Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

Abstract

Accurate forecasts of corporate failure in the Chinese energy sector are drivers for both operational excellence in the national energy systems and sustainable investment of the energy sector. This paper proposes a novel integrated model (NIM) for corporate failure forecasting in the Chinese energy sector by considering textual data and numerical data simultaneously. Given the feature of textual data and numerical data, convolutional neural network oriented deep learning (CNN-DL) and support vector machine (SVM) are employed as the base classifiers to forecast using textual data and numerical data, respectively. Subsequently, soft set (SS) theory is applied to integrate outputs of CNN-DL and SVM. Hence, NIM inherits advantages and avoids disadvantages of CNN-DL, SVM, and SS. It is able to improve the forecasting performance by taking full use of textual data and numerical data. For verification, NIM is applied to the real data of Chinese listed energy firms. Empirical results indicate that, compared with benchmarks, NIM demonstrates superior performance of corporate failure forecasting in the Chinese energy sector.

Assessment of a failure prediction model in the European energy sector: A multicriteria discrimination approach with a PROMETHEE based classification

“…This section deals with one of the initial steps in the development of a failure prediction model: the selection of the most predictive variables. It consists of the review of the distress prediction literature with special attention to the most predictive variables employed in the energy sector (see for a literature review of FRs on failure prediction models: Xu et al, 2019;Liang et al, 2016;Du Jardin, 2016). Table 2 shows the set of 42 FRs derived from the literature review, which are classified according to six firms' dimensions, together with their acronyms and definitions.…”

Evaluating the Sustainability of Microfinance Institutions Considering Macro-Environmental Factors: A Cross-Country Study

“…In the traditional TOPSIS, the weight of each criterion is determined by the expert system method [54]. The expert system method relies heavily on expert knowledge and the ability to be widely employed [44,55]. To overcome the disadvantages of traditional TOPSIS, we implement a new TOPSIS approach by integrating soft set theory and traditional TOPSIS.…”

A comprehensive review of artificial intelligence and machine learning applications in energy consumption and production

“…The subject matter was addressed within the framework employed to simulate the energy consumption of residential and commercial buildings. Furthermore, Xu et al (2019) argued that precise forecasts of corporate bankruptcy within the Chinese energy industry have a dual role in stimulating ongoing enhancements in state power generation and promoting sustainable investments in the energy sector. The findings were deliberated with stakeholders from the Chinese energy industry.…”

“…Moreover, Xu et al (2019) introduced a new integrated model (NIM) for predicting business failure in the Chinese energy industry, which incorporates both textual and numerical data. Based on the findings of Xu et al (2019), it can be inferred that the utilization of AI and ML holds significant potential in the energy segment, specifically in emerging economies, for energy production and consumption. Energy has a pivotal role in global economic and social growth (Raihan & Tuspekova, 2022b;.…”