Proposing the Mean-Variance-Skewness Portfolio Model Using Momentum and Contrarian Strategies (MCSs)

The use of quantitative methodologies for forecasting financial markets has now become an indispensable practice worldwide. Employing investment strategies along with genuine artificial intelligence can enable professional traders to mitigate risk and enhance returns. This study proposes an optimal framework for stock portfolio optimization through momentum and reversion strategies. Mean-variance-skewness optimization techniques were used in this study. Accordingly, the returns of analyzed stocks were ranked in descending order based on fluctuations in their prior earnings. Data from 160 firms listed on the Tehran Stock Exchange between 2014 and 2019 were used to depict the efficacy of the proposed strategies.The results of the objective functions for the specified portfolios were obtained in MATLAB. The data were then employed to facilitate the development and optimization of mean-variance skewness portfolios based on momentum and reversal strategies, which were compared in performance. Data analysis revealed that portfolios optimized using the momentum strategy exhibited superior performance and profitability to other strategies.