Author: Simon James Frank
Publisher:
ISBN:
Category : Corporations
Languages : en
Pages : 210

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Book Description

Author: Petr Hájek
Publisher:
ISBN: 9781616686796
Category : Credit analysis
Languages : en
Pages : 0

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Book Description
This book presents the modelling possibilities of neural networks on a complex real-world problem, i.e. credit rating process modelling. Current approaches in credit rating modelling are introduced, as well as the incorporation of previous findings on corporate and municipal credit rating modelling. Based on this analysis, the model is designed to classify US companies and municipalities into credit rating classes. The model includes data pre-processing, the selection process of input variables, and the design of various neural networks' structures for classification.

Author: Sun, Zhaohao
Publisher: IGI Global
ISBN: 1522572783
Category : Computers
Languages : en
Pages : 357

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Book Description
Big data, analytics, and artificial intelligence are revolutionizing work, management, and lifestyles and are becoming disruptive technologies for healthcare, e-commerce, and web services. However, many fundamental, technological, and managerial issues for developing and applying intelligent big data analytics in these fields have yet to be addressed. Managerial Perspectives on Intelligent Big Data Analytics is a collection of innovative research that discusses the integration and application of artificial intelligence, business intelligence, digital transformation, and intelligent big data analytics from a perspective of computing, service, and management. While highlighting topics including e-commerce, machine learning, and fuzzy logic, this book is ideally designed for students, government officials, data scientists, managers, consultants, analysts, IT specialists, academicians, researchers, and industry professionals in fields that include big data, artificial intelligence, computing, and commerce.

Author: Yixiao Jiang
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 0

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Book Description
Using machine learning methods, this chapter studies features that are important to predict corporate bond ratings. There is a growing literature of predicting credit ratings via machine learning methods. However, there have been less empirical studies using ensemble methods, which refer to the technique of combining the prediction of multiple classifiers. This chapter compares six machine learning models: ordered logit model (OL), neural network (NN), support vector machine (SVM), bagged decision trees (BDT), random forest (RF), and gradient boosted machines (GBMs). By providing an intuitive description for each employed method, this chapter may also serve as a primer for empirical researchers who want to learn machine learning methods. Moody,Äôs ratings were employed, with data collected from 2001 to 2017. Three broad categories of features, including financial ratios, equity risk, and bond issuer,Äôs cross-ownership relation with the credit rating agencies, were explored in the modeling phase, performed with the data prior to 2016. These models were tested on an evaluation phase, using the most recent data after 2016.

Author: Jaime Gil Aluja
Publisher: World Scientific
ISBN: 9814415774
Category : Business & Economics
Languages : en
Pages : 471

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Book Description
This book contains a selection of the papers presented at the XVII SIGEF Congress. It presents fuzzy logic, neural networks and other intelligent techniques applied to economic and business problems. This book is very useful for researchers and graduate students aiming to introduce themselves to the field of quantitative techniques for overcoming uncertain environments. The contributors are experienced scholars of different countries who offer real world applications of these mathematical techniques.

Author: Juan Ramon Rabunal
Publisher: IGI Global
ISBN: 1591409020
Category : Technology & Engineering
Languages : en
Pages : 395

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Book Description
"This book offers an outlook of the most recent works at the field of the Artificial Neural Networks (ANN), including theoretical developments and applications of systems using intelligent characteristics for adaptability"--Provided by publisher.

Author: Sabri Arik
Publisher: Springer
ISBN: 3319265350
Category : Computers
Languages : en
Pages : 680

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Book Description
The four volume set LNCS 9489, LNCS 9490, LNCS 9491, and LNCS 9492 constitutes the proceedings of the 22nd International Conference on Neural Information Processing, ICONIP 2015, held in Istanbul, Turkey, in November 2015. The 231 full papers presented were carefully reviewed and selected from 375 submissions. The 4 volumes represent topical sections containing articles on Learning Algorithms and Classification Systems; Artificial Intelligence and Neural Networks: Theory, Design, and Applications; Image and Signal Processing; and Intelligent Social Networks.

Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1799804151
Category : Computers
Languages : en
Pages : 1671

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Book Description
Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.

Author: Paul D. McNelis
Publisher: Academic Press
ISBN: 0124859674
Category : Business & Economics
Languages : en
Pages : 262

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Book Description
This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website

Author:
Publisher: BoD – Books on Demand
ISBN: 1803565241
Category : Business & Economics
Languages : en
Pages : 128

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Book Description
Econometrics uses statistical methods and real-world data to predict and establish specific trends. This analytical method sustains limitless potential, but the necessary research for professionals to understand and implement this is often lacking. Econometrics - Recent Advances and Applications explores the theoretical and practical aspects of detailed econometric theories and applications within economics, policymaking, and finance. This book covers various topics such as dynamic stochastic general equilibrium (DSGE) models, machine learning, spatial econometrics, and time series analysis. This book is a useful resource for economists, policymakers, financial analysts, researchers, academicians, and graduate students seeking research on the various applications of econometrics.