A Machine Learning-based DSS for mid and long-term company crisis prediction

In the field of detection and prediction of company defaults and bankruptcy, significant effort has been devoted to evaluating financial ratios as predictors using statistical models and machine learning techniques. This problem becomes crucially important when financial decision-makers are provided with predictions on which to act, based on the output of prediction models. However, research has shown that such predictors are sufficiently accurate in the short-term, with the focus mainly directed towards large and medium-large companies. In contrast, in this paper, we focus on mid- and long-term bankruptcy prediction (up to 60 months) targeting small and/or medium enterprises. The key contribution of this study is a substantial improvement of the prediction accuracy in the short-term (12 months) using machine learning techniques, compared to the state-of-the-art, while also making accurate mid- and long-term predictions (measure of the area under the ROC curve of 0.88 with a 60 month prediction horizon). Extensive computational tests on the entire set of companies in Italy highlight the efficiency and accuracy of the developed method, as well as demonstrating the possibility of using it as a tool for the development of strategies and policies for entire economic systems. Considering the recent COVID-19 pandemic, we show how our method can be used as a viable tool for large-scale policy-making

THE SURVEY OF CERTAIN METHODS AND BUSINESS DIFFICULTIES OR BANKRUPTCY PREDICTION RESEARCHES

Područje predviđanja poslovnih poteškoća i u krajnjemu, stečaja iznimno je popularno. Najsuvremenije metode i modeli iz različitih znanstvenih područja i grana apliciraju se kako bi interesenti dobili informaciju o tome kreće li se određeno trgovačko društvo prema stečaju, te kakvi su izgledi njegova budućeg poslovanja. Ovaj članak prezentira ključne točke u razvoju ovog područja ekonomije, ističući određene relevantnije autore i njihova istraživanja, s posebnim osvrtom na Republiku Hrvatsku i domaće radove. Naglasak je stavljen na mješoviti logit-model i neuronske mreže kojih se veća primjena u Hrvatskoj tek očekuje.Predicting business difficulties and bankruptcy as a case of extreme business difficulty, is expanding significantly and has already become very popular. The most up-to-date methods and models from different scientific areas are being applied in order to properly inform stakeholders about upcoming events in the companies. This article presents certain key points in the development of this area of economy, accentuating several relevant authors and their research, with specific emphasis on the Republic of Croatia and Croatian papers. Mixed logit model and Neural networks are stressed out as tools of bankruptcy prediction whose wider application in Croatia is still expected

Assessment of trajectories of non-bankrupt and bankrupt enterprises

Purpose: The aim of this study is to show how long-term trajectories of enterprises can be used to increase the forecasting horizon of bankruptcy prediction models. Design/Methodology/Approach: The author used seven popular forecasting models (two from Europe, two from Asia, two from North America and one from Latin America). These models (five multivariate discriminant analysis models and two logit models) were used to develop 17-year trajectories separately for non-bankrupt enterprises and those at risk of financial failure. Findings: Based on a sample of 200 enterprises, the author evaluated the differences between non-bankrupt and bankrupt firms in development during 17 years of activity. The long-term usability of the models was demonstrated. To date, these models have been used only to forecast bankruptcy risk in the short term (1–3 years’ prediction horizon). This paper demonstrates that these models can also serve to evaluate long-term growth and to identify the first symptoms of future bankruptcy risk many years before it actually occurs. Practical Implications: It was proven and specified that long-term developmental differences exist between non-threatened and future insolvent companies. These studies proved that the process of going bankrupt is very long, perhaps even longer than the literature has previously demonstrated. Originality/value: This study is one of the first attempts in the literature globally to assess such long-term enterprise trajectories. Additionally by implementing a dynamic approach to the financial ratios in the risk-forecasting model let visualize the changes occurring in the company.peer-reviewe

Bankruptcy prediction modelling in manufacturing branch

Tato diplomová práce se zabývá problematikou predikce bankrotu společností působících ve zpracovatelském průmyslu v České republice. V teoretické části práce jsou definovány pojmy související s tématem predikce bankrotu, metody tvorby bankrotních modelů a výběru proměnných a vybrané bankrotní modely. Analytická část práce zahrnuje testování vybraných bankrotních modelů. Dále je vytvořen nový bankrotní model, který je následně testován a jeho přesnost je porovnávána s modely od jiných autorů.The diploma thesis is aimed at the problematic within the prediction of bankruptcy of companies operating in manufacturing industry in Czech Republic. There are defined terms related to the topic, methods of creating bankruptcy models and selected bankruptcy models in the theoretical part. Analytical part includes testing of the selected bankruptcy models. Thereafter a new bankruptcy model is created, which is subsequently tested and its accuraccy is compared to models from other authors.

Influencia de la inteligencia artificial en el sector financiero. Desarrollo de un modelo de predicción de transacciones futuras

[ES] En los últimos años, con el rápido crecimiento de las tecnologías y el uso del big data, ha proliferado el uso de herramientas de inteligencia artificial. Estas se orientan al diseño e implementación de sistemas inteligentes, capaces de aprender autónomamente a gran velocidad con el fin de ayudar al ser humano en diversas actividades de su vida cotidiana. En concreto, en el sector financiero se han desarrollado un elevado número de aplicaciones para dar apoyo tanto a los clientes como a las propias entidades, contribuyendo así a propiciar el correcto funcionamiento de este sistema. En este contexto, el objetivo de este Trabajo Fin de Grado es identificar las principales aplicaciones hasta ahora implementadas en el entorno financiero, como por ejemplo los Chatbots o el reconocimiento facial, entre otros, evaluando los beneficios que estas reportan a la sociedad. Además, como aplicación práctica, se realiza el diseño de un modelo inteligente de clasificación binaria. Concretamente, consistirá en predecir si un determinado cliente realizará o no transacciones futuras independientemente de la cantidad a transferir. Para ello, se utilizarán redes neuronales, como es el caso de un perceptrón multicapa.[EN] In recent years, the rapid evolution experienced in the technology sector and the continuous growth of available data in the world have led to the birth of artificial intelligence. Its objective is the design and implementation of intelligent systems, capable of learning autonomously at high speed in order to help humans in various activities of daily life. Specifically, in the financial sector a large number of applications have been proposed to support both customers and the financial institutions, thus contributing to the proper working of the financial system. Therefore, the main objective of this Final Degree Project is to identify the main applications existing so far in the financial environment, such as chatbots and biometric authentication, among others, as well as the benefits they bring to society. In addition, as a practical example, the design of an intelligent binary classification model will be proposed. Specifically, it will consist of predicting whether or not a given customer will carry out future transactions regardless of the transfer amount. Artificial neural networks will be used for this purpose, as is the case of the multilayer perceptron.[CA] En els últims anys, la ràpida evolució experimentada al sector tecnològic i el constant increment del nombre de dades disponibles al món, han donat lloc al naixement de la intel·ligència artificial. Esta va encaminada al disseny i implementació de sistemes intel·ligents, capaços d’aprendre autònomament a gran velocitat amb el fi d’ajudar al ser humà en diverses activitats de la vida quotidiana. En concret, al sector financer, un elevat nombre d’aplicacions han sigut propostes per a donar suport tant als clients com a les pròpies entitats, contribuint així a propiciar el correcte funcionament del sistema financer. Per això, el principal objectiu d’aquest Treball Fi de Grau és identificar les principals aplicacions que, fins ara, existeixen a l’entorn financer, com ara els chatbotsi la autenticació biomètrica, entre altres, així com els beneficis que estes reporten a la societat. A més, com a exemple pràctic es va a proposar el disseny d’un model intel·ligent de classificació binària. Concretament, consistirà en predir si un determinat client realitzarà o no transaccions futures independentment de la quantitat a transferir. Per a això, s’utilitzaran xarxes neuronals artificials, com és el cas del perceptró multicapa.Nieves Del Amo, S. (2019). Influencia de la inteligencia artificial en el sector financiero. Desarrollo de un modelo de predicción de transacciones futuras. http://hdl.handle.net/10251/125187TFG

Kreiranje modela za predviđanje stečaja prerađivačkih i trgovinskih preduzeća u Republici Srbiji na bazi pokazatelja finansijske analize

The subject of the research of this PhD thesis is a critical analysis of the application of absolute and relative indicators of financial analysis in the function of developing a bankruptcy prediction model for the enterprises from processing and trade industries in the Republic of Serbia, as well as a comparative analysis of the results of its application in relation to the results of the application of selected traditional and contemporary bankruptcy prediction models for enterprises in the mentioned industries. A special attention was dedicated to the analysis of the impact of the industry on the power of the enterprises’ bankruptcy prediction when using contemporary bankruptcy prediction models. The main goal of the PhD thesis is to critically examine the advantages in anticipating the bankruptcy of a developed new model predicting bankruptcy of enterprises, based on the indicators of financial analysis with the application of logistic regression, in relation to selected traditional and contemporary models for predicting the bankruptcy of the enterprises from processing and trade industries in the Republic of Serbia. The sample consists of 204 enterprises from processing and trade industries in the Republic of Serbia, and the time horizon of observation includes the period from 2011 to 2017. The starting point of the research was the analysis of the financial performances of enterprises through 56 absolute and relative indicators, from which 6 relevant indicators were selected for their contribution to the development of a highly powerful predictive model. As the main result of the research is developed and proposed new model, with the help of using logistic regression, for bankruptcy predicting of enterprises from processing and trade industries, suitable for use in the Republic of Serbia. The proposed model has a higher accuracy of predictions than traditional models developed for efficient markets, such as Altman, Ohlson, and the Zmijevsky models. The contemporary model developed by the application of neural networks has lower predictive accuracy regarding bankruptcy compared to the created model, while the model generated by using decision trees has higher predicting accuracy in comparison to the proposed model created by logistic regression. Within the dissertation is emphasized the difference in the effects of applying the bankruptcy prediction model of enterprises in the Republic of Serbia, developed by the application of logistic regression, when applying on enterprises form different industries. The bankruptcy prediction model developed by using neural networks has higher predictive power if applied to data from individual industry (only processing, or only trade industry), than if applied to data from both observed industries (processing and trade industry together). However, the decision trees model shows equal accuracy in bankruptcy prediction when applied to data from individual industry as well as when applied to data from both observed industries

Neural and wavelet network models for financial distress classification

This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single "mother wavelet" function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997-2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models