This study proposes a Bayesian framework for sequential stock investment decision-making using daily high and low stock price data. The proposed methodology models stock behavior using the Beta distribution and constructs a prior Normal-Gamma distribution based on the derived mean and variance. Additional parameters are estimated from the observed stock price range to enhance the framework's adaptability. The methodology establishes two Bayesian control charts that simultaneously monitor investment performance (Expected Stock Performance Control Chart, ESCFCC) and volatility (Variability of Stock Performance Control Chart, VSCFCC). These control charts are periodically updated with observed data and iteratively revise the posterior probability distribution as new data becomes available. This updating procedure provides investors with timely and data-driven decision-making information. For empirical validation, four investment scenarios were analyzed based on Samsung Electronics stock data from January 4, 2010, to May 31, 2017. The results highlight the usability of a sequential Stock Cash Flow Control Chart (SCFCC) framework, which utilizes daily high and low stock price data to enable real-time evaluation of investment performance and risk. By integrating statistical quality control charts with Bayesian probabilistic models, the framework establishes a system for continuously updating investment information and dynamically monitoring performance throughout the investment period.

1. 서 론
2. 주식 투자 의사결정 프레임워크
3. 사례 연구(Illustrative Example)
    3.1 초기 사전확률 분포
4. 결 론
Acknowledgement
References

• Seongwon Ryu(Department of Industrial Engineering, Chosun University) | 류승원 (조선대학교 산업공학과)
• Gyutai Kim(Department of Industrial Engineering, Chosun University) | 김규태 (조선대학교 산업공학과)
• Jong-Ho Shin(Department of Industrial Engineering, Chosun University) | 신종호 (조선대학교 산업공학과) Corresponding author