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Books
Neural Networks in
Finance and Investing
Authors: Trippi, Robert R.; Turban,
Efraim
Publisher: Irwin Professional Publishing
Date: 1993
ISBN: 1-55738-919-5
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Summary:This
book is a diverse collection of survey and
research articles dealing with the use of neural
network technology in financial applications. The
book covers everything from bankruptcy prediction
to predicting stock price performance. It is
highly recommended for anyone who wants to obtain
a broad knowledge of neural network applications
in finance. Neural Networks in the Capital
Markets
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Neural Networks in
the Capital Markets
Editor: Refenes, Apostolos-Paul
Publisher: John Wiley & Sons
Date: 1995
ISBN: 0-471-94364-9
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| Summary:This book provides a very good
introduction on modeling considerations and
testing strategies written by the editor. The
rest of the book is used to present papers on the
following major topics: Equity Applications,
Foreign Exchange Applications, Bond Applications,
and Macroeconomic and Corporate Performance
Applications. A variety of neural network
forecasting techniques are explored within these
papers. |
Core Research
Papers
Selecting Neural Network
Architectures via the Prediction Risk: Application to
Corporate Bond Rating Prediction
Authors: Utans, Joachim; Moody, John
Source: Proceedings of the First International
Conference on Artificial Intelligence Applications on
Wall Street, IEEE Computer Society Press, Los Alamitos,
CA, 1991
Abstract:The notion of
generalization can be defined precisely as the prediction
risk, the expected performance of an estimator on new
observations. In this paper, we propose the prediction
risk as a measure of the generalization ability of
multi-layer perceptron networks and use it to select the
optimal network architecture. The prediction risk must be
estimated from the available data; here we approximate
the prediction risk by v-fold cross-validation and
asymtotic estimates of generalized cross-validation or
Akaike's final prediction error. We apply the technique
to the problem of predicting corporate bond ratings. This
problem is very attractive as a case study, since it is
characterized by the limited availability of the data and
by the lack of complete a priori information that could
be used to impose a structure to the network
architecture. Click to receive a postscript copy of the paper
via FTP
Architecture Selection
Strategies for Neural Networks: Application to Corporate
Bond Rating Prediction
Authors: Moody, John; Utans, Joachim
Source: Neural Networks in the Capital Markets,
Refenes A.N. (ed.), John Wiley Sons, 1994
Abstract:We propose
strategies for selecting a good neural network
architecture for modeling any specific data set. Our
approach involves efficiently searching the space of
possible architectures and selecting a ``best''
architecture based on estimates of generalization
performance. Since an exhaustive search over the space of
architectures is computationally infeasible, we propose
heuristic strategies which dramatically reduce the search
complexity. These employ directed search algorithms,
including selecting the number of nodes via sequential
network construction (SNC), sensitivity based pruning
(SBP)} of inputs, and optimal brain damage (OBD) pruning
for weights. A selection criterion, the estimated
generalization performance or prediction risk, is used to
guide the heuristic search and to choose the final
network. Both predicted squared error (PSE) and nonlinear
cross--validation (NCV) are used for estimating the
prediction risk from the available data. We apply these
heuristic search and prediction risk estimation
techniques to the problem of predicting corporate bond
ratings. This problem is very attractive as a case study,
since it is characterized by a limited set of data and by
the lack of a complete a priori model which could be used
to impose a structure to the network architecture. Click to receive a postscript copy of the paper
via FTP
Neural Networks for Bond
Rating Improved by Multiple Hidden Layers
Authors: Singleton, J.; Surkan, A.
Source: Proceedings of the IEEE International
Conference on Neural Networks, Vol. 2, 163-168, 1990
Modeling the Judgement of
Bond Rating Agencies: Artificial Intelligence Applies to
Finance
Authors: Singleton, J.; Surkan, A.
Source: Journal of the Midwest Finance
Association, 20, 72-80, 1991
Bond Rating: A
Non-Conservative Application of Neural Networks
Authors: Dutta, S.; Shekhar, S.
Source: IEEE International Conference on Neural
Networks, Vol. 2, pp. 443-450, 1988
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