A Case Study on the Establishment of an Equity Investment Optimization Model based on FinTech: For Institutional Investors

핀테크 기반 주식투자 최적화 모델 구축 사례 연구 : 기관투자자 대상

  • 김홍곤 (연세대학교 (투자정보공학과), DGB자산운용) ;
  • 김소담 (연세대학교 정보대학원) ;
  • 김희웅 (연세대학교 정보대학원)
  • Received : 2017.12.28
  • Accepted : 2018.02.23
  • Published : 2018.03.31


The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.


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