Forecasting Crashes, Credit Card Default, and Imputation Analysis on Missing Values by the use of Neural Networks

Jazmin Quezada, University of Texas at El Paso

Abstract

A neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks,– also called Artificial Neural Networks – are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence, or AI. Recent studies shows that Artificial Neural Network has the highest coefficient of determination (i.e. measure to assess how well a model explains and predicts future outcomes.) in comparison to the K-nearest neighbor classifiers, logistic regression, discriminant analysis, naive Bayesian classifier, and classification trees. In this work, the theoretical description of the neural network methodology and some practical applications which are based on real world data are presented. We used the Multilayer perceptron (often simply called neural network) to identify financial market crashes and also compute the credit card default payments of customers of a financial institution. The problem of detecting market crashes and credit card default payments were modeled as a special class of classification problem. The neural network technique is very efficient and robust compared to other classification techniques since it correctly discriminates with good accuracy.

Subject Area

Mathematics|Statistics|Applied Mathematics|Artificial intelligence

Recommended Citation

Quezada, Jazmin, "Forecasting Crashes, Credit Card Default, and Imputation Analysis on Missing Values by the use of Neural Networks" (2019). ETD Collection for University of Texas, El Paso. AAI22589816.
https://digitalcommons.utep.edu/dissertations/AAI22589816

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