Artificial intelligence model for building investment portfolio optimization mix using historical stock prices data

Purpose - The purpose of this paper is to implement a genetic algorithmic geared toward building an optimized investment portfolio exploring data set from stocks of firms listed on the Nigerian exchange market. To provide a research-driven guide toward portfolio business assessment and implementation for optimal risk-return. Design/methodology/approach - The approach was to formulate the portfolio selection problem as a mathematical programming problem to optimize returns of portfolio; calculated by a Sharpe ratio. A genetic algorithm (GA) is then applied to solve the formulated model. The GA lead to an optimized portfolio, suggesting an effective asset allocation to achieve the optimized returns. Findings - The approach enables an investor to take a calculated risk in selecting and investing in an investment portfolio best minimizes the risks and maximizes returns. The investor can make a sound investment decision based on expected returns suggested from the optimal portfolio. Research limitations/implications - The data used for the GA model building and implementation GA was limited to stock market prices. Thus, portfolio investment that which to combines another capital market instrument was used. Practical implications - Investment managers can implement this GA method to solve the usual bottleneck in selecting or determining which stock to advise potential investors to invest in, and also advise on which capital sharing ratio to reduce risk and attain optimal portfolio-mix targeted at achieving an optimal return on investment. Originality/value - The value proposition of this paper is due to its exhaustiveness in considering the very important measures in the selection of an optimal portfolio such as risk, liquidity ratio, returns, diversification and asset allocation.