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dc.contributor.author임규건-
dc.date.accessioned2021-10-01T01:46:06Z-
dc.date.available2021-10-01T01:46:06Z-
dc.date.issued2020-04-
dc.identifier.citation한국IT서비스학회지, v. 19, no. 2, page. 109-124en_US
dc.identifier.issn1975-4256-
dc.identifier.urihttp://koreascience.or.kr/article/JAKO202021741260602.page-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/165387-
dc.description.abstractFacing the 4th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.en_US
dc.language.isoko_KRen_US
dc.publisher한국IT서비스학회en_US
dc.subjectRobo Advisoren_US
dc.subjectMarkov Chain Monte Carlo (MCMC)en_US
dc.subjectOptimizationen_US
dc.subjectFinancial Indicatorsen_US
dc.subjectMarket Forecastsen_US
dc.subjectArtificial Intelligenceen_US
dc.title금융 지표와 파라미터 최적화를 통한 로보어드바이저 전략 도출 사례en_US
dc.title.alternativeA Case of Establishing Robo-advisor Strategy through Parameter Optimizationen_US
dc.typeArticleen_US
dc.relation.no2-
dc.relation.volume19-
dc.identifier.doi10.9716/KITS.2020.19.2.109-
dc.relation.page109-124-
dc.relation.journal한국IT서비스학회지-
dc.contributor.googleauthor강민철-
dc.contributor.googleauthor임규건-
dc.contributor.googleauthorKang, Mincheal-
dc.contributor.googleauthorLim, Gyoo Gun-
dc.relation.code2020039819-
dc.sector.campusS-
dc.sector.daehakSCHOOL OF BUSINESS[S]-
dc.sector.departmentDIVISION OF BUSINESS ADMINISTRATION-
dc.identifier.pidgglim-
Appears in Collections:
GRADUATE SCHOOL OF BUSINESS[S](경영전문대학원) > BUSINESS ADMINISTRATION(경영학과) > Articles
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