Abstract
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.
Highlights
Portfolio optimization remains one among the essential info for investors before they create an investment selection attributable to its direct relationship with the performance of a corporation
Building a portfolio mix of the NSE top 30, after (3030 Iterations) the asset allocation distribution from the optimized Sharpe ratio function using genetic algorithm (GA) was generated, and the optimization technique advised that for an investment portfolio consisting of NSE top 30, 8% of the investment capital should be allocated to Unilever, 6.3% to international breweries, 6.2% to PZ, 5.2% to Nigerian breweries, 4.7% to UBA [. . .] [. . .] [. . .] [. . .] and 0.4% to Dangote sugar
From the analysis above, drawing a comparison between the optimal returns, that is, anticipated result using the optimum allocation and individual returns, we could observe that the optimal returns performed more than 60% of the individual returns and the optimal returns curve is the most stable curve compared to the individual returns curve
Summary
Portfolio optimization remains one among the essential info for investors before they create an investment selection attributable to its direct relationship with the performance of a corporation. 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 advise on which capital sharing ratio to reduce risk and attain optimal portfolio-mix targeted at achieving an optimal return on investment.
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