Study On Analysis And Forecasting Methods Of Financial Distress In The Enterprises

Financial distress prediction is an important research direction of financial management and investment management, since whether the enterprise will be in financial difficulties or not is not only related to the business strategy formulation and adjustment of its own but also related to the interests of investors and creditors. The purpose of this study is to put forward a method that can be widely used in financial distress prediction and suitable for China's listed companies, with no restrictions of firm size, no limitations of industry, and no restrictions of ownership structure. Thus, it can reveal which companies will get into financial difficulties to the regulatory authorities and investors so that they may be alerted, and then maintain market stability and provide scientific information for decision making.Financial distress prediction has been a breakthrough since Altman carried out pioneering research on it. In recent years, many scholars in this field have done a lot of useful work .However, generally speaking , the current study is still lack of systematic theoretical guidance, especially in how to reduce the number of training samples, how to shorten the run-time of models, and how to optimize model and the nuclear parameters under the premise of improving the model prediction accuracy. Plus, the pre-existing achievments are very few, and some of them are still in start-up and exploratory stage.This article applies genetic algorithms theory and support vector machine to corporate financial distress prediction, and does a bold attempt to ameliorate spport vctor mchine algorithms and model parameters . It also deeply analyzes and studies in aspects of improving the prediction accuracy of models, reducing the number of training samples, and shortening running time of models, etc. To conclude, the main work and innovations are as follows:Firstly, based on the definitions of the concept of financial distress both at home and abroad,this paper puts forward a definition of the concept of financial distress according to China's actual situation.Secondly, by statistical analysis of study samples, the paper explicitly states the different characteristics of ailing companies and normal companies in varied timepoints before ST in three aspects: financial reports items, financial indexes and non-financial indexes. According to results of significant difference tests and tendency charts of mean changes of ST and normal companies'reports data and financial index data, the paper dissects index data which lead to companies'financial distress from the statistics point of view, looking for the"Warning Resources". Finally, the paper makes in-depth analysis of internal and external factors which result in corporate finacial distress and introduces the process of corporate financial distress prediction and forecasting methodological framework.Secondly, this article proposes that short-term and long-term financial distress predictions of corporate should use different indicator systems. After carrying out normal tests, significant difference tests and the treatment of factor analysis on two study samples's indicator data of ST and normal companies separately , it shows that indicators which have a significant influences on short-term forecasts is much more, while indicators which have a significant effects on long-term forecasts reduce obviously. Because of the reduction of indicators which have a significant effects on long-term forecasts, the information which forecasting models can use reduces, and then compared to short-term forecasts the accuracy of forecast long-term forecast accuracy has declined markedly. In addition, it uses non-financial indicators for the first time in the aspect of indicators selection, and concludes that the two non-financial indicators—the geographical environment and the capital structure—affect both short- time forecasts and long-term forecasts significantly .Thirdly, this article provides a growth memory algorithm of least squares support vector machine which is based on the Renyi-entropy. Considering that the solving process of the dual problem of the traditional support vector machine is equivalent to solving a linear constrained quadratic programming problem, the inverse matrix calculation and storage of nuclear function matrix require more memory spaces, and at the same time quadratic optimization algorithm also requires more running time, this article therefore derives independently a growth memory algorithm of least squares support vector machine which is suitable for enterprise financial distress prediction of discrete sequences in order to avoid solving the inverse matrix. Meanwhile, it introduces the information entropy to growth memory algorithm model for the first time. Empirical results show that growth memory algorithm of least squares support vector machine does indeed save the running time of program, while the introduction of information entropy not only reduces the number of training samples but also improves the model prediction accuracy.Fourthly, considering the serious shortcomings that support vector machine algorithm and its improvement by artificial means alone will not have access to model parameters and the nuclear parameters of the optimal solution, this article introduces genetic algorithm parameter optimization technology which is based on bio-genetic mechanisms to corporate financial distress prediction. Empirical studies confirm that genetic algorithms can indeed optimize automatically in a wider range, which can significantly improve the model prediction accuracy. In particular, by applying genetic algorithm to growth memory algorithm of least squares support vector machine which is based on the Renyi-entropy , it is also able to obtain a higher prediction accuracy in the case of a small number of training samples.Fifthly, this article takes horizontal and vertical comparison on many forecast mode of short-term and long-term prediction with the support vector machine and its improved algorithm as a tool. Longitudinal comparison shows that the shorter the forecast ahead period is, the higher the forecasting accuracy will be, and with the increase of forecast ahead time, forecast accuracy drops significantly; horizontal comparison shows that prediction accuracy of support vector machine and its improved model are better than the traditional forecasting models, the probability of making mistakes of first classⅠ,Ⅱis obviously lower than the traditional model, which further confirms that the support vector machine has a good fitting capability as well as a good prediction capability. The empirical results also show that after using Gaussian kernel function, its effect of model prediction is better than the polynomial kernel. But the kernel function of the Renyi entropy can only use polynomial kernel, Gaussian kernel is not suitable to be the kernel function of Renyi entropy .This is different from other application areas, resulting from the special nature of financial distress prediction, which is also a major contribution of this study.