• Title/Summary/Keyword: ETF

Search Result 40, Processing Time 0.033 seconds

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

  • Jung, Hee-Seog
    • Journal of Digital Convergence
    • /
    • v.16 no.5
    • /
    • pp.185-197
    • /
    • 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.

Analysis on the Investment Effect of ETFs (ETF(상장지수펀드)의 투자효과 분석)

  • Jung, Hee-Seog;Kim, Sun-Je
    • Journal of Service Research and Studies
    • /
    • v.9 no.1
    • /
    • pp.51-71
    • /
    • 2019
  • The purpose of this research is to analyze the ETF market, which has a large increase in the number of listed shares and the market capitalization, and to identify the investment effects of ETFs. The study procedure and method used to calculate the return and change trend of ETFs for the sample of the transaction information, the transaction amount, and the market capitalization for the period from 2010 to 2018, and performed correlation and regression analysis. As a result, the ETF's total return was 2.11%, the domestic underwriting market ETF yield was 2.39%, and the stock ETF yield was 2.59%, which was lower than the KOSPI 200 index and the KOSPI 200 index. Index ETF was 2.63%, followed by stock ETF and oversea underwriting market ETF. The problem with ETF investment is that the annual return of ETFs and domestic ETFs is as low as 2%, which is not enough for investors to expect more than 5%. The study contributes to the realization of the ETF by analyzing the actual effect of the investment and to establishing considerations when buying ETFs from the viewpoint of investors. The direction of the research is to accumulate more ETF data and present the investment direction precisely.

A study on the information effect of tracking error affecting the sector ETF pricing (산업별 ETF의 가격결정에 영향을 미치는 추적오차의 정보효과에 관한 연구)

  • Byun, Young Tae;Lee, Sang Goo
    • Journal of Korea Society of Industrial Information Systems
    • /
    • v.18 no.1
    • /
    • pp.81-89
    • /
    • 2013
  • The purpose of this study is to analyze the information effect about the pricing using the ETF price, the benchmark index, and the total tracking error between the ETF price and the benchmark index on the index ETF market and sector ETF markets. Furthermore, the total tracking error is distinguished between the market tracking error and the NAV tracking error. Summary of this study are as follows: First, While KODEX200 don't have impact factors on the price, the most sectors of ETF have the factors affecting the pricing decision. They are the day before the total tracking error or market tracking error. Second, for the ETF price of the most industry, we find that the day before the market tracking error have the price discovery function because it is a negative(-) coefficients. But NAV tracking error could not find such a feature. Finally, the sector ETF price of energy chemical, construction, IT, and semiconductor industries affected of the day before positive(+) impact by the benchmark index price.

Hedging Performance Using KODEX200 ETF (KODEX200 ETF를 이용한 헤지성과)

  • Byun, Youngtae
    • The Journal of the Korea Contents Association
    • /
    • v.14 no.11
    • /
    • pp.905-914
    • /
    • 2014
  • In this study, we examine hedging effectiveness of KODEX200 ETF and KOSPI200 futures with respect to KOSPI200 spot or KODEX200 ETF using naive, the risk-minimization models and the VECM. The sample period covers from January 5. 2010 to October 31. 2013. Daily prices of the KOSPI200 spot, KOSPI200 futures and KODEX200 were used in this study. The results are summarized ans follows. First, this study show that there is cointegration relationship among KOSPI200 spot, futures and KODEX200 ETF market. Second, there is no significant difference in hedging performance among the models. Finally, hedged position of KOSPI200 cash(unhedged position)-KODEX200 ETF(hedge vehicle) or KODEX200 ETF-KOSPI200 futures seems to improve hedging performance compared to KOSPI200 cash-KOSPI200 futures. This implies that the portfolio managers may be encouraged to use the former than the latter.

ETF risk management (ETF 위험관리에 관한 연구)

  • Lee, Woosik
    • Journal of the Korean Data and Information Science Society
    • /
    • v.28 no.4
    • /
    • pp.843-851
    • /
    • 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.

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
    • /
    • v.10 no.4
    • /
    • pp.1-6
    • /
    • 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.

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

  • Kim, Soo-Kyung
    • Management & Information Systems Review
    • /
    • v.35 no.2
    • /
    • pp.1-12
    • /
    • 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.

  • PDF

Hybrid Machine Learning Model for Predicting the Direction of KOSPI Securities (코스피 방향 예측을 위한 하이브리드 머신러닝 모델)

  • Hwang, Heesoo
    • Journal of the Korea Convergence Society
    • /
    • v.12 no.6
    • /
    • pp.9-16
    • /
    • 2021
  • In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.

ETF Recommendation Service through AI RoboAdvisor (AI 로보어드바이저를 통한 ETF 추천 서비스)

  • Lee, Eun-Ju;Park, Seol-Ha;Lee, Seung-Jun;Lee, Ye-Ryung;Moon, Jae-Hyun
    • Proceedings of the Korea Information Processing Society Conference
    • /
    • 2021.11a
    • /
    • pp.1059-1062
    • /
    • 2021
  • 투자에 대한 관심 증가에 따라 적은 비용과 시간으로 객관적인 정보 제공의 필요성 증가와 함께 인공지능 기술을 활용한 로보어드바이저 서비스가 확대되었다. 또한, 최근 ETF 를 통한 안정적인 투자에 대한 선호도가 증가함에 따라 ETF 중심의 AI 로보어드바이저 추천 서비스가 필요할 것으로 보인다. 하지만, 기존의 투자 어플리케이션에서는 뉴스 기반의 감성적인 요인이 반영되지 않은 추천 방식으로 주가에 영향을 미치는 다양한 요인들을 고려하지 못하는 문제점이 있다. 이에 본 연구에서는 뉴스의 감성분석을 통한 감성지수를 기반으로 새로운 주가 예측 모델을 제안하고, 사용자의 투자 성향 분석을 통한 맞춤 추천 서비스를 통해 개인화된 ETF 서비스를 제공한다.

A Study on Dynamic Glide Path of Target Date Fund Reflecting Market Expectations (시장기대를 반영한 타겟 데이트 펀드의 동적 글라이드패스에 관한 연구)

  • Moon, Myung-Deok;Kim, Sun Woong;Choi, Heung Sik
    • Knowledge Management Research
    • /
    • v.22 no.3
    • /
    • pp.17-29
    • /
    • 2021
  • The purpose of this study is to analyze investment performance by applying dynamic methodologies that reflect market expectations rather than traditional static methodologies in applying the glide path of target date fund. In calculating market expectations, the number of distributed shares in the ETF market was used, and the dynamic glide path model portfolio considering market expectations in the analysis period from late 2011 to October 2020 could show better results than the existing static glide path. According to the analysis, increasing the portion of risky assets at a time when the number of shares in the ETF's distribution increases, and in the opposite case, reducing the portion of risky assets is advantageous for profit. The results of this study are expected to provide useful theoretical and practical implications for researchers and asset management workers who are interested in knowledge management from a broad perspective beyond the boundary of pension asset management to the public fund market and ETF market.