Financial Portfolio Optimization: Computationally guided agents to investigate, analyse and invest!?

Financial portfolio optimization is a widely studied problem in mathematics, statistics, financial and computational literature. It adheres to determining an optimal combination of weights that are associated with financial assets held in a portfolio. In practice, portfolio optimization faces challenges by virtue of varying mathematical formulations, parameters, business constraints and complex financial instruments. Empirical nature of data is no longer one-sided; thereby reflecting upside and downside trends with repeated yet unidentifiable cyclic behaviours potentially caused due to high frequency volatile movements in asset trades. Portfolio optimization under such circumstances is theoretically and computationally challenging. This work presents a novel mechanism to reach to an optimal solution by encoding a variety of optimal solutions in a solution bank to guide the search process with regard to the global investment objective formulation. It conceptualizes the role of individual solver agents that contribute optimal solutions to a bank of solutions, and a super-agent solver that learns from the solution bank, and, thus reflects a knowledge-based computationally guided agents approach to investigate, analyse and reach to optimal solution for informed investment decisions. Conceptual understanding of classes of solver agents that represent varying problem formulations and, mathematically oriented deterministic solvers along with stochasticsearch driven evolutionary and swarm-intelligence based techniques for optimal weights are discussed in this work. Algorithmic implementation of the computational guidance approach from a bank of optimal solutions is presented by an enhanced neighbourhood generation mechanism in the Simulated Annealing algorithm. A framework for inclusion of heuristic knowledge and human expertise from financial literature related to investment decision making process is reflected via the introduction of controlled perturbation strategies using a decision matrix for neighbourhood generation. Empirical validation of the proposed methodology has been carried out for Bearish and Bullish market scenarios.