1,533 research outputs found

    Financial Soundness Prediction Using a Multi-classification Model: Evidence from Current Financial Crisis in OECD Banks

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    The paper aims to develop an early warning model that separates previously rated banks (337 Fitch-rated banks from OECD) into three classes, based on their financial health and using a one-year window. The early warning system is based on a classification model which estimates the Fitch ratings using Bankscope bankspecific data, regulatory and macroeconomic data as input variables. The authors propose a “hybridization technique” that combines the Extreme learning machine and the Synthetic Minority Over-sampling Technique. Due to the imbalanced nature of the problem, the authors apply an oversampling technique on the data aiming to improve the classification results on the minority groups. The methodology proposed outperforms other existing classification techniques used to predict bank solvency. It proved essential in improving average accuracy and especially the performance of the minority groups

    Comparing the Performance of Deep Learning Methods to Predict Companies' Financial Failure

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    This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades under Project RTI2018-102002-A-I00, in part by the Ministerio de Economia y Competitividad under Project TIN2017-85727-C4-2-P and Project PID2020-115570GB-C22, in part by the Fondo Europeo de Desarrollo Regional (FEDER) and Junta de Andalucia under Project B-TIC-402-UGR18, and in part by the Junta de Andalucia under Project P18-RT-4830.One of the most crucial problems in the eld of business is nancial forecasting. Many companies are interested in forecasting their incoming nancial status in order to adapt to the current nancial and business environment to avoid bankruptcy. In this work, due to the effectiveness of Deep Learning methods with respect to classi cation tasks, we compare the performance of three well-known Deep Learning methods (Long-Short Term Memory, Deep Belief Network and Multilayer Perceptron model of 6 layers) with three bagging ensemble classi ers (Random Forest, Support Vector Machine and K-Nearest Neighbor) and two boosting ensemble classi ers (Adaptive Boosting and Extreme Gradient Boosting) in companies' nancial failure prediction. Because of the inherent nature of the problem addressed, three extremely imbalanced datasets of Spanish, Taiwanese and Polish companies' data have been considered in this study. Thus, ve oversampling balancing techniques, two hybrid balancing techniques (oversamplingundersampling) and one clustering-based balancing technique have been applied to avoid data inconsistency problem. Considering the real nancial data complexity level and type, the results show that the Multilayer Perceptron model of 6 layers, in conjunction with SMOTE-ENN balancing method, yielded the best performance according to the accuracy, recall and type II error metrics. In addition, Long-Short Term Memory and ensemble methods obtained also very good results, outperforming several classi ers used in previous studies with the same datasets.Ministerio de Ciencia, Innovacion y Universidades RTI2018-102002-A-I00Spanish Government TIN2017-85727-C4-2-P PID2020-115570GB-C22European Commission B-TIC-402-UGR18Junta de Andalucia B-TIC-402-UGR18 P18-RT-483

    Influence of the Event Rate on Discrimination Abilities of Bankruptcy Prediction Models

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    In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy prediction models. First the statistical association and significance of public records and firmographics indicators with the bankruptcy were explored. Then the event rate was oversampled from 0.12% to 10%, 20%, 30%, 40%, and 50%, respectively. Seven models were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. Under different event rates, models were comprehensively evaluated and compared based on Kolmogorov-Smirnov Statistic, accuracy, F1 score, Type I error, Type II error, and ROC curve on the hold-out dataset with their best probability cut-offs. Results show that Bayesian Network is the most insensitive to the event rate, while Support Vector Machine is the most sensitive

    Machine learning for corporate failure prediction : an empirical study of South African companies

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    Includes bibliographical references (leaves 255-266).The research objective of this study was to construct an empirical model for the prediction of corporate failure in South Africa through the application of machine learning techniques using information generally available to investors. The study began with a thorough review of the corporate failure literature, breaking the process of prediction model construction into the following steps: * Defining corporate failure * Sample selection * Feature selection * Data pre-processing * Feature Subset Selection * Classifier construction * Model evaluation These steps were applied to the construction of a model, using a sample of failed companies that were listed on the JSE Securities Exchange between 1 January 1996 and 30 June 2003. A paired sample of non-failed companies was selected. Pairing was performed on the basis of year of failure, industry and asset size (total assets per the company financial statements excluding intangible assets). A minimum of two years and a maximum of three years of financial data were collated for each company. Such data was mainly sourced from BFA McGregor RAID Station, although the BFA McGregor Handbook and JSE Handbook were also consulted for certain data items. A total of 75 financial and non-financial ratios were calculated for each year of data collected for every company in the final sample. Two databases of ratios were created - one for all companies with at least two years of data and another for those companies with three years of data. Missing and undefined data items were rectified before all the ratios were normalised. The set of normalised values was then imported into MatLab Version 6 and input into a Population-Based Incremental Learning (PBIL) algorithm. PBIL was then used to identify those subsets of features that best separated the failed and non-failed data clusters for a one, two and three year forward forecast period. Thornton's Separability Index (SI) was used to evaluate the degree of separation achieved by each feature subset

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

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    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

    Financial risk management in shipping investment, a machine learning approach

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    There has been a plethora of research into company credit risk and financial default prediction from both academics and financial professionals alike. However, only a limited volume of the literature has focused on international shipping company financial distress prediction, with previous research concentrating largely on classic linear based modelling techniques. The gaps, identified in this research, demonstrate the need for increased effort to address the inherent nonlinear nature of shipping operations, as well as the noisy and incomplete composition of shipping company financial statement data. Furthermore, the gaps illustrate the need for a workable definition of financial distress, which to date has too often been classed only by the ultimate state of bankruptcy/insolvency. This definition prohibits the practical application of methodologies which should be aimed at the timely identification of financial distress, thereby allowing for remedial measures to be implemented to avoid ultimate financial collapse. This research contributes to the field by addressing these gaps through i) the creation of a machine learning based financial distress forecasting methodology and ii) utilising this as the foundation for the development of a software toolkit for financial distress prediction. This toolkit enables the practical application of the financial risk principles, embedded within the methodology, to be readily integrated into an enterprise/corporate risk management system. The methodology and software were tested through the application of a bulk shipping company case study utilising 5000 bulk shipping company-year accounting observations for the period 2000-2018, in combination with market and macroeconomic data. The results demonstrate that the methodology improves the capture of distress correlations, that traditional financial distress models have struggled to achieve. The methodology's capacity to adequately treat the problem of missing data in company financial statements was also validated. Finally, the results also highlight the successful application of the software toolkit for the development of a multi-model, real time system which can enhance the financial monitoring of shipping companies by acting as a practical "early warning system" for financial distress.There has been a plethora of research into company credit risk and financial default prediction from both academics and financial professionals alike. However, only a limited volume of the literature has focused on international shipping company financial distress prediction, with previous research concentrating largely on classic linear based modelling techniques. The gaps, identified in this research, demonstrate the need for increased effort to address the inherent nonlinear nature of shipping operations, as well as the noisy and incomplete composition of shipping company financial statement data. Furthermore, the gaps illustrate the need for a workable definition of financial distress, which to date has too often been classed only by the ultimate state of bankruptcy/insolvency. This definition prohibits the practical application of methodologies which should be aimed at the timely identification of financial distress, thereby allowing for remedial measures to be implemented to avoid ultimate financial collapse. This research contributes to the field by addressing these gaps through i) the creation of a machine learning based financial distress forecasting methodology and ii) utilising this as the foundation for the development of a software toolkit for financial distress prediction. This toolkit enables the practical application of the financial risk principles, embedded within the methodology, to be readily integrated into an enterprise/corporate risk management system. The methodology and software were tested through the application of a bulk shipping company case study utilising 5000 bulk shipping company-year accounting observations for the period 2000-2018, in combination with market and macroeconomic data. The results demonstrate that the methodology improves the capture of distress correlations, that traditional financial distress models have struggled to achieve. The methodology's capacity to adequately treat the problem of missing data in company financial statements was also validated. Finally, the results also highlight the successful application of the software toolkit for the development of a multi-model, real time system which can enhance the financial monitoring of shipping companies by acting as a practical "early warning system" for financial distress
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