Invasive weed optimization based metaheuristic approach for solving constrained risk budgeted portfolio selection problem

Portfolio management focuses on investing in the financial sector to achieve the highest return while tolerating the lowest risk. The optimal financial allocation has long been considered one of the essential aspects of risk-adjusted financial sector investment. Therefore, many optimization techniques have been established to maximize the return on risk. This paper presents a novel framework named the risk-budgeted portfolio selection (RBPS) model, which allocates the total risk of a portfolio across different securities by incorporating risk budgeting (RB) levels to ensure the portfolio's risk is diversified while maximizing the Sharpe ratio. To address the proposed RBPS model, an invasive weed optimization (IWO) algorithm-based solution method is suggested, and risk budgeting constraints are accommodated using resilient and flexible repairing procedures. Experiments have been performed using two newly created datasets from the Sensex of the Bombay Stock Exchange and the National Stock Exchange from India. The percentage improvement of the maximum Sharpe ratio obtained by IWO is up to 1.95 % at RB% = 12.5 among its peer's algorithms. Moreover, the experiments have been extended to global benchmark datasets to evaluate the proposed approach. Finally, statistical analysis is conducted to test the significance of improvement in the RBPS model.