Forecasting Financial Distress With Machine Learning – A Review

Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical & Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic

Revisión de las limitaciones de la investigación sobre predicción de quiebras financieras

The objective of this paper is to critically evaluate the main weaknesses associated with the limitations of financial failure prediction research studies. For more than 80 years, researchers have unsuccessfully studied ways to create a general theory of financial failure, which is useful for prediction. In this paper, we review the main boundaries of failure prediction research through a critical evaluation of previous papers and our own approach from the research experience. Our findings corroborate that these studies suffer from a lack of theoretical and dynamic research, an unclear definition of failure, deficiencies with the quality of financial statement data and a shortfall in the diagnostic analyses of failure. The most relevant implications for future research in this area are also outlined. This is the first study to analyse in deep the caveats of financial failure prediction studies, a crucial topic nowadays due to the hints of an economic crisis caused by the Covid-19 pandemic.El objetivo de este artículo es evaluar críticamente los principales puntos débiles asociados a las limitaciones de los estudios de investigación sobre predicción de quiebras financieras. Durante más de 80 años, los investigadores han estudiado sin éxito la forma de crear una teoría general del fracaso financiero que sea útil para la predicción. En este artículo, revisamos los principales límites de la investigación sobre predicción de quiebras mediante una evaluación crítica de trabajos anteriores y nuestro propio enfoque a partir de la experiencia investigadora. Nuestras conclusiones corroboran que estos estudios adolecen de una falta de investigación teórica y dinámica, una definición poco clara del fracaso, deficiencias con la calidad de los datos de los estados financieros y un déficit en los análisis de diagnóstico del fracaso. También se esbozan las implicaciones más relevantes para futuras investigaciones en este ámbito. Se trata del primer estudio que analiza en profundidad las salvedades de los estudios de predicción de la quiebra financiera, un tema crucial en la actualidad debido a los atisbos de crisis económica provocados por la pandemia del Covid-19

Revisión de las limitaciones de la investigación sobre predicción de quiebras financieras

The objective of this paper is to critically evaluate the main weaknesses associated with the limitations of financial failure prediction research studies. For more than 80 years, researchers have unsuccessfully studied ways to create a general theory of financial failure, which is useful for prediction. In this paper, were view the main boundaries of failure prediction research through a critical evaluation of previous papers and our own approach from the research experience. Our findings corroborate that these studies suffer from a lack of theoretical and dynamic research, an unclear definition of failure, deficiencies with the quality of financial statement data and a shortfall in the diagnostic analyses of failure. The most relevant implications for future research in this area are also outlined. This is the first study to analyse in deep the caveats of financial failure prediction studies, a crucial topic nowadays due to the hints of an economic crisis caused by the Covid-19 pandemic.El objetivo de este artículo es evaluar críticamente los principales puntos débiles asociados a las limitaciones de los estudios de investigación sobre predicción de quiebras financieras. Durante más de 80 años, los investigadores han estudiado sin éxito la forma de crear una teoría general del fracaso financiero que sea útil para la predicción. En este artículo, revisamos los principales límites de la investigación sobre predicción de quiebras mediante una evaluación crítica de trabajos anteriores y nuestro propio enfoque a partir de la experiencia investigadora. Nuestras conclusiones corroboran que estos estudios adolecen de una falta de investigación teórica y dinámica, una definición poco clara del fracaso, deficiencias con la calidad de los datos de los estados financieros y un déficit en los análisis de diagnóstico del fracaso. También se esbozan las implicaciones más relevantes para futuras investigaciones en este ámbito. Se trata del primer estudio que analiza en profundidad las salvedades de los estudios de predicción de la quiebra financiera, un tema crucial en la actualidad debido a los atisbos de crisis económica provocados por la pandemia del Covid-19.©2023 ASEPUC. Published by EDITUM - Universidad de Murcia. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)fi=vertaisarvioitu|en=peerReviewed

ANNs-Based Early Warning System for Indonesian Islamic Banks

This research proposes a development of Early Warning System (EWS) model towards the financial performance of Islamic bank using financial ratios and macroeconomic indicators. The result of this paper is ready-to-use algorithm for the issue that needs to be solved shortly using machine learning technique which is not widely applied in Islamic banking. The research was conducted in three stages using Artificial Neural Networks (ANNs) technique: the selection of variables that significantly affect financial performance, developing an algorithm as a predictor and testing the predictor algorithm using out of sample data. Finally, the research concludes that the proposed model results in 100% accuracy for predicting Islamic bank’s financial conditions for the next two consecutive months

Сравнительная оценка количественных и качественных методов принятия управленческих решений в условиях антикризисного управления

Цель статьи – провести сравнительный анализ известных классов инструментов диагностики банкротства, используемых при принятии управленческих решений в условиях антикризисного управления. Научная новизна исследования заключается в том, что на основе модели надежного планирования по Г. Тагути показано текущее соотношение эффективности рассмотренных моделей и ожидаемые тенденции использования классов моделей принятия управленческих решений в условиях антикризисного управлени

Corporate Bankruptcy Prediction

Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy

Using K-fold cross validation proposed models for SpikeProp learning enhancements

Spiking Neural Network (SNN) uses individual spikes in time field to perform as well as to communicate computation in such a way as the actual neurons act. SNN was not studied earlier as it was considered too complicated and too hard to examine. Several limitations concerning the characteristics of SNN which were not researched earlier are now resolved since the introduction of SpikeProp in 2000 by Sander Bothe as a supervised SNN learning model. This paper defines the research developments of the enhancement Spikeprop learning using K-fold cross validation for datasets classification. Hence, this paper introduces acceleration factors of SpikeProp using Radius Initial Weight and Differential Evolution (DE) Initialization weights as proposed methods. In addition, training and testing using K-fold cross validation properties of the new proposed method were investigated using datasets obtained from Machine Learning Benchmark Repository as an improved Bohte's algorithm. A comparison of the performance was made between the proposed method and Backpropagation (BP) together with the Standard SpikeProp. The findings also reveal that the proposed method has better performance when compared to Standard SpikeProp as well as the BP for all datasets performed by K-fold cross validation for classification datasets

A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods

НЕЙРОСЕТЕВАЯ МОДЕЛЬ ДИАГНОСТИКИ СТАДИЙ РАЗВИВАЮЩЕГОСЯ БАНКРОТСТВА КОРПОРАЦИЙ

The article deals with the problem of developing an information and mathematical model to support decisionmaking on the restructuring of corporate debt in the banking technologies of financial management. The purpose of the article is to create a model that allows diagnostic of the stages of developing corporate crisis in difficult conditions of incomplete and noisy data. The model should serve as a tool for improving the objectivity and quality of decisions on the restructuring of corporate debt. The study was conducted on the basis of neural network modelling and system analysis methods, methods of decision-making theory, a solution of inverse problems of interpretation, i.e. extraction of new knowledge from data. We developed an original method of constructing neural network logistic model of bankruptcies (NNLMB) in the difficult conditions of the simulation. New features of the method, increasing the predictive power of the model, are: 1) optimal selection of factors using Bayesian ensemble of auxiliary neural networks, performing compression of factor space; 2) step compression of factors based on the generalized Harrington desirability function; 3) regularization of the main (working) neural network model on Bayesian ensemble of neural networks. NNLMB is tested on real data from corporations of the construction industry. The number of correctly identified objects on the test set was more than 90% on all neural networks of the ensemble. In NNLMB, a sufficiently high prognostic quality of the neural network model is provided by new features of the method and generates an emergent effect, which was proven in computational experiments: the improvement of the quality of the neural network model by the criterion of correctly identified objects Θ is 3.336 times with the compression of factors by 1.35 times. NNLMB can be applied to a wide range of financial management tasks.В статье исследуется проблема разработки информационно-математической модели для поддержки принятия решений по реструктуризации кредитной задолженности корпораций в банковских технологиях финансового менеджмента.Цель статьи — создание модели, позволяющей диагностировать стадии развивающегося кризиса корпораций в сложных условиях неполноты и зашумленности данных. Модель должна служить инструментом повышения объективности и качества принимаемых решений по реструктуризации кредитной задолженности корпораций. Исследование проводилось на основе нейросетевых методов моделирования и системного анализа, методов теории принятия решений, решения обратных задач интерпретации, т.е. извлечения новых знаний из данных. Разработан оригинальный метод построения нейросетевой логистической модели банкротств (НЛМБ) в сложных условиях моделирования. Новыми признаками метода, увеличивающими прогностическую силу модели, являются: 1) оптимальный отбор факторов с помощью байесовского ансамбля вспомогательных нейросетей, осуществляющих компрессию факторного пространства; 2) ступенчатая компрессия факторов на основе обобщенной функции желательности Харрингтона; 3) регуляризация основной (рабочей) нейросетевой модели на байесовском ансамбле нейросетей. НЛМБ апробирована на реальных данных корпораций строительной отрасли. Число верно идентифицированных объектов на тестовом множестве составило более 90% на всех нейросетях ансамбля.В НЛМБ достаточно высокое прогностическое качество нейросетевой модели обеспечивается новыми признаками метода и порождает эмерджентный эффект, проверенный в вычислительных экспериментах: улучшение качества нейросетевой модели по критерию правильно идентифицированных объектов Θ составляет 3,336 раза при компрессии факторов в 1,35 раза. НЛМБ может быть распространен на широкий круг задач финансового менеджмента.