Systematic and non-systematic risks have influence on decisions in agriculture sector. Evaluating the performance of different crops in terms of returns and risk using appropriate performance indices is important. Such evaluation provides the necessary information to change crop pattern to a pattern that meets the risk conditions of each region and help to reorganize the crop production. Accordingly, this study aims to evaluate the performance of major crops in different provinces of Iran based on Portfolio Performance Evaluation Indicators and then to develop a pattern corresponding to these indicators for different major production regions of the country, using data for 2001 to 2019 period. The results of this study show that there is no proportionality between the high Sharpe ratio and the share of cultivated area in the provinces of the country, i.e. that the largest share of cultivated area in a province does not belong to the crops that have the highest Sharpe ratio. This result indicates that farmers do not act correctly in taking into account the cost of risk in choosing the crops to produce. Accordingly, changes in crop pattern and increasing the knowledge of farmers on the risk consideration is recommended.

Many performance measures, such as the classical Sharpe ratio have difficulty in evaluating the performance of mutual funds whose return distributions are skewed. In this article, we examine the ability of the downside risk and the upside potential ratio (UPR) in evaluating the skewed return distributions. We have used a sample of the active mutual funds in the Tehran Stock Exchange (TSE) for the period of 22- dec - 2010 To 22- dec - 2011. In order to make a fair comparison between the Sharpe ratio and UPR, we assume that MAR in UPR plays the role of the risk-free rate in Sharpe ratio. We constructed a ranking based on both criteria, and we find a very high correlation between the Sharpe ratio and the UPR. This has seen to be the result of normal in the return distributions. Therefore, we prefer to use the UPR as an alternative to the Sharpe ratio, as it gives a more adequate evaluation of the forecasting skills.

‎‎‎Performance measures are essential for evaluating portfolio performance in the risk management and fund industries‎, ‎with the Sharpe ratio being a widely adopted risk-Adjusted metric‎. ‎This ratio compares the excess expected return to its standard deviation‎, ‎enabling investors to assess the returns of risk-taking activities against risk-free options‎. ‎Its popularity stems from its ease of calculation and straightforward interpretation‎. ‎However‎, ‎the actual Sharpe ratio value is often unavailable and must be estimated empirically based on the assumption of normality of asset returns‎. ‎In practice‎, ‎financial assets typically exhibit non-normal distributions and nonlinear dependencies‎, ‎which can compromise the accuracy of the Sharpe ratio estimation when normality is assumed‎. ‎This paper challenges the normality assumption‎, ‎aiming to enhance the accuracy of Sharpe ratio estimates‎. ‎We investigate the impact of dependency on the Sharpe ratio of a two-asset portfolio using copulas‎. ‎Theoretical findings and extensive simulations demonstrate the effectiveness of the proposed copula-based approach to the classic Sharpe ratio‎.

‎‎‎Performance measures are essential for evaluating portfolio performance in the risk management and fund industries‎, ‎with the Sharpe ratio being a widely adopted risk-Adjusted metric‎. ‎This ratio compares the excess expected return to its standard deviation‎, ‎enabling investors to assess the returns of risk-taking activities against risk-free options‎. ‎Its popularity stems from its ease of calculation and straightforward interpretation‎. ‎However‎, ‎the actual Sharpe ratio value is often unavailable and must be estimated empirically based on the assumption of normality of asset returns‎. ‎In practice‎, ‎financial assets typically exhibit non-normal distributions and nonlinear dependencies‎, ‎which can compromise the accuracy of the Sharpe ratio estimation when normality is assumed‎. ‎This paper challenges the normality assumption‎, ‎aiming to enhance the accuracy of Sharpe ratio estimates‎. ‎We investigate the impact of dependency on the Sharpe ratio of a two-asset portfolio using copulas‎. ‎Theoretical findings and extensive simulations demonstrate the effectiveness of the proposed copula-based approach to the classic Sharpe ratio‎.

The Sharpe ratio is one of the performance evaluation criteria of the stock portfolio, which shows the return per unit of risk. This ratio is particularly important for risk-averse investors. In the current research, the hidden Markov model approach introduces a new stock portfolio model called the maximum predictability portfolio of the Sharpe ratio. The hidden Markov model used for each hidden state has a mixture of normals distribution output, which is used to calculate the return and standard deviation to calculate the Sharpe ratio in the investment horizon. The research portfolio calculates the weights of the portfolio in such a way that the Sharpe ratio is maximized in the horizon of the portfolio. The optimal research portfolio was optimized using the historical data of 10 indices from the Tehran Stock Exchange between 2018 and 2018 in a four-member mode space. The evaluation of the performance of the optimal portfolio in the Sharpe ratio criterion shows that the research model has a better performance than the mean-variance model and the equal-weight model.

This paper investigates factors which can discriminate between financial successful companies and unsuccessful companies. Data from all listed companies in TSE for period of 1380-1383 was gathered. Companies which their data had missing value for this period were removediTom the sample.Financial success was measured through using two performance appraisal criteria i.e., the Sharp ratio and Jensen's alpha multiplier. 9 different company specific characteristics which have a significant relationship with company's financial performance according to researches have been done in other countries were used as potential indictors of company financial success.To test the results of research, financial perfonnance of companies was predicted for 1384. In order to quantify and make comparable forecasted performance with actual performance binary logic function was used.The results show that large companies which have a good profitability and no problem with working capital management have better financial performance than rest. In addition results indicate the relatively high power of identified factors in forecasting financial performance of companies. 

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