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

Deep Generative Models for Reject Inference in Credit Scoring

Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-supervised Bayesian models for reject inference in credit scoring, in which we model the data generating process to be dependent on a Gaussian mixture. The goal is to improve the classification accuracy in credit scoring models by adding reject applications. Our proposed models infer the unknown creditworthiness of the rejected applications by exact enumeration of the two possible outcomes of the loan (default or non-default). The efficient stochastic gradient optimization technique used in deep generative models makes our models suitable for large data sets. Finally, the experiments in this research show that our proposed models perform better than classical and alternative machine learning models for reject inference in credit scoring

Piecewise linear regularized solution paths

We consider the generic regularized optimization problem $\hat{\mathsf{\beta}}(\lambda)=\arg \min_{\beta}L({\sf{y}},X{\sf{\beta}})+\lambda J({\sf{\beta}})$. Efron, Hastie, Johnstone and Tibshirani [Ann. Statist. 32 (2004) 407--499] have shown that for the LASSO--that is, if $L$ is squared error loss and $J(\beta)=\|\beta\|_1$ is the $\ell_1$ norm of $\beta$--the optimal coefficient path is piecewise linear, that is, $\partial \hat{\beta}(\lambda)/\partial \lambda$ is piecewise constant. We derive a general characterization of the properties of (loss $L$, penalty $J$) pairs which give piecewise linear coefficient paths. Such pairs allow for efficient generation of the full regularized coefficient paths. We investigate the nature of efficient path following algorithms which arise. We use our results to suggest robust versions of the LASSO for regression and classification, and to develop new, efficient algorithms for existing problems in the literature, including Mammen and van de Geer's locally adaptive regression splines.Comment: Published at http://dx.doi.org/10.1214/009053606000001370 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modelling Credit Defaults Using Support Vector Machine And Binary Logistic Models

Defaulting on a loan essentially occurs when an individual has stopped making payments on a loan or credit card according to the account's terms. A default model is constructed by financial institutions to determine default probabilities on credit obligations by a corporation or sovereign entity. A probability of default model uses multivariate analysis and examines multiple characteristics or variables of the borrower, and it will usually account for credit or business cycles by either incorporating current financial data into the generation of the model or by including economic adjustments. Modelling loan default allows financial institutions to determine typical features and patterns of behavior that lead to a future inability to make debt repayments. This modelling helps to assess the probability of future default for each client. The focus of this study was to apply the support vector machine and binary logistic models to model credit defaults. The process involved identification of the predictors that could be associated with credit defaults as well as comparison of the performance of the prediction models on their statistical power to model credit defaults. The analysis was done using R statistical software. The results showed that variables; credit amount, marital status, credit history and location of property used as security were significant predictors of credit defaults. The results also showed that the binary logistic model had a better performance that the support vector machine model in terms of F1 score and accuracy of predicting credit defaults. The logistic model had the accuracy of 0.826087 and an F1 score of 0.8809524. The support vector machine had the accuracy of 0.7826087 and an F1 score of 0.8554913. From the study findings, it was concluded that, the accuracy of the prediction models in modelling of credit defaults was dependent on the variables considered. Different set of variables would yield different accuracies for the prediction models