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- W2556200931 abstract "The application of multi-objective optimization to portfolio optimization has attracted increased attention in recent years. Portfolio optimization is defined as the task to select a combination of assets that simultaneously satisfy two objectives, namely maximized portfolio return and minimized portfolio volatility of return. In its original application, Markowitz (Journal of Finance, 7(1):77–91, 1952) used mean returns and the variances from each of those asset’s returns as inputs for the model. However, the ex-post results of portfolio optimization based on these historical assets’ mean returns and volatilities are sub-optimal. Although the historical mean and volatilities are substituted by forecasted returns and volatilities, the ex-post results are still sub-optimal due to the inaccuracy of the forecasting models leading to what is known as model risk. In this chapter, we propose a modified version of Multi-Objective Genetic Algorithm (MOGA) called MOGA3O (3 Objectives) to handle model risk in portfolio optimization using a number of ‘real world’ constraints. Model risk is captured by adding a third objective in the algorithm to approximate portfolio Sharpe ratio errors. Results provide clear evidence that the efficiency of optimizing portfolios, measured by Sharpe ratios, is improved by introducing a third objective to our proposed algorithm. Portfolios produced by our MOGA3O model are superior to the other two benchmark models throughout the sample period. It outperforms both the multi-objective model, which uses two objectives (MOGA), as well as the algorithm (MOGA_MEAN) which uses historical average returns and volatilities from each stock. We conclude that by incorporating a third objective model risk, which is inherent in the current forecasting process, is reduced. Our model therefore not only considers stocks that have high expected returns and low volatilities however it also takes its ability to forecast future values into consideration. Hence, is more inclined to select stocks that it is able to forecast more accurately, given that the other objectives, expected return and expected volatilities, are equal." @default.
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- W2556200931 date "2016-01-01" @default.
- W2556200931 modified "2023-10-17" @default.
- W2556200931 title "Handling Model Risk in Portfolio Selection Using Multi-Objective Genetic Algorithm" @default.
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- W2556200931 doi "https://doi.org/10.1057/978-1-137-48880-0_10" @default.
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