An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Análise de risco de crédito enfrentada por empresas de capital aberto no Brasil: uma abordagem utilizando análise discriminante de regressão logística e redes neurais artificiais

The aims of the present article are to identify the economic-financial indicators that best characterize Brazilian public companies through credit-granting analysis and to assess the most accurate techniques used to forecast business bankruptcy. Discriminant analysis, logistic regression and neural networks were the most used methods to predict insolvency. The sample comprised 121 companies from different sectors, 70 of them solvent and 51 insolvent. The conducted analyses were based on 35 economic-financial indicators. Need of working capital for net income, liquidity thermometer, return on equity, net margin, debt breakdown and equity on assets were the most relevant economic-financial indicators. Neural networks recorded the best accuracy and the Receiver Operating Characteristic Curves (ROC curve) corroborated this outcome.El objetivo del presente artículo es identificar cuáles son los indicadores económicos-financieros que mejor distinguen a las empresas brasileras de capital abierto por medio de concesión de crédito y evaluar cuáles de las técnicas utilizadas son las más precisas para prever la bancarrota de las empresas. Los métodos utilizados para prever la insolvencia fueron análisis discriminante, regresión logística y redes neuronales. La muestra fue compuesta por 121 empresas de diversos sectores, siendo 70 solventes y 51 insolventes. Los análisis utilizaron 35 indicadores económicos-financieros. Los indicadores económicos-financieros más relevantes fueron: necesidad de capital de trabajo sobre utilidad líquida, termómetro de liquidez, retorno sobre el patrimonio líquido, margen de beneficio, ratio de endeudamiento y patrimonio líquido sobre los activos. El modelo de redes neuronales presentó una mayor precisión y fue corroborada por la Curvas Características Operativas del Receptor (curva ROC).O objetivo deste artigo é identificar quais são os indicadores econômico-financeiros que melhor distinguem as empresas de capital aberto brasileiras por meio da concessão de credito e avaliar quais das técnicas utilizadas são as mais precisas para prever a falência das empresas. Os métodos utilizados para antecipar a insolvência foram analise discriminante, regressão logística e redes neurais. A amostra foi composta por 121 empresas de diversos setores, sendo 70 solventes e 51 insolventes. As analises utilizaram 35 indicadores econômico-financeiros. Os indicadores-econômico-financeiros mais relevantes foram: necessidade de capital de giro sobre lucro líquido, termômetro de liquidez, retorno sobre patrimônio líquido, margem de lucro, índice de dívida e patrimônio líquido sobre ativos. O modelo de rede neural apresentou maior precisão e foi corroborado pela curva ROC

An It2fs Model for Sharia Credit Scoring: Analysis & Design

Credit scoring system is a classic problem which is still interesting to study. There are many studies on credit scoring. But, most of them only discuss feasibility analysis. In fact, credit scoring system should accommodate all processes from feasibility analysis until the end of contract. This study is aimed to analyze and design scoring of default status and fines computation processes in Islamic bank. BPMN 2.0 was used to model their processes. Beside that, this study proposed new mechanisms and algorithms using Interval Type-2 Fuzzy Sets for maintaining Sharia rules and fairness guarantee. The results showed that the new methods offer more fair and comply to sharia than existing methods

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

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

Over the last years, it has been observed an increasing interest of the finance and business communities in any application tool related to the prediction of credit and bankruptcy risk, probably due to the need of more robust decision-making systems capable of managing and analyzing complex data. As a result, plentiful techniques have been developed with the aim of producing accurate prediction models that are able to tackle these issues. However, the design of experiments to assess and compare these models has attracted little attention so far, even though it plays an important role in validating and supporting the theoretical evidence of performance. The experimental design should be done carefully for the results to hold significance; otherwise, it might be a potential source of misleading and contradictory conclusions about the benefits of using a particular prediction system. In this work, we review more than 140 papers published in refereed journals within the period 2000–2013, putting the emphasis on the bases of the experimental design in credit scoring and bankruptcy prediction applications. We provide some caveats and guidelines for the usage of databases, data splitting methods, performance evaluation metrics and hypothesis testing procedures in order to converge on a systematic, consistent validation standard.This work has partially been supported by the Mexican Science and Technology Council (CONACYT-Mexico) through a Postdoctoral Fellowship [223351], the Spanish Ministry of Economy under grant TIN2013-46522-P and the Generalitat Valenciana under grant PROMETEOII/2014/062

Default prediction of Spanish companies. A logistic analysis

Licencia Creative Commons: Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)In the field of credit risk management, the calculation of the probability of default of companies plays a key role. For that reason, bankruptcy prediction of companies has generated extensive research in the past decades. This paper applies one of the most popular techniques, the logistic regression. This technique is extensively used both by professionals and academics and is employed in many studies as a benchmark. Here we will apply it on a vast data base of the Spanish companies and a statistical analysis of the robustness of the model will be undertaken, with very satisfactory results.Bartual Sanfeliu, C.; García García, F.; Guijarro Martínez, F.; Moya Clemente, I. (2013). Default prediction of Spanish companies. A logistic analysis. Intellectual economics. 7(3):333-343. doi:10.13165/IE-13-7-3-05S3333437

Using fundamental, market and macroeconomic variables to predict financial distress : a study of companies listed on the Johannesburg Stock Exchange

This study presents a three-stage approach in determining financial distress of companies listed on the Johannesburg Stock Exchange. A novel feature of the present study is that it deviates from a binary classification of corporate distress prediction to present a multinomial outcome where the model predicts distressed, depressed and healthy companies. The research results show an improvement in the prediction accuracy rate when fundamental data is combined with market-based data. However, the further addition of macroeconomic indicators does not enhance the prediction accuracy.This manuscript is based on S.W.S.’s PhD thesis, submitted at the University of Pretoria. L.M.B. was the supervisor while J.H.H. and H.P.W. were co-supervisors. (http://hdl.handle.net/2263/60519)https://www.jefjournal.org.zaam2018Financial Managemen