Devising a Model to Predict Financial Distress Based on the Deep Belief Network Algorithm

高階管理碩士在職專班(EMBA)

105

The use of new artificial intelligent (AI) techniques, such as machine learning (ML) in the accounting domains, have unleashed great potential for researchers to improve accounting information systems (AIS). An automatic AIS reports financial statements from the supporting documents. The basic four financial statements provide information about the results of operations and financial position of an enterprise. As a result, those financial statements could be used to establish a diagnosis model for financial distress prediction (FDP). This study proposes an FDP model based on two ML approaches. Sixteen selected financial variables are calculated from financial statements to establish an FDP model using the deep belief network (DBN) algorithm coupled with the support vector machine (SVM) algorithm. Thrity-two distressed and thirty-two non-distressed companies (as matching samples) companies are selected from the Taiwan Economic Journal (TEJ) database, spanning the 2010-to-2016 sample period, to construct the FDP model. Three latent features of the financial data are extracted from 16 selected ratios by DBN and then divided into validation and training sets for SVM classification model construction. The constructed model is further used for prediction and evaluated by cross-validation. Our empirical results demonstrate that the proposed model could accurately predict the financial distress of a company. When using the previous two consecutive quarters of financial data before any event of distress, the prediction accuracy of the model could reach around 89% with the type I error of 4.7% and the type II error of 6.2%.