Abstract
Several improvements and alternatives to Mean Variance Optimization (MVO) have been suggested and used since its inception in 1952. The improvements have mostly included addition of constraints to the traditional MVO model, using alternative risk measures and using non risk-reward models. This paper seeks to compare MVO against the Threshold Accepting model, which is a general optimization model, in portfolio selection. Using data on 29 stocks in the Kenyan stock market we compare the relative performance of the two models using performance measures such as the Sharpe Ratio, Sortino Ratio and Information Ratio. We find that the Threshold Accepting (TA) model outperforms the Mean-Variance Optimization model though MVO yields similar results when we use monthly or weekly data but the latter is observed as a more consistent model. The TA model has portfolios with generally more superior risk-adjusted returns for the full period and during periods of high volatility in the stock market performance market. This observation implies that relatively more attention should be given to the TA model rather than relying entirely on the classical MVO approach.
Highlights
Mean-variance analysis as proposed by [1] [2] has been used in portfolio optimization where risk and return are traded off
We find that the Threshold Accepting (TA) model outperforms the Mean-Variance Optimization model though Mean Variance Optimization (MVO) yields similar results when we use monthly or weekly data but the latter is observed as a more consistent model
This paper concludes that the TA model outperforms the MVO model for portfolio selection but it does not perform well consistently over different time periods
Summary
Mean-variance analysis as proposed by [1] [2] has been used in portfolio optimization where risk and return are traded off. Linear simulations and other algorithms have been used for portfolio selection with the most common ones including, Threshold Accepting (TA) [6]; and, the Genetic Algorithm (GA) used together with a risk measure [7] These are non risk-reward heuristic models; where risk and reward as measures of portfolio performance are not dismissed but are incorporated as part of the specifications. In the Kenyan market, Portfolio managers mainly use the mean-variance analysis and factor models in portfolio selection These traditional models are viewed as useful and acceptable since they have been applied frequently for a long time. Based on 29 stocks in the Kenyan stock market this paper uses the Threshold Accepting model in constructing optimal portfolios and compares risk-adjusted performance across the portfolios selected with by the MVO model to determine the most optimal model best model for optimization. TA has been recommended as an optimization model that leads to better optimization results over classical optimization approaches [12]
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