An Information-Theoretic Approach to Portfolio Optimization

In this work, we study Probabilistic Optimization Problems(PCOPs) under the a priori method. The randomness of input variables makes the analysis of PCOPs difficult. We use a mathematical modeling technique to model PCOPS through deductive reasoning. The method includes new matrices and transformations with the noisy communication channel. The proposed information-theoretical approach is applied to determining a set of assets, and their respective portfolio participation weights, satisfying the investor concerning the risk-return combination.