Dynamic prediction of financial distress using Malmquist DEA

Abstract

Creditors such as banks frequently use expert systems to support their decisions when issuing loans and credit assessment has been an important area of application of machine learning techniques for decades. In practice, banks are often required to provide the rationale behind their decisions in addition to being able to predict the performance of companies when assessing corporate applicants for loans. One solution is to use Data Envelopment Analysis (DEA) to evaluate multiple decision-making units (DMUs or companies) which are ranked according to the best practice in their industrial sector. A linear programming algorithm is employed to calculate corporate efficiency as a measure to distinguis h healthy companies from those in financial distress. This paper extends the cross-sectional DEA models to time-varying Malmquist DEA, since dynamic predictive models allow one to incorporate changes over time. This decision-support system can adjust the efficiency frontier intelligently over time and make robust predictions. Results based on a sample of 742 Chinese listed companies observed over 10 years suggest that Malmquist DEA offers insights into the competitive position of a company in addition to accurate financial distress predictions based on the DEA efficiency measures.

An Empirical Study on the Efficiency and Influencing Factors of the Photovoltaic Industry in China and an Analysis of Its Influencing Factors

“…To measure the industrial efficiency, it is necessary to clarify the input and output factors [53]. When Li [54], Yu [55] and Imanirad [56] studied industrial efficiency through DEA analysis, they mainly selected input and output indicators from the three perspectives of personnel, finance and material. Similarly, this paper selects the number of employees, fixed assets, and operating costs as three major input indicators, and operating income as an output indicator to measure the efficiency of the PV device industry.…”

Modeling of the enterprise functioning stability using the automatic control theory apparatus

“…Finitization is done by replacing the argument ω=t/(1-t), tÎ [0;1] in the characteristic equation (5). The resulting characteristic polynomial has the form: …”

“…However, the issues related to the construction of the model complex were highlighted fragmentarily. In a number of sources, applied models of wind power plants stability assessment are considered, which allow forming a system of stability diagnostic indicators on the basis of signs complete and incomplete reduction methods [3,4], carrying out classification of IES stability states on the basis of cluster analysis methods [3], DEA methods [5,6], identifying the class of IES stability on the basis of classification trees [3], models of multiple choice [4], discriminant analysis [5]. It should be noted that along with the undoubted advantages, the above works do not adequately cover the issues of the comparative analysis of application effectiveness of various simulation methods.…”

Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach

“…Within benchmarking, Data Envelopment Analysis (DEA) [9] is one of the most widely used tools, [10,12,18,20,23,28,33,34]. It aims at benchmarking the performance of decision marking units (DMUs), which use the same types of inputs and produce the same types of outputs, against each other.…”