## 5 Application: Iran Stock Market  
This research employs an applied methodology, utilizing quantitative data within a post-event framework and drawing upon historical data from automotive enterprises. Data was collected via archival retrieval from stock exchange repositories. The statistical sample comprises data from 21 automotive firms over the period of 2017 to 2021. The selection of the Iranian automotive industry for this study was driven by several compelling factors. It represents a large and strategically important sector within Iran's economy, characterized by substantial transaction volumes and a rich availability of data, including stock prices, returns, and financial information. Furthermore, the industry's inherent high volatility and considerable susceptibility to economic policies present a challenging yet fertile ground for the rigorous testing of financial models. This dynamism provides a realistic scenario for evaluating model performance under complex market conditions. Finally, the chosen period of 2017-2021 was critical as it yielded a robust and comprehensive dataset suitable for both model training and testing, unlike preceding or subsequent periods, which contained incomplete or less pertinent data. Statistical computations and model implementation were performed utilizing MATLAB software. Market evaluation commenced with an analysis of the automotive market index, as illustrated in Figure 2. Observations indicate a non-normal distribution of the market index. Further analysis, through the construction of a histogram, revealed a positive skew, as evidenced in Figure 2. This positive skewness suggests a potential for future appreciation in the automotive price index, implying opportunities for substantial returns for market participants. Subsequently, the statistical sample underwent analysis to ascertain the beta risk, semivariance risk, and expected returns for each stock. Subsequently, the statistical sample underwent analysis to calculate the beta risk, semivariance risk, and expected returns for each stock from Equations (1), (8), and (13). \[\varphi[\Re]=E[\Re]=\frac{1}{n}\sum_{i=1}^{n}\Re_{i}\ \ \ (\Re_{i}\ \ \textit{is the daily return}). \tag{13}\]  
Additional details on the computation of daily returns and semivariance can be found in Appendix A. The respective values for \(\beta\), semivariance, and expected returns are presented in Figure 3. In Figure 3, the negative beta observed in certain stocks within automotive companies indicates a negative covariance between the returns of these stocks and the automotive market index. In other words, these stocks tend to move in the opposite direction of the automotive market. Consequently, the inclusion of such stocks in an automotive investment portfolio can mitigate the portfolio's systematic risk. Furthermore, according to Figure 3, stock #21 exhibits the lowest beta risk, while stock #1 demonstrates the lowest semivariance. Conversely, stock #8 presents the highest expected return. The proposed model (10) introduces a new parametric optimization approach, building on DEA. Its input parameters include \(\varphi\) (expected return), \(MSV\) (semivariance risk), and \(\beta\) (market risk). This model uniquely applies DEA to conceptualize risk as a consumed resource (input) and return as a generated product (output), enabling an evaluation of individual assets' relative efficiency in converting risk into return. The utilization of beta risk, in conjunction with semivariance risk, facilitates a more nuanced assessment of an asset's efficiency by incorporating systematic market risk. Assets demonstrating superior return generation for a given level of risk, or conversely, exhibiting lower risk for a given level of return, are identified as relatively more efficient and consequently receive higher efficiency scores, approaching unity. Conversely, assets exhibiting comparatively lower returns relative to their incurred risk, when juxtaposed with other assets within the dataset, are deemed inefficient and are assigned lower efficiency scores.