Research On Intelligent Decision-Making Methods For Business Financial Distress Early Warning

Financial distress (FD) is the most synthetic and notable business distress. With the stock market mechanism and business bankruptcy law gradually being perfect, enterprises in FD not only suffer great loss, but also their survival and development are directly affected. Besides, when many enterprises run into FD at the same time, it may cause a new financial crisis in capital market. So how to effectively make business FD early warning has already become a research topic worthy of urgent attention.However, past FD early warning research only paid attention to traditional single classifier FD prediction methods, lack of systematism. Not only artificial intelligence single classifier FD preidciton methods should be further extended, but also they ignored the possible benefit of multi-classifier combination for FD prediction and the importance of experts'experiential knowledge and non-financial information for FD early warning. Nowadays, computer science, artificial intelligence, data mining and group decision-making are developing rapidly, which provides new idea for studying FD early warning from a new prospect.Based on financial management theory and business early warning theory, following the newest theories, methods and techniques in the fields of artificial intelligence, data mining and group decision, adopting the inter-subject methodology integrating qualitative and quantitative analysis, normative and empirical research, and expert experience and machine learning, this paper systematically studies the theory and method system for business FD early warning.Starting with anatomizing the phenomena and essence of business FD, this paper puts forward a multi-layer framework for FD early warning, which is consist of financial ratio's real-time monitor, machine learning FD early warning, and FD early warning with experts'experience. For lucubration, it grasps two key clues of machine learning FD early warning based on quantitative financial data and FD early warning with experts'experience based on qualitative financial and non-financial information. Firstly, sample data set and quantitative financial ratios system were constructed. Data of 135 pairs of listed companies'30 financial ratios were collected from Shanghai and Shenzhen stock exchange. After eliminating missing and abnormal data, statistical description and normality test proved that these data sets were suitable for empirical experiments, whose aim was to validate the effectiveness of quantitative FD prediction methods. Then, quantitative financial ratios system for FD prediction was refined through mean comparison, stepwise discriminant analysis and collinearity test.Secondly, FD early warning methods based on artificial intelligence single classifier were studied. FD prediction method based on genetic algorithm dynamically optimizing decision tree (DT) was brought forward. By optimizing the input attributes set of DT, it can improve the generalization ability of FD prediction. Experimental result showed that this method was much better than traditional DT which statically chooses input attributes in advance. The workflow of support vector machine (SVM) FD prediction method was designed to find the optimal classification hyper-plane by maximizing the soft margin. Parameters of SVM model were determined by cross validation and grid search. Experimental result showed that this method had very good synthetic performance in fitting ability, generation ability, and model stability. FD prediction method based on similarity weighted voting CBR was proposed. A hybrid case retrieval on knowledge guided strategy and k nearest neighbor principle was adopted. The principle of maximum similarity weighted voting probability was designed to determine the financial condition class of target case. Empirical experiment not only analyzed the empirical value range of parameters, but also proved that this method was very suitable for short-term FD prediction.Thirdly, FD early warning mehod based on multi-classifier combination was studied. Parallel combination FD prediction method was brought forward. Parallel combination's weighted majority voting model and basic classifier's voting weight model were constructed. Diversity principle and individual optimization principle were adopted to select basic classifiers. Sequential combination FD prediction method guided by apriori class-wise knowledge was proposed. Single best selection operator and whole best selection operator were adopted to choose basic classifiers for sequential combination, and the workflow of sequential combination FD prediction was designed in detail. Hybrid combination FD prediction method was thought out. Parallel structure was used as the basic module of sequential structure, so as to make up the limitation of pure sequential combination, which is easily dominated by certain basic classifier. Contrastive experiment among single classifier FD prediction methods and three combination FD prediction methods indicated that parallel combination FD prediction method got the highest mean accuracy as well as decreased the variation degree, and hybrid combination FD prediction method got the lowest variation degree as well as improved mean accuracy. However, sequential combination FD prediction method was easily dominated by the first basic classifier and did not get evident improvement in prediction performance.Finally, FD possibility evaluation early warning method based on group decision was studied. To remedy the shortcoming of FD early warning which only processes quantitative financial data by means of machine learning, this paper advanced the thought of FD early warning that experts'experience and knowledge should be fully utilized to process financial and non-financial information and evaluate enterprise's FD possibility. Therefore, qualitative evaluation measures system consist of financial and non-financial information was designed, as well as its scoring criterion. Given definition of the new concept of expert's expected negotiation factor, multi-expert negotiation mechanism for weighting qualitative measures was designed in detail. Grey synthetic evaluation method was used to evaluate enterprise's FD possibility, and the grey classes and their whitenization weight functions were designed specifically. Case study validates the effectiveness of this method.Theory and method system of business FD early warning proposed in this paper can greatly enrich the theoretical research fruit in this field. It also can guide and support particular enterprises to implement FD early warning. Therefore, this research is theoretically and practically important.