, Exchange Traded Fund) 수익률을 예측하는 모형을 생성하고자 하였다. 본 연구는 그래프 구조에 금융 데이터를 내장하고 고급 신경망 기술을 적용하여 예측 정확도를 향상시키는 데에 기여할 수 있음을 확인하였다.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.843-851
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• 2017

The rise of the Robo-advisor represents one of the most profound shifts in FinTech. It also raises concerns about their financial management. As the most Robo-Advisors utilize ETFs, we seek to determine the appropriate risk management model in estimating 95% Value-at-Risk (VaR) and 99% VaR in this paper. The GARCH and the Markov regime wwitching GARCH are evaluated in terms of the accuracy of probability, the independence of extreme events occurrence and both. The result shows that the Markov regime switching GARCH can be a good ETF risk management tool since it can reflect financial market structural changes into the volatility.

• 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.