A reliability model for assessing corporate governance using machine learning techniques

Abstract

Corporate governance assesses the efficiency and effectiveness of companies' operations and decisions to ensure value creation for shareholders and optimal risk taking. As investors' decision making process largely depends on financial information and corporate reports, transparency is capital for the stability of a company, or even the stability of a country via the corporate sector. This research introduces the system reliability theory to properly model the behavior of companies regarding their corporate governance mechanisms. We propose the assessment of the corporate governance framework by mapping its inputs as components (either in operating or failed state) along with firm characteristics to determine an approximate Structure Function that enables alternatively modeling the functioning of the system, quantifying its reliability and detecting critical components. The advantage of the proposed mapping approach is illustrated using a sample of 1,109 U.S. listed companies during the period 2002-2014, reporting financial and non-financial information as components of the corporate governance system and the return on assets as the system output. The proposed approach is also useful for modeling other non-engineering sub-systems; companies, financial markets or even economies would be exposed to significant risk if these systems do not function properly.

Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe

“…In particular, Hernandez−Perdomo et al [10] derive, through an ML method, a reliability structure function to model CSR. The work considers CSR as a complex system including mechanisms and practices of reporting and transparency and performances.…”

“…Furthermore, we consider a combined approach of machine learning and inferential models to investigate ESG metrics. To this end, we contribute to the existing debate [10] with a machine learning approach to predict the accuracy of financial indicators such as the expected Return of Equity (ROE) and Return of Assets (ROA) on several ESG and other economic metrics; and with a logistic regression model to infer on the relationships between ESG factors and ROE and ROA performances of European enterprises.…”

The Influence of CEO Personal Characteristics on the Market Value of Russian Companies

“…Following a similar research pattern, Hernandez-Perdomo et al propose a machine-learning solution for the assessment of a firm's corporate governance, picking the best performing firms as measured by RoA [78].…”

Does relative strength in corporate governance improve corporate performance? Empirical evidence using MCDA approach

“…The first is external corporate governance associated with the market for corporate control (Jensen, 1986;La Porta et al, 2000), for example, law and investor protection. The second is internal corporate governance related to aspects such as board structure, board function, executive ownership and compensation Hernandez-Perdomo et al, 2019;Walsh & Seward, 1990).…”

“…PROMETHEE output is a set of values based on which it is possible to rank alternatives and show the resulting rank. Yet, we ought to note that the motivation and priority of our paper is the general behaviour of the system rather than a single company (see e.g.,Hernandez-Perdomo et al, 2019).…”