Forecasting Models/ Time Series
Sepideh Etemadi; Mehdi Khashei
Abstract
Purpose: The purpose of this paper is to present a new methodology for statistical modeling, which, unlike all commonly developed models and algorithms, maximizes the reliability of the results instead of the resulting accuracy. Accordingly, a new class of statistical modeling approaches has been developed ...
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Purpose: The purpose of this paper is to present a new methodology for statistical modeling, which, unlike all commonly developed models and algorithms, maximizes the reliability of the results instead of the resulting accuracy. Accordingly, a new class of statistical modeling approaches has been developed by replacing conventional processes with the proposed process.Methodology: The multiple linear regression method has been selected to implement the proposed methodology in this paper. To comprehensively evaluate the performance of the proposed regression model, 10 standard datasets from the literature on statistical modeling have been considered.Findings: Overall, the results show that in 65% of the studied data sets, the proposed model can generalize more than the usual multiple linear regression. The proposed regression model, on average, has been able to improve the accuracy of the modeling by 5.571% and 6.466% in mean absolute error and mean square error, respectively, compared to its classic version. These results clearly show the significant effect of reliability of the results on the degree of generalizability, which is basically not considered in the usual statistical modeling processes.Originality/Value: Statistical modeling is one of the most important tools for simulating real-world systems and data sets that are often used to make decisions in a wide range of applications. Several different approaches have been developed in the literature with different features to cover real-world issues with the desired accuracy. However, such methods follow a similar concept and idea in the modeling process. The performance basis in all conventional statistical modeling approaches is based on the assumption that maximum accuracy in experimental and inaccessible data will be obtained from models with minimization of error in training data. Although this is a logical and standard procedure in traditional statistical modeling spaces, it is not the unique way to achieve maximum generalizability. In other words, the generalizability of the model simultaneously depends on the model's accuracy and the level of results' reliability. In this paper, a new methodology for statistical modeling is presented, which, unlike all commonly developed models and algorithms, maximizes the reliability of the results instead of the resulting accuracy.
Decisions in new businesses
Seyed Fakhreddin Fakhrehosseini; Omid Aghaei Meybodi
Abstract
The present paper presents the possibility of predicting firms' bankruptcy with Sprint, Altman, Fulmer, Zmijewski and Mckee Genetic models among companies in the Tehran Stock Exchange in a different way from previous research to introduce companies which have the potential for higher bankruptcy with ...
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The present paper presents the possibility of predicting firms' bankruptcy with Sprint, Altman, Fulmer, Zmijewski and Mckee Genetic models among companies in the Tehran Stock Exchange in a different way from previous research to introduce companies which have the potential for higher bankruptcy with a comparative approach among the models. To achieve this goal, 75 companies that are selected not covered base on 141 of the Commercial law. Required data for the 10 years (86-95) has been compiled. According to the results in each of the above models, some companies were identified as high-risk probability companies, and then companies that were identified as most likely to be bankrupt in most of these models. The results also show that, with the exception of Mckee model, in four other models, three companies with high bankruptcy probability were included. Among these four models, Zmijewski model has a higher coefficient of determination, hence we can say that relative to Other models have been more accurately predicted for bankruptcy and have a significant role in corporate bankruptcy among financial ratios, debt ratios, asset turnover, and asset returns.
Financial modeling
Meisam Kaviani
Abstract
The present research is aimed at predicting the beta coefficient (systematic risk) prediction dynamics within the framework of two macroeconomic structural models, the model in the context of dynamic stochastic general equilibrium (DSGE) and Panel Vector Autoregressive (PVAR) with the inclusion of financial ...
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The present research is aimed at predicting the beta coefficient (systematic risk) prediction dynamics within the framework of two macroeconomic structural models, the model in the context of dynamic stochastic general equilibrium (DSGE) and Panel Vector Autoregressive (PVAR) with the inclusion of financial data of companies and Some of the facts observed in the Iranian economy during the 15-year period (2002-2016). The results of the research show that economic shocks affect the beta coefficient of the stock. Also, in three approaches to predict stock beta coefficient, the VAR model has a lower error than the DSGE model. Finally, by comparing the moments of the present variables in the DSGE model and the real data of Iran's real moments, it shows the relative success of this model in the realities of Iran's economy.