Investigating the Power of Explaining Spectral, Coherent, Deviation and Artificial Neural Networks risk criterias and Their Application in Selecting the Optimal Investment Basket in Tehran Stock Exchange
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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
The purpose of the present study was to compare the efficiency of neural network models and available criteria in risk criteria in optimal portfolio formation. At first, time series data related to the return rate of different companies were collected from the databases of the Securities and Exchange Organization of Iran since 2008-2017, and analyzed in the framework of neural network models and Spectral, Deviation and Coherent risk measures by using Kupiec, Christofferssen and Lopez test. The results of the Kupiec and Christofferssen test for neural network models and CVaR, showed that the LR test statistic for the whole group of investigated companies was larger than the critical value. Based on this, it was concluded that the performance of neural network models and CVaR criteria can be acceptable for the entire group of companies at a significant level of 5% and the LR test statistic is lower than the critical value for SE criteria for the whole group of investigated companies. This suggests that SE's performance is not acceptable to all companies at a significant level of 5%. On the other hand, based on the results of Kupiec and Christofferssen test for VaR, the LR test statistic for the six groups of companies was larger and for the four was smaller than the critical value. Therefore, we can say that in artificial neural network models, the average number of violations or the state of exception in the level of 5% were less than the spectral, deviation and coherent risk measures.
Language:
Persian
Published:
Journal of Investment Knowledge, Volume:8 Issue: 30, 2019
Pages:
287 to 312
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