Forecasting Models/ Time Series
Adel Gardoon; Nader Khedri; Ali Mahmoodi; Mehdi Basert
Abstract
Purpose: Financial statements are the main decision-making bases of capital market actors; which is affected by internal and external factors. Uncertainty in other markets is one of the most important factors affecting the financial statements of listed companies. As a result, the aim of the current ...
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Purpose: Financial statements are the main decision-making bases of capital market actors; which is affected by internal and external factors. Uncertainty in other markets is one of the most important factors affecting the financial statements of listed companies. As a result, the aim of the current research is to model the spillover of uncertainties of parallel markets on the types of profit management.Methodology: The present research is practical. The research period is a 10-year period with seasonal data between 2011 and 2021. VAR-MGARCH model has been used to investigate the spillover of uncertainties of parallel markets on the types of profit management.Findings: Based on the results of VECH, CCC, BEKK and DCC models to extract the uncertainty of the studied variables; VECH models had higher accuracy. Based on the results of multivariate GARCH models, the spillover effect between different markets was observed. As a result, the uncertainty of one market strengthens the uncertainty between other markets. Based on the results of vector autoregression model; Uncertainties of variables have a stronger effect on accrual profit management than actual profit management. The results of variance analysis show the fact that oil price uncertainty has the highest contribution in the interpretation of real profit management change and exchange rate uncertainty in the interpretation of accrual profit management change.Originality/Value: Uncertainties of parallel markets reinforce each other and increase the level of profit management in the investigated companies.
Forecasting Models/ Time Series
Shirin Shoaee; Mohammad Mehdi Gholi Keshmarzi
Abstract
Purpose: Mortality is a dynamic process that completes over time and is a fundamental issue in life insurance, pension fund, health insurance, and in general any issue related to financial planning that deals with the longevity of individuals. Therefore, the accuracy of mathematical models in predicting ...
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Purpose: Mortality is a dynamic process that completes over time and is a fundamental issue in life insurance, pension fund, health insurance, and in general any issue related to financial planning that deals with the longevity of individuals. Therefore, the accuracy of mathematical models in predicting mortality rates is an important challenge. The purpose of this study is to generalize static stochastic mortality models to dynamic stochastic mortality models and to predict mortality rates based on the generalization of stochastic mortality models by the Cox-Ingersoll-Ross (CIR) process and to compare the results with each other.Methodology: In this research, two suggestions are presented: the first idea is to provide a dynamic correction method to increase the prediction accuracy using the CIR process and the second idea is to examine the out-of-sample validation method.Findings: In this study, using the out-of-sample validation method, the force of mortality from the best models selected from the two famous mortality model families (Lee-Carter and Cairns, Blake and Dowd (CBD)) is compared with the results of the generalized model. After estimating the parameters of the studied models and calculating the prediction of the mortality rates, by calculating the mean absolute error and root mean squares error of prediction, it is determined that the generalization of stochastic mortality models by the CIR process performs much better than static mortality models. The Bayesian information criterion also indicates that the use of generalized stochastic mortality models is justified.Originality/Value: In this study, stochastic mortality index models, which include Lee-Carter and Cairns-Blake-Dowd family models, are used and generalized by the CIR process. In this regard, Human Mortality Database (HMD) data is used. But there is no information about our country in this database. Because the French mortality pattern is very close to the Iranian pattern and the life tables of this country (TD 88-90) are used in Iranian insurance applications, the crude death rate of French men in the years 1900-2018 on the ages of 18, 40 and 65 years is used. Using these data and the backtesting method, static mortality models and generalized models with the CIR process are compared.
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.
Forecasting Models/ Time Series
Reza Raei; Saeed Bajalan; Zahra Saedi
Abstract
Purpose: In this research, the effect of scale-time volatility of assets (currency, stocks and housing) on the efficiency of the banking network in the period 1399: 4-1388: 1 has been studied quarterly using the Markov switching model.Methodology: In this study, we first calculate the efficiency of the ...
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Purpose: In this research, the effect of scale-time volatility of assets (currency, stocks and housing) on the efficiency of the banking network in the period 1399: 4-1388: 1 has been studied quarterly using the Markov switching model.Methodology: In this study, we first calculate the efficiency of the bank network using the data envelopment analysis model with bootstrap data. Then, the volatility of asset market (exchange rate, stock market index and housing price index) extracted using the wavelet conversion pattern and examines the impact of volatility of asset market on the efficiency of the country's banking network in the form of the Markov switching model and observing their effect on different levels of efficiency.Findings: The average efficiency of the country's banking network in the study period has been about 56.1%, which indicates that efficiency has not been appropriate. The short-term volatility of the exchange rate in the state that the efficiency of the bank network and the high regime has a negative and significant effect, but if the long-term exchange volatility, regardless of the regime and the level of banking network efficiency, has a negative and significant effect. The short-term volatility of the stock market index have had a positive and significant effect on the level of low banking network efficiency. But if volatility are continued in the stock market, regardless of the level and regime, the efficiency of the banking network has a negative and significant effect. The short-term volatility in the housing market have had a positive and significant effect on the level of bank network efficiency but in the opposite side of the long-term volatility in this market and in a high level of bank network efficiency, it can lead to significant reductions. Therefore, by stabilizing the economy (lack of large exchange rate, stock index and housing), it can be expected to improve the efficiency of the country's banking network due to its level and regime.Originality/Value: One of the issues that can be important in policy making perspective is to consider the impact of volatility of assets market in different time periods on different levels of banking network efficiency. Because they may have a different impact on different levels of bank network efficiency as well as different periods of volatility of assets market.
Forecasting Models/ Time Series
davood darvishi; Mostafa Nori joybari; parvin babaei
Abstract
Purpose: Covid-19 virus is a major threat to the health and safety of people around the world. One of the key components in dealing with this global threat is rapid and timely decision-making to control the epidemic of the disease, so predicting the future trend of this disease in the world, including ...
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Purpose: Covid-19 virus is a major threat to the health and safety of people around the world. One of the key components in dealing with this global threat is rapid and timely decision-making to control the epidemic of the disease, so predicting the future trend of this disease in the world, including predicting deaths, can be useful for policy-making, management and control of its prevalence. Therefore, the mortality rate caused by this virus has been predicted with grey models in the world.Methodology: This study examines the process of predicting mortality rates in the world using the theory of grey systems models. Research data were collected from the World Health Organization website and predicted the number of deaths in the world on a monthly basis by five methods: GM (1, 1), Verhulst Grey, DGM (1, 1), NGBM (1, 1) and FNGBM(1, 1). In order to evaluate the error of the models, the common error evaluation criteria MAE, RMSE and MAPE were used.Findings: By evaluating the model error, the prediction of the F-NGBM model (1, 1) in the category of excellent models, the prediction values of the GreyVerhulst model are in the category of acceptable predictions and the rest of the models are in the category of good predictions. Also, the F-NGBM (1, 1) model with MAE, RMSE and MAPE error values of 26989.54, 21533.94 and 7.21, respectively, is the most suitable model compared to the other methods. An estimated 250,958 deaths are estimated by the F-NGBM (1.1) model by the end of 2021, which may be the most appropriate value among forecasting methods.Originality/Value: Due to the lack of historical data and also a lot of uncertainty in the available data, it is necessary to use approaches to dealing with uncertainty such as the grey system theory in predicting the mortality rate of this disease. Various grey predictions estimate the mortality rate, which requires relatively less data than existing methods, and the model error is much lower. The study also looked at the worldwide mortality rate and will be more comprehensive on integrated global action.
Forecasting Models/ Time Series
Moeen Sammak Jalali; Seyed Mohammad Taghi Fatemi Ghomi
Abstract
Among various applications of time series, applying this concept in production industries with the intention of pre-detecting failure times of machines and implementing maintenance tasks, is considered as one of the most valuable activities in the automotive industries. With this regard, this article ...
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Among various applications of time series, applying this concept in production industries with the intention of pre-detecting failure times of machines and implementing maintenance tasks, is considered as one of the most valuable activities in the automotive industries. With this regard, this article embarks on applying time series analysis as well as quality assurance concepts to detect failure times and implement proper actions. With this regard, we will analyze the data derived from maintenance department of the company. Then we determine influential factors on parts failure by means of quality assurance concepts.