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From: mavrmgls@rtsg.mot.com (Nick M. Mavromagoulos)
Newsgroups: comp.ai.neural-nets
Subject: NN in Financial Apps (1 of 2)
Date: 31 Jan 1994 20:11:20 GMT
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The following is a listing of information concerning Neural Networks in
financial applications. Thanks to all who responded. This is file 1 of 2.
BOOKS & MAGAZINES:
------------------
1. Title: Neural Networks in Finance and Investing,
Summary: Using artificial intelligence to improve real-world performance.
Editors: Robert R. Trippi, Efraim Turban.
Chicago, Ill., Probus, 1992
xxviii, 513p, ill
2. From an advertisement in 'PC AI' magazine.
Title: Forecasting With Neural Networks
Price: $12.50 (US).
Summary: The interesting bit is that the detailed example in
the paper is a "how to" on using neural networks to
predict commodity prices. It is really quite good.
Address: Bellwood Research Center
19 Bellwood Avenue
Ottawa, Ontario
Canada, K1S 1S6
3. Fortune Sept. 93.
An article about Neural Netwirks had clips on financial management.
Look at the picture, you can tell what software they used.
4. Title: NEUROVE$T JOURNAL
Summary: A bi-monthly journal on applying neural networks and other
advanced technologies and tools to the financial markets.
Looking for papers and subscribers. If you're interested in
either or both, please contact the editor. (See below).
Address: P.O. Box 764
Haymarket, VA 22069 USA
Editor: Randall Caldwell
E-Mail: rbcaldwell@delphi.com
5. Title: Forecasting with Neural Networks
Source: Neuron Digest (V12 #24)
6. Title: Technical Analysis of Stocks and Commodities (ISSN 0738-3355)
Summary: The magazine covers "the waterfront" on financial markets from the
trader's view and focuses only on market trading. As it strives
to appeal to a broad audience, the articles tend to be short and
introductory, but the magazine does introduce all the hot topics.
Note: This may be a BONUS issue which are only available to subscribers.
It is usally found at the "better" book stores. The bonus issues
contain abstracts and idexes to all of the articles.
Source: Technical Analysis, Inc.
Address: 4757 California Ave. S. W.
Seattle, WA 98116-4499
7. Source: Forbes
Date: Jan 21-28 1994
GROUPS / CONFERENCES:
---------------------
1. Name: The Society for the Management of Advanced Relevant Technologies
in Financial Services (SMART-F$)
Summary: SMART-F$ membership is a great way to find out what the financial
services community's interests and activities are in the emerging
technologies area. The organization was established in 1987 and
was originally called Society for the Management of AI Resources
and Technology in Financial Services, with the same acronym --
SMART-F$.
Cost: SMART-F$ annual membership, which includes admission to 5 general
meetings per year and all of its SIG sessions, is $40.00 per
person for end users of advanced/emerging technologies and
$100.00 per person for vendors of advanced/emerging technologies
or vendors to the financial services companies.
Contact: Dr. Dan Schutzer Susan Garavaglia
Chairman Advisory Board Member
212-599-1876 201-605-6216
MISCELLANEOUS:
--------------
1. I'm near-real-time testing a NN solution for predicting SPX (SP 500 Index
Options). Took months to generate usable preprocessing rules for training
data (selecting applicable features, manipulations for averaging, scaling,
etc.), days (hours) to train Back Propagation network. Currently, I am
loading each day's closing data and testing to see if the NN can predict
the future - then checking to see if it was right at the end of the
prediction period. If it works, I'll happily publish my methods and
results after I retire. If it dosen't, I'll go ahead and post them in the
next few months. At least you'll be able to eliminate one method.
Mark Jurik (I think I've seen some of his posts in this group) gives as
good a description of the mechanics of selecting and preprocessing data as
any I've seen, but you'd better be your own domain expert and select your
own features - not use the ones he uses in his example.
Also, Jurik's basic article is in the Appendix to the User Manual of
BrainCel - a simple, easy NN package that's a DLL for Microsoft Excel on a
Windows 3.1 PC. I found Braincel a cheap way to get started before
spending a lot of time coding my own NN algorithms (or spending a major
chunk of my risk capital on one of the more elegant packages like
BrainMaker) - they had a $99.95 special going at one time. Manufacturer is
Promised Land Technologies - they advertise in 'AI Expert' and 'Stocks &
Commodities' magazines. Might also subscribe to Neurove$t Journal - I've
seen one issue and it had some hints. Also advertised in 'Stocks &
Commodities'.
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From: mavrmgls@rtsg.mot.com (Nick M. Mavromagoulos)
Newsgroups: comp.ai.neural-nets
Subject: NN in Financial Apps (2 of 2)
Date: 31 Jan 1994 20:13:26 GMT
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The following is a listing of information concerning Neural Networks in
financial applications. Thanks to all who responded. This is file 2 of 2.
REFERENCES ON NEURAL NETWORKS, GENETIC ALGORITHMS IN FINANCIAL
FORECASTING
Note:
The below mentioned references have been arranged in order of
receipt. Some redundancies and inconsistencies in the formatting
will therefore be there.
==========++++++++++++++++++++++++++++++++++++++++===============
Akaike H, 1986, Use of Statistical Models for Time Series
Analysis, Proceesings ICASSP 86, 3147-3155, IEEE, Tokyo.
Bulsari A. B. & Sax H., 1993, A Recurrent Neural Network for
Time Series Modelling, in Artificial Neural Nets and Genetic
Algorithms, Ed. Albrecht R.F., Steele N.C., & Reeves C.R., 285-
291, Springer-Verlag, Innsbruck, Austria.
Casdagli M, 1989, Nonlinear Prediction of Chaotic Time Series,
Physical Review, 35, 335-356.
Farmer J. D. & Sidorowich J. J., 1987, Predicting chaotic time
series, Physical Review Letters,59, 845-848.
Gabr M. M. & Subba Rao, T, 1981, The estimation and prediction of
subset bilinear time series models with applications, Journal of
Time Series Analysis, 2, 155-171.
Gent C.R., 1990, Predicting Time Series by a Fully Connected
Recurrent Net Trained by Back Propagation -- A First
Look,Handsheed at Neuro Nimes 90.
Jones R D , Y. C. Lee , C. W. Barnes , G. W. Flake , K. Lee , P.
S. Lewis and S. Qian, Function Approximation and Time Series
Prediction withNeural Networks,International Joint Conference on
Neural Networks, Univ. of Calif., Los Alamos Nat. Laboratory,
June, San Diego, CA, 649-665, I, 1990
Lewis P A. W.& J. G. Stevens, Nonlinear modeling of time series
using multivariate adaptive regression splines (MARS) Preprint,
Naval Postgraduate School, Monterey, CA, January 1991,
Martin R. Douglas, 1981, Robust methods for time series, Applied
Time Series Analysis II 683-759, D. F. Findley, New York,
Academic Press.
Metzger M.A., Mitsumoto K, 1991, Forecasting Multivariate Time-
Series: Confidence Intervals and Comparison of Performances of
Feed-Forward Neural Network and StateSpace Models, International
Joint Conference on Neural Networks, submitted, Seattle,
Washington.
Packard N H, J P. Crutchfield, J. D. Farmer and R. S. Shaw,
1980, Geometry from a Time Series, Physical Review Letters, 712-
716, 45.
Weigend A S, & David E. Rumelhart, Bernardo A. Huberman, 1990,
Back-propagation, weight-elimination and time series prediction,
Proceedings of the 1990 Connectionist Models Summer School,
Ed. David S. Touretzky and J. L. Elman, Terence J. Sejnowski,
Geoffrey E. Hinton, PUBLISHER = Morgan Kaufmann, San Mateo,
CA, 105--116
Kuan, Chung-Ming "Forecasting Exchange Rates Using Feedforward
And Recurrent Neural Networks", faculty working paper of
University of Illinois at Urbana Champaign.
PC AI Magazine "Forecasting With Neural Networks", available
from Bellwood Research Center, Bellwood Avenue, Ottawa, Ontario,
Canada, K1S 1S6.
Choi, Jai J, O'Keefe, Kenneth H and Baruah, Pranab "Non-linear
System Diagnosis Using Neural Networks And Fuzzy Logic" [FUZZY
IEEE 92] and Boeing Computer Services TR B91-91.
Collard, J E "Commodity Trading With A Three Year Old" [IJCNN].
Grinzberg, I and Horn, D "Learning The Rule Of A Time Series"
International Journal Of neural Systems, volume 3 number 2/3
Murtagh, Fionn "Neural Networks For Statistical And Economic
Data" Workshop Proceeding Dublin.
Refenes, A N, Barac, M Azeman,Chen, L and Karouses, S A "Currency
Exchange Rate Prediction And Neural Networks Design Strategies"
TR Department of Computerscience University College London and
Journal Of Neurocomputing and Applications.
Scho:neburg, E "Stock Price Prediction Using Neural Networks: A
Project Report" Neurocomputing 2 pp.
Sharda, R and Patil, R B "Neural Network A Forecasting Expert: An
Empirical Test" [IJCNN90].
White, H "Economic Prediction Using Neural Networks: The Case Of
IBM Daily Stock Return" University of California, San Diego TR
88-20.
Wang, F and Tan, P Y "Neural Networks And Genetic Algorithms For
Economic Forecasting".
Lapedes, A and faber, R "Non-linear Signal Processing Using
Neural Networks: Prediction and System Modeling" Los Alamos
National Laboratory Report.
`Nonlinear modeling and forecasting', book edited by Casdagli and
Eubank, The Santa fe Institute Studies in the Sciences of
Complexity -Addison Wesley 1992
Soft Classification, a.k.a. Penalized Log Likelihood and
Smoothing Spline Analysis of Variance by Grace Wahba, Chong Gu,
Yuedong Wang and Rick Chappell to appear in the proceedings of
the Santa Fe Workshop on Supervised Machine Learning, August
1992, D. Wolpert and A. Lapedes, eds. also partly presented at
CLNL*92.
Smoothing Spline ANOVA with Component-Wise Bayesian `Confidence
Intervals' by Chong Gu and Grace Wahba, J. Computational and
Graphical Statistics, March 1993
W. Hsu and L. S. Hsu and M. F. Tenorio, "Feature Subset Selection
with Application to Financial Prediction Tasks," in Financial
Applications and Neural Networks, P. Refenes, ed., Cambridge
Press, 1993.
Tenorio, M. F., and W. T. Lee, "Self Organizing Networks for
Optimum Supervised Learning," IEEE Trans. on Neural Networks,
IEEE Press, vol. 1, no. 1, pp. 100-110, 1990, premier issue.
Tenorio, M. F., "Topology Synthesis Networks: Self Organization
of Structure and Weight Adjustment as a Learning Paradigm,"
Parallel Computation, North Holland, vol. 14, pp. 363-380, 1990.
Tenorio, M. F., and W. T. Lee, "Self Organizing Neural Networks
for the Identification Problem," 2nd IEEE Neural Information
Processing Systems Conference, sponsored by IEEE, Denver,
Colorado, December 1988.
Tenorio, M. F., "The Self Organizing Neural Network Algorithm:
Adapting Structure for Optimum Supervised Learning," Hawaii
International Conference System Sciences, HICSS-22, Honolulu,
Hawaii, January 1990.
Hsu, W., and M. F. Tenorio, "A Similarity Based Approach to
Forecasting," IEEE Workshop on Neural Networks, co-sponsored by
Auburn University Space Power Institute, the Center for
Commercial Development of Space Power and Advanced Electronics,
and NASA Headquarters, Auburn, Alabama, pp. 138-142, February
1992.
Hsu, W. and Tenorio, M. F., "Plastic Network for Predicting the
Mackey-Glass Time Series," International Joint Conference on
Neural Networks, sponsored by the IEEE Neural Network Council
and the International Neural Network Society, Baltimore,
Maryland, pp.941-946, June 1992.
Thome, A. C., M. F. Tenorio, "Economic Signal Analysis,
Modelling, and Estimation Through a Similarity Based Approach,"
IX Brazilian Society for Artificial Intelligence Meeting, Rio de
Janeiro, Brazil, October 1992.
M. F. Tenorio, "Application of Neural Network for Chaotic System
Modelling," International Keynote Speech, First Workshop on
Neural Networks, co-sponsored by IBM and Varig, Rio de Janeiro,
Brazil, October 1992.
M. F. Tenorio, "Implications of Neural Networks to Economic
Forecasting," First Workshop on Neural Networks, co-sponsored by
IBM and Varig, Rio de Janeiro, Brazil, October 1992.
W. Hsu and L. S. Hsu and M. F. Tenorio, "A ClusNet Architecture
for Prediction," International Conference on Neural Networks,
sponsored by the IEEE Neural Network Council, IEEE Publishing
Services, 1993.
Thome, A. C., M. F. Tenorio, "Analysis, Modelling, and Estimation
Through a Similarity Based Approach: An Economic Signal Case
Study" International Conference on Neural Networks, sponsored by
the IEEE Neural Network Council, IEEE Publishing Services, 1993.
Hsu, W., L. S. Hsu and M. F. Tenorio,"Feature Subset Selection
and Financial Prediction Tasks", TR-EE-92-53, Technical Report of
the School of Electrical Engineering, Purdue University,
December 1992.
Hsu W., L. S. Hsu and M. F. Tenorio, "A Neural Network
Architecture for Prediction", TR-EE-92-38, Technical Report of
the School of Electrical Engineering, Purdue University,
December, 1992.
Hsu, W. , L. S. Hsu and M. F. Tenorio, "An Embedding Selection
Algorithm for the Prediction of Chaotic Dynamical Systems", TR-
EE-92-51, Technical Report of the School of Electrical
Engineering, Purdue University, December, 1992.
Hsu W., L. S. Hsu and M. F. Tenorio, "A Decision Boundary Method
for Embedding Selection", TR-EE-92-52, Technical Report of the
School of Electrical Engineering, Purdue University, December,
1992.
Hsu,W., L. S. Hsu, M. F. Tenorio, "Indicator Selection with
SupNet and Currency Prediction," The Journal of Neural Computing
& Applications, submitted.
Hsu,W., L. S. Hsu, M. F. Tenorio, "The ClusNet Algorithm for the
Prediction of Time Series" International Journal of Neural
Systems,submitted.
Hsu,W., L. S. Hsu, M. F. Tenorio, "A Decision Boundary Method
for Chaotic Time Series Embedding Selection," Journal of Complex
Systems, submitted.
Hsu,W., L. S. Hsu, M. F. Tenorio, "A Novel Embedding Selection
Algorithm for Chaotic Time Series Prediction," Physics Review
Letters , submitted.
J.-S. Roger Jang, ANFIS: Adaptive-Network-Based Fuzzy Inference
System, itsmc, 1993, 23, 03, may, (Forthcoming)
S. Margarita, "Genetic neural networks for financial markets:
Some results", 10th European Conf on AI, ECAI-92, pp211-213.
Andrewa Beltratti & Segrio Margarita, "Evolution of trading
strategies among heterogeneous artificial economic agents," 2nd
international conf on simulation of adaptive behaviour (SAB92).
T. Tanigawa,k K. Kamijo, "Stock price pattern matchin system --
Dynamic programming neural net approach", IJCNN92, Vol II, pp
465-471.
R. D. Jones, Y. C. Lee, C. W. Barnes, G. W. Flake, K Lee, P. S.
Lewis, S. Qian, "Function approximation and time series
prediction with neural networks", tech report LA-UR 90-21, Los
Alamos National Lab.
Richard J. Bauer & Gunar Liepins, "Genetic Algorithms and Stock
Market Timing Trading Rules", 11th International Syumposium on
Forcasting, New York City, 1991.
Chia-Fen Chang, B. J. Sheu, Jeff Thomas, "Multil-Layered back-
propagation neural networks for finance analysis", World Congress
on Neural Networks, Portland, OR, 1993, pp 445-450.
Refenes A.N et al, Currency exchange Rate prediction and Neural
network Design Strategies, Neural Computing and Applications, 1,
1, 1993.
Hoptroff RG The principles and practice of Time series
forecasting and Business Modelling Using neural nets, Neural
Computing and Applications, 1,1, 1993 (springer)
Back, A.D., Tsoi, A.C. ``A time series modelling methodology
using FIR and IIR synapses''. Proc Workshop on Neural Networks
for Statistical and Economic Data, Dublin, DOSES, Statistical
Office of European Communities, F. Murtagh (Ed.), pp 187 - 194,
199
K. Chakraborty, K. Mehrotra, C.K. Mohan & S. Ranka,`Forecasting
the behavior of multivariate time series using neural networks',
Neural Networks 5 (1992): 961-70.
J. Deppisch, H.U. Bauer & T. Geisel, `Hierarchical training of
neural networks and prediction of chaotic time series Physics
Letters A 158 (1991): 57-62.
J.B. Elsner, `Predicting time series using a neural network as a
method of distinguishing chaos from noise', J. of Physics A 25
(1992): 843-50.
W.R. Foster, F. Collopy & L.H. Ungar, `Neural network forecasting
of short, noisy time series', Computers & Chemical Engineering 16
(1992): 293-7.
A.R. Hoptroff, `The principles and practice of time series
forecasting and business modelling using neural nets', Neural
Computing and Applications 1 (1) (1993): 59-66.
D. McCaffrey, S. Ellner, A.R. Gallant & D. Nychka, `Estimating
the Lyapunov exponent of a chaotic system with nonparametric
regression', J. American Statistical Assoc. 87 (1992): 682-.
R.M. Miller, Computer-Aided Financial Analysis (Addison-Wesley,
1990).
D. Nychka, S. Ellner, A.R. Gallant & D. McCaffrey, `Finding chaos
in noisy systems', J. Royal Statistical Soc. B 54 (1992): 399-
426.
A.N. Refenes, `Currency exchange rate prediction and neural
network design strategies', Neural Computing and Applications 1
(1) (1993).
E. Schoenenberg, `Stock price prediction using neural networks: a
project report', Neurocomputing 2 (1990): 17-27.
A.S. Weigend & N.A. Gershenfeld, eds, Predicting the Future and
Understanding the Past: A Comparison of Approaches, Santa Fe
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XVII (Addison-Wesley, 1993, to appear).
A.S. Weigend, B.A. Huberman & D.E. Rumelhart, `Predicting the
future: a connectionist approach', International J. of Neural
Systems 1 (1990): 193-209.
A.S. Weigend, B.A. Huberman & D.E. Rumelhart, `Predicting
sunspots and exchange rates with connectionist networks', in M.
Casdagli & S. Eubank, eds, Nonlinear Modeling and Forecasting
Santa Fe Institute Studies in the Sciences of Complexity,
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H. White, `Economic prediction using neural networks: the case of
IBM daily returns', in R. Trippi & E. Turban, eds, Investment
Management, Decision Support and Expert Systems (Boyd & Fraser,
1990),\ 383-92.
D.M. Wolpert & R.C. Miall, `Detecting chaos with neural
networks', Proc. Royal Society of London B 242 (1990): 82-6.
F. Wong & P.Y. Tan, `Neural networks and genetic algorithm for
economic forecasting', available by anonymous ftp from
archive.cis.ohio-state.edu, file wong.nnga.ps.Z in
pub/neuroprose.
Y. Yamamoto & S.A. Zenios, `Predicting prepayment rates for
mortgage-backed securities using the cascade-correlation learning
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Weigend A. S., "Predicting the Future: A Connectionist Approach,"
International Journal of Neural Systems, Vol.1, No.3, 193-209
(1990)
Chakraborty K. et al., "Forecasting the Behavior of Multivariate
Time Series Using Neural Networks," Neural Networks, Vol.5, 961-
970 (1992)
Bacha H. et al., "A Neural Network Architecture for Load
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Park D. C. et al., "Electric Load Forecasting Using An Artificial
Neural Network," IEEE Trans. Power Systems, Vol.6, No.2, 442-449
(1991)
Caire P. et al., "Progress in Forecasting by Neural
Networks,"Proc. of IJCNN'92-Baltimore, Vol.II, 540-545 (1992)
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Connectionist Methods," Proc. of INNC'90-Paris, 342-345 (1990)
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Neural Network Study," Proc. of INNC'90-Paris, 357-360 (1990)
Kimoto T. et al., "Stock Market Prediction with Modular Neural
Networks," Proc. of IJCNN'90-San Diego, Vol.I, 1-6 (1990)
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357-360
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Kuan, Chung-Ming "Forecasting Exchange Rates Using Feedforward And
Recurrent Neural Networks", faculty working paper of University of
Illinois at Urbana Champaign.
Trippi and Turban "Neural Networks In Finance And Investing" available
from Finance Trading Seminar, phone 1-800-458-0939.
PC AI Magazine "Forecasting With Neural Networks", available from
Bellwood Research Center, Bellwood Avenue, Ottawa, Ontario, Canada,
K1S 1S6.
Choi, Jai J, O'Keefe, Kenneth H and Baruah, Pranab "Non-linear System
Diagnosis Using Neural Networks And Fuzzy Logic" [FUZZY IEEE 92] and
Boeing Computer Services TR B91-91.
Collard, J E "Commodity Trading With A Three Year Old" [IJCNN].
Grinzberg, I and Horn, D "Learning The Rule Of A Time Series"
International Journal Of neural Systems, volume 3 number 2/3
Murtagh, Fionn "Neural Networks For Statistical And Economic Data"
Workshop Proceeding Dublin.
Refenes, A N, Barac, M Azeman,Chen, L and Karouses, S A "Currency
Exchange Rate Prediction And Neural Networks Design Strategies" TR
Department of Computerscience University College London and Journal Of
Neurocomputing and Applications.
Scho:neburg, E "Stock Price Prediction Using Neural Networks: A
Project Report" Neurocomputing 2 pp.
Sharda, R and Patil, R B "Neural Network A Forecasting Expert: An
Empirical Test" [IJCNN90].
White, H "Economic Prediction Using Neural Networks: The Case Of IBM
Daily Stock Return" University of California, San Diego TR 88-20.
Wang, F and Tan, P Y "Neural Networks And Genetic Algorithms For
Economic Forecasting".
Lapedes, A and faber, R "Non-linear Signal Processing Using Neural
Networks: Prediction and System Modeling" Los Alamos National Laboratory
Report.