Acceptance sampling-based investment indicators for financial security selection and portfolio optimization

Area of Science:

• Finance
• Statistics
• Investment Management

Background:

• Traditional portfolio optimization often includes underperforming assets.
• Determining optimal sample sizes for investment decisions can be complex.
• Incorporating buyer's risk and uncertainty is crucial for robust financial strategies.

Purpose of the Study:

• To propose novel acceptance sampling investment indicators for financial security selection.
• To develop a generalized mean-variance portfolio optimization model incorporating risk and uncertainty.
• To enhance decision-making processes for both buyers and sellers of securities.

Main Methods:

• Development of simple and double acceptance sampling indicators based on historical data and tolerable thresholds.
• Formulation of a generalized mean-variance portfolio optimization model.
• Empirical validation using DJIA 30 index stocks.

Main Results:

• The proposed indicators effectively determine security acceptance or rejection.
• The double acceptance sampling indicator provides equivalent information with a smaller sample size.
• The generalized model demonstrated reduced portfolio size with equivalent or superior returns compared to the Markowitz model.

Conclusions:

• Acceptance sampling indicators offer an efficient method for investment decision-making.
• The generalized portfolio optimization model effectively integrates buyer's risk and entropy-based uncertainty.
• The proposed approach enhances portfolio performance and reduces investment risk.