750 research outputs found
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
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
Financial distress prediction using the hybrid associative memory with translation
This paper presents an alternative technique for financial distress prediction systems.
The method is based on a type of neural network, which is called hybrid
associative memory with translation. While many different neural network architectures
have successfully been used to predict credit risk and corporate failure, the
power of associative memories for financial decision-making has not been explored
in any depth as yet. The performance of the hybrid associative memory with translation
is compared to four traditional neural networks, a support vector machine
and a logistic regression model in terms of their prediction capabilities. The experimental
results over nine real-life data sets show that the associative memory here
proposed constitutes an appropriate solution for bankruptcy and credit risk prediction,
performing significantly better than the rest of models under class imbalance
and data overlapping conditions in terms of the true positive rate and the geometric
mean of true positive and true negative rates.This work has partially been supported by the Mexican CONACYT through the Postdoctoral Fellowship Program [232167], the Spanish Ministry of Economy [TIN2013-46522-P], the Generalitat Valenciana [PROMETEOII/2014/062] and the Mexican PRODEP [DSA/103.5/15/7004]. We would like to thank the Reviewers for their valuable comments and suggestions, which have helped to improve the quality of this paper substantially
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
The efficiency of bankruptcy predictive models - genetic algorithms approach
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe present dissertation evaluates the contribution of genetic algorithms to improve the performance
of bankruptcy prediction models.
The state-of-the-art points to a better performance of MDA (Multiple Discriminant Analysis)-based
models, which, since 1968, are the most applied in the field of bankruptcy prediction. These models
usually recur to ratios commonly used in financial analysis.
From the comparative study of (1) logistic regression-based models with the forward stepwise method
for feature selection, (2) Altman's Z-Score model (Edward I. Altman, 1983) based on MDA and (3)
logistic regression with the contribution of genetic algorithms for variable selection, a clear
predominance of the efficiency revealed by the former models can be observed. These new models
were developed using 1887 ratios generated a posteriori from 66 known variables, derived from the
accounting, financial, operating, and macroeconomic analysis of firms.
New models are thus presented, which are very promising for predicting bankruptcy in the medium to
long term, in the context of increasing instability surrounding firms for different countries and sectors.A dissertação realizada avalia a contribuição dos algoritmos genéticos para melhorar a performance
dos modelos de previsão de falência.
O estado da arte aponta para uma melhor performance dos modelos baseados em MDA (Análise
descriminante multivariada) que por isso, desde de 1968, são os mais aplicados no âmbito da previsão
de falência. Estes modelos recorrem habitualmente a rácios comumente utlizados em análise
financeira.
A partir do estudo comparado de modelos baseados em (1) regressão logística com o método forward
stepwise para escolha variáveis, (2) o modelo Z-Score de Edward Altman (1983) baseado em MDA e (3)
regressão logística com o contributo de algoritmos genéticos para escolha variáveis, observa-se um
claro predomínio da eficácia revelada por estes últimos. Estes novos modelos, agora propostos, foram
desenvolvidos com recurso a 1887 rácios gerados a posteriori a partir de 66 variáveis conhecidas,
oriundas da análise contabilística, financeira, de funcionamento e de enquadramento
macroeconómico das empresas.
São assim apresentados novos modelos, muito promissores, para a previsão de falência a médio longo
prazo em contexto de crescente instabilidade na envolvente das empresas, para diferentes países e
sectores
Financial distress model prediction using SVM +
Financial distress prediction is of great importance
to all stakeholders in order to enable better decision-making
in evaluating firms. In recent years, the rate of bankruptcy
has risen and it is becoming harder to estimate as companies
become more complex and the asymmetric information between
banks and firms increases. Although a great variety of techniques
have been applied along the years, no comprehensive
method incorporating an holistic perspective had hitherto
been considered. Recently, SVM+ a technique proposed by
Vapnik [17] provides a formal way to incorporate privileged
information onto the learning models improving generalization.
By exploiting additional information to improve traditional
inductive learning we propose a prediction model where data is
naturally separated into several groups according to the size of
the firm. Experimental results in the setting of a heterogeneous
data set of French companies demonstrated that the proposed
model showed superior performance in terms of prediction
accuracy in bankruptcy prediction and misclassification cost.This work was partially supported by Fundacao da Ciencia e Tecnologia' under grant no.PTDC/GES/70168/2006
Feature selection for bankruptcy prediction: a multi-objective optimization approach
In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature
selection in the problem of bankruptcy prediction. The aim is to maximize the accuracy of the
classifier while keeping the number of features low. A two-objective problem - minimization
of the number of features and accuracy maximization – was fully analyzed using two
classifiers, Logistic Regression (LR) and Support Vector Machines (SVM). Simultaneously,
the parameters required by both classifiers were also optimized. The validity of the
methodology proposed was tested using a database containing financial statements of 1200
medium sized private French companies. Based on extensive tests it is shown that MOEA is
an efficient feature selection approach. Best results were obtained when both the accuracy and
the classifiers parameters are optimized. The method proposed can provide useful information
for the decision maker in characterizing the financial health of a company
Feature selection in credit risk modeling: an international evidence
This paper aims to discover a suitable combination of contemporary feature selection techniques and robust prediction classifiers.
As such, to examine the impact of the feature selection method
on classifier performance, we use two Chinese and three other
real-world credit scoring datasets. The utilized feature selection
methods are the least absolute shrinkage and selection operator
(LASSO), multivariate adaptive regression splines (MARS). In contrast, the examined classifiers are the classification and regression
trees (CART), logistic regression (LR), artificial neural network
(ANN), and support vector machines (SVM). Empirical findings
confirm that LASSO’s feature selection method, followed by
robust classifier SVM, demonstrates remarkable improvement and
outperforms other competitive classifiers. Moreover, ANN also
offers improved accuracy with feature selection methods; LR only
can improve classification efficiency through performing feature
selection via LASSO. Nonetheless, CART does not provide any
indication of improvement in any combination. The proposed
credit scoring modeling strategy may use to develop policy, progressive ideas, operational guidelines for effective credit risk management of lending, and other financial institutions. The finding
of this study has practical value, as to date, there is no consensus
about the combination of feature selection method and prediction classifiers
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