8,658 research outputs found
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
A literature review on the application of evolutionary computing to credit scoring
The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees. However, the importance of credit grant decisions for financial institutions has caused growing interest in using a variety of computational intelligence techniques. This paper concentrates on evolutionary computing, which is viewed as one of the most promising paradigms of computational intelligence. Taking into account the synergistic relationship between the communities of Economics and Computer Science, the aim of this paper is to summarize the most recent developments in the application of evolutionary algorithms to credit scoring by means of a thorough review of scientific articles published during the period 2000–2012.This work has partially been supported by the Spanish Ministry of Education and Science under grant TIN2009-14205 and the Generalitat Valenciana under grant PROMETEO/2010/028
Particle swarm optimization for linear support vector machines based classifier selection
Particle swarm optimization is a metaheuristic technique widely applied to solve various optimization problems as well as parameter selection problems for various classification techniques. This paper presents an approach for linear support vector machines classifier optimization combining its selection from a family of similar classifiers with parameter optimization. Experimental results indicate that proposed heuristics can help obtain competitive or even better results compared to similar techniques and approaches and can be used as a solver for various classification tasks
A Simple Iterative Algorithm for Parsimonious Binary Kernel Fisher Discrimination
By applying recent results in optimization theory variously known as optimization transfer or majorize/minimize algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The problem is converted into a smooth optimization that can be solved iteratively with no greater overhead than iteratively re-weighted least-squares. The result is simple, easily programmed and is shown to perform, in terms of both accuracy and parsimony, as well as or better than a number of leading machine learning algorithms on two well-studied and substantial benchmarks
Credit scoring: comparison of non‐parametric techniques against logistic regression
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceOver the past decades, financial institutions have been giving increased importance to credit risk
management as a critical tool to control their profitability. More than ever, it became crucial for
these institutions to be able to well discriminate between good and bad clients for only
accepting the credit applications that are not likely to default. To calculate the probability of
default of a particular client, most financial institutions have credit scoring models based on
parametric techniques. Logistic regression is the current industry standard technique in credit
scoring models, and it is one of the techniques under study in this dissertation. Although it is
regarded as a robust and intuitive technique, it is still not free from several critics towards the
model assumptions it takes that can compromise its predictions. This dissertation intends to
evaluate the gains in performance resulting from using more modern non-parametric
techniques instead of logistic regression, performing a model comparison over four different
real-life credit datasets. Specifically, the techniques compared against logistic regression in this
study consist of two single classifiers (decision tree and SVM with RBF kernel) and two ensemble
methods (random forest and stacking with cross-validation). The literature review demonstrates
that heterogeneous ensemble approaches have a weaker presence in credit scoring studies and,
because of that, stacking with cross-validation was considered in this study. The results
demonstrate that logistic regression outperforms the decision tree classifier, has similar
performance in relation to SVM and slightly underperforms both ensemble approaches in similar
extents
Using neural networks and support vector machines for default prediction in South Africa
A thesis submitted to the Faculty of Computer Science and Applied Mathematics,
University of Witwatersrand,
in fulfillment of the requirements for the
Master of Science (MSc)
Johannesburg
Feb 2017This is a thesis on credit risk and in particular bankruptcy prediction. It investigates
the application of machine learning techniques such as support vector machines and
neural networks for this purpose. This is not a thesis on support vector machines
and neural networks, it simply looks at using these functions as tools to preform the
analysis.
Neural networks are a type of machine learning algorithm. They are nonlinear mod-
els inspired from biological network of neurons found in the human central nervous
system. They involve a cascade of simple nonlinear computations that when aggre-
gated can implement robust and complex nonlinear functions. Neural networks can
approximate most nonlinear functions, making them a quite powerful class of models.
Support vector machines (SVM) are the most recent development from the machine
learning community. In machine learning, support vector machines (SVMs) are su-
pervised learning algorithms that analyze data and recognize patterns, used for clas-
si cation and regression analysis. SVM takes a set of input data and predicts, for
each given input, which of two possible classes comprises the input, making the SVM
a non-probabilistic binary linear classi er. A support vector machine constructs a
hyperplane or set of hyperplanes in a high or in nite dimensional space, which can
be used for classi cation into the two di erent data classes.
Traditional bankruptcy prediction medelling has been criticised as it makes certain
underlying assumptions on the underlying data. For instance, a frequent requirement
for multivarate analysis is a joint normal distribution and independence of variables.
Support vector machines (and neural networks) are a useful tool for default analysis
because they make far fewer assumptions on the underlying data.
In this framework support vector machines are used as a classi er to discriminate
defaulting and non defaulting companies in a South African context. The input data
required is a set of nancial ratios constructed from the company's historic nancial
statements. The data is then Divided into the two groups: a company that has
defaulted and a company that is healthy (non default). The nal data sample used
for this thesis consists of 23 nancial ratios from 67 companies listed on the jse.
Furthermore for each company the company's probability of default is predicted.
The results are benchmarked against more classical methods that are commonly used
for bankruptcy prediction such as linear discriminate analysis and logistic regression.
Then the results of the support vector machines, neural networks, linear discriminate
analysis and logistic regression are assessed via their receiver operator curves and
pro tability ratios to gure out which model is more successful at predicting default.MT 201
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The Classification Performance of Multiple Methods and Datasets: Cases from the Loan Credit Scoring Domain
Decisions to extend credit to potential customers are complex, risky and even potentially catastrophic for the credit granting institution and the broader economy as underscored by credit failures in the late 2000s. Thus, the ability to accurately assess the likelihood of default is an important issue. In this paper the authors contrast the classification accuracy of multiple computational intelligence methods using five datasets obtained from five different decision contexts in the real world. The methods considered are: logistic regression (LR), neural network (NN), radial basis function neural network (RBFNN), support vector machine (SVM), k-nearest neighbor (kNN), and decision tree (DT). The datasets have various characteristics with respect to the number of cases, the number and type of attributes, the extent of missing values as well as different ratios for bad loans/good loans. Using areas under ROC charts as well as the classification accuracy rates for overall, bad loans, and good loans the performances of six methods across five datasets and the five datasets across the methods are examined to find if there are significant differences between the methods and datasets. Our results reveal some interesting findings which may be useful to practitioners. Even though no method consistently outperformed any other method using the above metrics on all datasets, this study provides some guidelines as to the most appropriate methods suitable for each specific data set. In addition, the study finds that customer financial attributes are much more relevant than the personal, social, or employment attributes for predictive accuracy
Supervised classification and mathematical optimization
Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized. It is shown that many different areas of Mathematical Optimization play a central role in off-the-shelf Supervised Classification methods. Moreover, Mathematical Optimization turns out to be extremely
useful to address important issues in Classification, such as identifying relevant variables, improving the interpretability of classifiers or dealing with vagueness/noise in the data.Ministerio de Ciencia e InnovaciónJunta de Andalucí
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