A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

Rethinking Economic Capital Management through the Integrated Derivative-based Treatment of Interest Rate and Credit Risk

This research revisits the economic capital management regarding banking books of financial institutions exposed to the emerging market sovereign debt. We develop a derivative-based integrated approach to quantify economic capital requirements for considered jointly interest rate and credit risk. Our framework represents a major contribution to the empirical aspects of capital management. The proposed innovative modeling allows applying standard historic value-at-risk techniques developed for stand-alone risk factors to evaluate aggregate impacts of several risks. We use the time-series of credit default swap spreads and interest rate swap rates as proxy measures for credit risk and interest rate risk, respectively. An elasticity of interest rate risk and credit risk, considered a function of the business cycle phases, maturity of instruments, creditworthiness, and other macroeconomic parameters, is gauged by means of numerical modeling. Our contribution to the new economic thinking regarding the interest rate risk and credit rate risk management consists in their integrated treatment as the dynamics of interest rate and credit spreads is found to demonstrate the features of automatic stabilizers of each other. This research sheds light on how financial institutions may address hedge strategies against downside risks. It is of special importance for emerging markets heavily dependent on foreign capital as it potentially allows emerging market banks to improve risk management practices in terms of capital adequacy and Basel III rules. From the regulatory perspective, by taking into account inter-risk diversification effects it allows enhancing financial stability through jointly optimizing Pillar 1 and Pillar 2 economic capital.info:eu-repo/semantics/publishedVersio

Six papers on computational methods for the analysis of structured and unstructured data in the economic domain

This work investigates the application of computational methods for structured and unstructured data. The domains of application are two closely connected fields with the common goal of promoting the stability of the financial system: systemic risk and bank supervision. The work explores different families of models and applies them to different tasks: graphical Gaussian network models to address bank interconnectivity, topic models to monitor bank news and deep learning for text classification. New applications and variants of these models are investigated posing a particular attention on the combined use of textual and structured data. In the penultimate chapter is introduced a sentiment polarity classification tool in Italian, based on deep learning, to simplify future researches relying on sentiment analysis. The different models have proven useful for leveraging numerical (structured) and textual (unstructured) data. Graphical Gaussian Models and Topic models have been adopted for inspection and descriptive tasks while deep learning has been applied more for predictive (classification) problems. Overall, the integration of textual (unstructured) and numerical (structured) information has proven useful for systemic risk and bank supervision related analysis. The integration of textual data with numerical data in fact, has brought either to higher predictive performances or enhanced capability of explaining phenomena and correlating them to other events.This work investigates the application of computational methods for structured and unstructured data. The domains of application are two closely connected fields with the common goal of promoting the stability of the financial system: systemic risk and bank supervision. The work explores different families of models and applies them to different tasks: graphical Gaussian network models to address bank interconnectivity, topic models to monitor bank news and deep learning for text classification. New applications and variants of these models are investigated posing a particular attention on the combined use of textual and structured data. In the penultimate chapter is introduced a sentiment polarity classification tool in Italian, based on deep learning, to simplify future researches relying on sentiment analysis. The different models have proven useful for leveraging numerical (structured) and textual (unstructured) data. Graphical Gaussian Models and Topic models have been adopted for inspection and descriptive tasks while deep learning has been applied more for predictive (classification) problems. Overall, the integration of textual (unstructured) and numerical (structured) information has proven useful for systemic risk and bank supervision related analysis. The integration of textual data with numerical data in fact, has brought either to higher predictive performances or enhanced capability of explaining phenomena and correlating them to other events

Concepts of work wellbeing: A multidisciplinary approach to theory and method

The focus of this thesis is on work wellbeing. The interface and tensions between organisational psychology researchers and practitioners are explored through the lens of work wellbeing. Prevailing disciplinary values favour a form of natural or experimental science over interpretivist science. (Experimental science is a general term used throughout the thesis to include experiments, quasi experiments, and quantitative surveys.) The study proposed additions to the theoretical and methodological repertoire to facilitate the applicability of research in work settings

Financial market regulation in the wake of financial crises: the historical experience

The focus of the present volume - which originates from a workshop held at the Bank of Italy on 16 and 17 April 2009 - is the regulatory response given to financial crises in the past, across countries. Alongside the scholarly interest of such a review its aim is also to offer some insights that may be useful in re-designing regulation in the present time of distress. Financial crises have been examined under many perspectives, including that of regulatory failures. The studies assembled in this volume, which touch on a significant array of countries, can be viewed as part of a historical survey on this issue. The basic question is whether regulatory responses form a pattern, and more specifically, whether they tend to be biased with respect to an optimum, however defined. In the end, rather than finding one pattern of response, we were able to identify the "disturbances" which most often enter the post-crisis decisional process. The awareness of such factors, and some knowledge of their functioning, are instrumental in understanding (for academics) and in governing (for policy makers) the response to major financial crises.Financial crises, financial regulation, economic history

Artificial Intelligence in Asset Management

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways