The paper＇s aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data （i...The paper＇s aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data （i.e., secular trends, cyclical variations, seasonal effects, and stochastic variations）, they believe the best forecasting model is the one which realistically considers the underlying causal factors in a situational relationship and therefore has the best ＂track records＂ in generating data. Paper＇s models can be adjusted for variations in related a time series which processes a great deal of randomness, to improve the accuracy of the financial forecasts. Because of Na＇fve forecasting models are based on an extrapolation of past values for future. These models may be adjusted for seasonal, secular, and cyclical trends in related data. When a data series processes a great deal of randomness, smoothing techniques, such as moving averages and exponential smoothing, may improve the accuracy of the financial forecasts. But neither Na＇fve models nor smoothing techniques are capable of identifying major future changes in the direction of a situational data series. Hereby, nonlinear techniques, like direct and sequential search approaches, overcome those shortcomings can be used. The methodology which we have used is based on inferential analysis. To build the models to identify the major future changes in the direction of a situational data series, a comparative model building is applied. Hereby, the paper suggests using some of the nonlinear techniques, like direct and sequential search approaches, to reduce the technical shortcomings. The final result of the paper is to manipulate, to prepare, and to integrate heuristic non-linear searching methods to serve calculating adjusted factors to produce the best forecast data.展开更多
The concept of crisis evolution is still not fully understood, despite over 40 years of research into investigations in the field of crisis and insolvency prediction. This is due to the fact that the financial situati...The concept of crisis evolution is still not fully understood, despite over 40 years of research into investigations in the field of crisis and insolvency prediction. This is due to the fact that the financial situation of a firm changes within an unobservable life cycle continuum, comprising different economic states which are not in fact properly defined. The aim of this study was to contribute towards a better understanding of the differences between solvent and insolvent finns for the periods of one and two years prior to insolvency respectively. Through the application of correlation and factor analysis, an attempt was made to detect behavioral pattems in accounting ratios, which can in turn explain differences and similarities between the two groups of finns. The results of this study show that although accounting ratios from two consecutive years had low correlations for both groups of finns, they were much higher for insolvent firms. This provides evidence that the economic and financial situation of insolvent firms is much more dependent on its history when compared to solvent firms. Moreover, there is evidence to suggest that the change of the economic and fmancial situation of insolvent firms within the life cycle continuum tends to follow a predetermined path, in contrast to the more random nature of a solvent firm＇s behavior. Additionally, the results showed that the factor loadings for solvent and insolvent finns differ for both observation periods, indicating that there are different underlying factors affecting the final outcomes for the two groups of firms. This is mainly attributable to disturbances in the scaling factors of total assets for both observation periods, as well as the disappearing size factor for the pre-distress year for insolvent firms, based on factor analysis.展开更多
文摘The paper＇s aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data （i.e., secular trends, cyclical variations, seasonal effects, and stochastic variations）, they believe the best forecasting model is the one which realistically considers the underlying causal factors in a situational relationship and therefore has the best ＂track records＂ in generating data. Paper＇s models can be adjusted for variations in related a time series which processes a great deal of randomness, to improve the accuracy of the financial forecasts. Because of Na＇fve forecasting models are based on an extrapolation of past values for future. These models may be adjusted for seasonal, secular, and cyclical trends in related data. When a data series processes a great deal of randomness, smoothing techniques, such as moving averages and exponential smoothing, may improve the accuracy of the financial forecasts. But neither Na＇fve models nor smoothing techniques are capable of identifying major future changes in the direction of a situational data series. Hereby, nonlinear techniques, like direct and sequential search approaches, overcome those shortcomings can be used. The methodology which we have used is based on inferential analysis. To build the models to identify the major future changes in the direction of a situational data series, a comparative model building is applied. Hereby, the paper suggests using some of the nonlinear techniques, like direct and sequential search approaches, to reduce the technical shortcomings. The final result of the paper is to manipulate, to prepare, and to integrate heuristic non-linear searching methods to serve calculating adjusted factors to produce the best forecast data.
文摘The concept of crisis evolution is still not fully understood, despite over 40 years of research into investigations in the field of crisis and insolvency prediction. This is due to the fact that the financial situation of a firm changes within an unobservable life cycle continuum, comprising different economic states which are not in fact properly defined. The aim of this study was to contribute towards a better understanding of the differences between solvent and insolvent finns for the periods of one and two years prior to insolvency respectively. Through the application of correlation and factor analysis, an attempt was made to detect behavioral pattems in accounting ratios, which can in turn explain differences and similarities between the two groups of finns. The results of this study show that although accounting ratios from two consecutive years had low correlations for both groups of finns, they were much higher for insolvent firms. This provides evidence that the economic and financial situation of insolvent firms is much more dependent on its history when compared to solvent firms. Moreover, there is evidence to suggest that the change of the economic and fmancial situation of insolvent firms within the life cycle continuum tends to follow a predetermined path, in contrast to the more random nature of a solvent firm＇s behavior. Additionally, the results showed that the factor loadings for solvent and insolvent finns differ for both observation periods, indicating that there are different underlying factors affecting the final outcomes for the two groups of firms. This is mainly attributable to disturbances in the scaling factors of total assets for both observation periods, as well as the disappearing size factor for the pre-distress year for insolvent firms, based on factor analysis.
