This study explores modern portfolio theory by integrating the Black-Litterman portfolio with time-series clustering, specificially emphasizing K-shape clustering methodology. K-shape clustering enables grouping time-series data effectively, enhancing the ability to plan and manage investments in stock markets when combined with the Black-Litterman portfolio. Based on the patterns of stock markets, the objective is to understand the relationship between past market data and planning future investment strategies through backtesting. Additionally, by examining diverse learning and investment periods, it is identified optimal strategies to boost portfolio returns while efficiently managing associated risks. For comparative analysis, traditional Markowitz portfolio is also assessed in conjunction with clustering techniques utilizing K-Means and K-Means with Dynamic Time Warping. It is suggested that the combination of K-shape and the Black-Litterman model significantly enhances portfolio optimization in the stock market, providing valuable insights for making stable portfolio investment decisions. The achieved sharpe ratio of 0.722 indicates a significantly higher performance when compared to other benchmarks, underlining the effectiveness of the K-shape and Black-Litterman integration in portfolio optimization.

1. 서 론
2. 이론적 배경
    2.1 선행연구
    2.2 시계열 데이터 군집화
    2.3 포트폴리오 이론
3. 시계열 군집화 기반 포트폴리오 구성
    3.1 데이터 전처리
    3.2 시계열 군집화 기반 포트폴리오 백테스팅
    3.3 포트폴리오 수익성 지표
4. 백테스팅 결과 및 분석
    4.1 수익성 평가
    4.2 기간별 수익성 평가
    4.3 민감도 분석
5. 결 론
Acknowledgement
References

• 김예지(가천대학교 AI소프트웨어학부) | Yeji Kim (School of Computing, Gachon University)
• 조풍진(가천대학교 AI소프트웨어학부) | Poongjin Cho (School of Computing, Gachon University) Corresponding author