## 6 Conclusions  
This study introduces a novel approach to portfolio optimization by integrating the DEA model with the MSV framework. Initially, the DEA model assesses asset efficiency using semivariance and beta as risk inputs and expected return as the output. Subsequently, \begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{3}{c}{**Efficiency**} \\ \cline{2-4}
**Portfolio** & **NSGA2** & **SPEA2** & **NN** \\ \hline
1 & 0.9881 & 0.9593 & 1 \\
2 & 1 & 0.9804 & 1 \\
3 & 0.9387 & 0.912 & 0.9653 \\
4 & 0.9956 & 0.9973 & 0.9814 \\
5 & 0.9986 & 0.8193 & 0.9339 \\
6 & 0.9171 & 0.8817 & 0.9742 \\
7 & 0.6897 & 0.8413 & 1 \\
8 & 0.858 & 0.9662 & 0.9922 \\
9 & 0.8511 & 1 & 0.9561 \\
10 & 0.9986 & 0.9479 & 0.9542 \\
11 & 0.9655 & 0.9852 & 0.9725 \\ \hline \end{tabular}
\end{table} 
Table 8: Efficiency of portfolios. an optimal portfolio is constructed from the identified efficient stocks. To determine the optimal weights of these stocks within the portfolio, the MSV model is solved using both a neural network and evolutionary algorithms (NSGA2 and SPEA2). In the neural network-based optimization method, random weights are fed into the neural network, with the portfolio's semivariance serving as the output. This output then acts as the objective function to determine the optimal asset weights. Conversely, in the evolutionary algorithm-based optimization, the MSV model is solved for the efficient stocks using the NSGA2 and SPEA2 algorithms, and their respective outcomes are compared. The findings indicate that optimizing efficient stocks with a neural network significantly reduces the portfolio risk and yields higher efficiency compared to both evolutionary algorithm-based methods. Notably, the NSGA2 algorithm outperforms SPEA2 in this context. These results suggest that neural networks are effective in discerning the inherent non-linear behavior within market data, thus offering investors a valuable tool for both forecasting and optimizing their investment portfolios. The success of the neural network in solving the semivariance model underscores its robust learning capabilities. Furthermore, the favorable performance of genetic algorithms in generating efficient portfolios opens promising avenues for future research, particularly in integrating the neural network's objective function with these algorithms for enhanced optimization and analysis. The findings of this research are derived from data pertaining to the Iranian automotive sector over a specific period. Consequently, the unique fluctuations of the Iranian market may have influenced the model's performance. Therefore, the proposed model's performance requires further testing and validation in other markets such as developed markets with different regulatory frameworks or additional emerging markets--as well as across different asset classes, including bonds, commodities, and currencies. Additionally, generalizing these results to markets experiencing lower returns, recessionary conditions, or crises requires further validation. Hence, to further develop the DEA-\(\beta\)-MSV framework and broaden its applicability in more intricate market scenarios, several promising avenues for future research exist. In illiquid and noisy data markets (e.g., small-cap stocks), robust optimization can be employed to effectively manage the inherent uncertainties in return and risk estimations. It will be crucial to integrate liquidity constraints and transaction cost models into the optimization problem, while also leveraging robust DEA models for more reliable asset filtering in such volatile conditions. For bond portfolios, the concept of downside risk can be redefined using yield spread semivariance, necessitating re-training the ANN on bond-specific data and incorporating bond-specific constraints (like duration and credit rating) into the optimization model. Furthermore, DEA inputs and outputs will require updates to accurately assess the bond efficiency.