89.Dijk, Vi. W. van (1993), Neural networks and data analysis, a marketing application, EUR, Dep. of computer science. Mar. 1-109
90.Ding, Z., Granger, C. W. J. and Engle, R. F. (1993), A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1:1, 83-106
91.Doherty, C. (1990), Recurrent cascade-correlation architecture vs. the Box & Jenkins method on forecasting univariate time series. In: Murtagh, F. (ed). Proceedings PASE 1990, Neural networks for statistical and economic data, 167-177
92.Dorizzi, В., Duval, J. M. and Debar H. (1992), Utilisation de reseaux recurrents pour la prevision de consommation electrique. Proceedings ofNeuroNimes 1992
93.Dutta, S. and Shekhar, S. (1988), Bond rating: A non-conservative application of neural networks. Proceedings of the IEEE International Conference on Neural Networb, II, 443-450
94.Eaton, M. and Collins, B. J. (1990), Neural networks front end to an expertsystem for decision taking in an uncertain environment. In: Murta, F. (ed). Proceedings PASE 1990, Neural networks for statistical and economic data, 179-182
95.Economist Intelligence Unit Limited (1993a), Poland country profile 1992-1993, Economist Intelligence Unit, London, No. 1
96.Economist Intelligence Unit Limited (1993b), Poland country report. Economist Intelligence Unit, London, No. 1
97.Economist, The (1992a), Neural networks: The campaign for real neurons. The Economist, 9 May, 116
98.Economist, The (1992b), Beating the market? Yes, it can be done. The Economist, 5 Dec. 25-27
99.Economist, The (1993a), Polands economic reforms: If it works, youve fixed it. The Economist, 23 Jan. 21-25
100.Economist, The (1993b),The mathematics of markets: A survey of the frontiers of finance. The Economist, 9 Oct. 1-20
101.Eisenbeis, R. A. (1977), Pitfalls in the application of discriminant analysis in business, finance, and economies. Journal of Finance, June, 875-899
102.Errunza,V. R. (1983), Emerging markets: new opportunities for improving global portfolio performance. Financial Analysts Journal, 39, 51-58
103.Eubank, R.L. (1988), Spline Smoothing and Non-parametric Regression, NY: Marcel Dekker Inc
109.
110.
111.
118.
119.
104.Fahlman, S. E. (1988), An empirical study of learning speed in back-propagation networks. Technical Report CMU-CS-88-162, CMU
105.Fahlman, S. E. (1992), Comments on comp.ai.neural-nets, item 2198
106.Fahlman, S.E. and Lebiere, C. (1990), The cascade-correlation learning architecture. In: Touretzky, D. S. (ed). Advances in Neural Information Processing Systems, San Mateo, California: Kaufman Publishing
107.Fama,E. F. and МШег, M. H. (1972), The Theory of Finance, New York: Holt, Rinehart and Winston
108.Fama, E. F. and Gibbons, M. R. (1984), A comparison of inflation forecasts. Journal of Monetary Economics, 13, 327-348
Fase,M. M. G., Beckers, C. E., Kemna, A. G. Z. and de Wilde, S. (1990), Tussen Rokin en Damrak, de Wisselwerking Tussen de Effectenen Optiebeurs in Amsterdam, Amsterdam Stock Exchange Person, W. A. and Harvey, C. R. (1991), Amsterdam Stock Exchange Sources of predictability in portfolio returns, Finanancial Analysts Journal, May-June, 49-56
Fisher, R. A. (1936), The use of multiple measurements in taxonomic problems, Ann. Eugenics, 7, 179-188
112.Fogarty, T. C. (1991), Credit scoring and control applications of the genetic algorithm. In: Proceedings PASE 1991, Zurich Dec. Parallel problem solving-applications in statistics and economics, 147-148
113.Fogelman-Soulie, F. (1992), Neural networks: State of the art. In: 3rd International Workshop on Parallel Applications in Statistics and Economics, Prague (PASE 92), 7-8 Dec. 1-37
114.Frain, John, (1992), Complex dynamics and chaos in economies. Conference on Analysis and Forecasting of Time Series, Torino, June
115.Frane, J. W. (1977), A note on checking tolerance in matrix inversion and regression, Technometrics, 19,513-514
116.Frean, M. (1990), The Upstart algorithm: a method for constructing and training feedforward neural networks. Neural Computation, Vol. 2, 2, 198-209
117.FuUerton, Jr. T. M. (1989), A composite approach to forecasting state government revenues: Case study of the Idaho sales tax. International Journal of Forecasting 5, 37J-380
Funahashi, K. I. (1989), On the approximate realization of continous mappings by neural networks. Neural Networb 2, 183 Gabr,M. M. and Subba Rao, T. (1981), The estimation and prediction of subset bilinear time series models with applications. Journal of Time Series Analysis, 2, 155
120.Garson, G. D. (1991), Interpreting neural networks connection weights, AI Expert, 47-51
121.George, A. (1991), Qualitative analysis: Evaluating a borrowers management and business risks. The Journal of Commercial Bank Lending, Aug. 6-16
122.Ghaziri, H. EL (1991), An efficient neural network algorithm for routing problems, in Proceedings PASE 1991, Zurich, Dec. Parallel problem solving-applications in statistics and economics, 165
123.Gielen,S. and Kappen, B. (eds) (1993), Proceedings of the International Conference on Artificial Neural Networks, ICANN-93, Amsterdam, London: Springer-Verlag
124.Gifi, A. (1990), Nonlinear Multivariate Analysis, NY: John Wiley & Sons
125.GUbert,L. R., Menon, K. and Schwartz, K. B. (1990), Predicting bankruptcy for firms in financial distress. Journal of Business, Finance &(. Accounting, No. 17 (1), Spring, 161-171
126.Girosi,F. and Poggio T. (1990), Networks and the best approximation property. Biological Cybernetics, 63, 169-176
127.Gorman, R. P. and Sejnowski T. J. (1988), Analysis of hidden units in a layered network trained to classify sonar targets. Neural Networks, Vol. 1,75-89
128.Graddy, D. B. and Spencer, A. H. (1990), Managing Commercial Banks: Community, Regional and Global, Englewood Cliffs, N. J.: Prentice Hall
129.Granger, C. W. J. and Anderson, T. W. (1978), Introduction to Bilinear Time Series Models, Gottingen: Vandenhoeck und Ruprecht
130.Hakala,J., Goerke, N. and Fahner, G. (1991), HENAMnet: an alternative neural net approach for prediction of chaotic time series, in Proceedings PASE 1991, Zurich, Dec. Parallel problem solving-applications in statistics and economics, 153-155
131.Hampshire, J. B. and Pearlmutter, B. A. (1990), Equivalence proof for muhilayer perceptron classifiers and the Bayesian discriminant function, Proceedings of the 1990 Connectionist Models Summer School
132.Hanson, S. J. and Pratt, L. (1989), A comparison of different biases for minimal network construction with back-propagation. In: Touretzky, D. S. (ed). Advances in Neural Information Processing Systems, San Mateo, California: Kaufman Publishing
133.Hansson, P. A. (1991), Chaos: implications for forecasting. Futures, No. 1, 50-58
134.Harp, S. A., Samad,T. and Guha, A. (1989), Design application-specific neural networks using the genetic algorithm. In: Touretzky, D. S. (ed). Advances in Neural Information Processing Systems, San Mateo, California: Kaufman Publishing, 447-454
135.Hart, A. (1992), Using neural networks for classification tasks-some experiments on datasets and practical advice, /. Opl Res., No. 3, 215-226
136.Hawley, D. D., Johnson, J. D. and Raina, D. (1990), Artificial neural systems: A new tool for financial decision-making, Financial Analysts Journal, Nov/Dec. 63-72
137.Hecht-Nielsen, R. (1987), Kolmogorovs Mapping Neural Network Existence Theorem, Proc IEEE 1st International Conference on Neural Networks, June, San Diego, III-1 l-III-14
138.Hecht-Nielsen, R. (1991), Neurocomputing, Reading: Addison-Wesley
139.Henon, M. (1976), A Two Dimensional Mapping with a Strange Attractor, Communications in Mathematical Physics, 50
140.Hertz,!., Krogh,A. and Palmer, R. G. (1991), Introduction to the Theory of Neural Computation, Massachusetts: Addison-Wesley
141.Heskes, T. M. and Kappen, Bert (1992), Learning-parameter adjustment in neural networks, Physical Review A, A15-9a, June, 1-14
142.Hinich, M. J. and Patterson, D. M. (1985), Evidence of nonlinearity in daily stock returns. Journal of Business & Economics Statistics, 3:1, 69-77
143.Holloway, T. M. (1984), The economy and the federal budget: guides to the automatic effects. Survey of Current Business 64, July, 102-108
144.Holloway, T. M. (1989), Measuring the cyclical sensitivity of Federal receipts and expenditures: Simplified estimation procedures. International Journal of Forecasting 5, 347-360
145.Hooijmans, F. C. (1989), Controlekaarten voor het financieringstekort, Ministerie van Financien, Bureau Financiele Analyse en Planning, Onderzoeksnotitie S901, Jan.
146.Hooijmans, F. C. (1992), Weekrapport afdeling Centraal Kasbeleid, 2 oktober, Ministerie van Financien
147.Hsieh,D.A. (1989), Testing for nonlinear dependence in daily foreign exchange rates. Journal of Business, No. 3, 1989, 339-368.
148.Hsieh, D. A. (1991), Chaos and nonlinear dynamics: Application to financial markets. Journal of Finance, XLVI:5, 1839-1877
149.Hull, J- (1989), Options, Futures and other Derivative Securities, Englewood Cliffs, NJ: Prentice Hall
150.Humpert, B. (1989), Neurocomputing in financial services. Expert Systems for Information Management, No. 3, 172-199
151.Jacobs, R. A. (1988), Increased rates of convergence through learning rate adaptation. Neural Networks, Vol. 1, 295-307
152.Jensen, H.L. (1992), Using neural networks for credit scoring. Managerial Finance, No. 6, 14-26
153.Jones, L.K. (1990), Constructive approximations for neural networks by sigmoidal functions, Proc of the IEEE, No. 10, Oct. 1586-1589
154.Kamijo, K. and Tanigawa, T. (1990), Stock price pattern recognition: A recurrent network approach. Proceedings of the IEEE International Joint Conference on Neural Networks, 1215-1221
155.Karels,G. V. and Prakash,A. J. (1987), Multivariate normality and forecasting of business bankruptcy. Journal of Business Finance and Accounting 14:4, 573-593
156.Kat, H.M. (1992), Modeling S&P 500 futures misprlclng using a neural network. Financial Management Department, UvA, 28-09-93, 1-20
157.Kayama,M., Abe, S., Takenaga, H. and Morooka, Y. (1990), Constructing optimal neural networks by linear regression analysis, 364-376
158.Keasey, K. and Watson, R. (1987), Non-financial symptoms and the prediction of small company failure: A test of Argentis hypotheses. Journal of Business & Accounting, 14 (3) Autumn, 335-354
159.Keyes, J. (1990), Neural networks cant think, but they can learn-almost, Computerworld, 8 Oct.
160.Khanna, T. (1990), Foundations of Neural Networb, Massachusetts: Addison-Wesley
161.Kim,J. H. and Stringer, J. (eds) (1992), Applied Chaos, NY: John Wiley & Sons
162.Kimoto,T., Asakawa, K., Yoda, M. and Takeoda, M. (1990), Stock market predictions system with modular neural networks. Paper presented at the IJCNN, San Diego, 1-6
163.Klemic, G. G. (1990), The use of neural computing technology to develop profiles of Chapter 11 debtors who are likely to become tax
, delinquents. In: Trippi, R. R. and Turban, E. (eds) (1993), Neural Networb in Finance & Investment, Chicago: Probus Publishing
164.Knerr, S., Personnaz, L., and Dreyfus, G. (1990), Single-layer revisited: a stepwise procedure for building and training a neural network. In: Fogelman-Souille and Herault, J. (eds) Neurocomputing: Algorithms, Architectures and Applications, NATO ASI Series, Springer
165.Knoop van der, H. S. (1988), Control charts to check yearly predictions by monthly observations. Ministry of Finance, Bureau Financial Analysis and Planning, Researchmemorandum 8803, Nov.
166.Kochan, N. (1993), Warsaw advances step by step, Euromoney, Jan. 69-72
167.Kohonen, T. (1984), Self-organization and Associative Memory, Springer
168.Kohonen, T. (1988), An Introduction to neural computing. Neural Networb, Vol. 1, 1, 3-16
169.Kosko, B. (1992), Neural Networb and Fuzzy Systems, London: Prentice Hall
170.Kouam,A., Badran, F. and Thiria, S. (1992), Approche methodologique pour 1etude de la prevision a Iaide de reseaux de neurones. Proceeding ofNeuro-Ntmes 1992
171.Lapedes, A. and Farber, R. (1987), Nonlinear signal processing using neural networks: prediction and system modelling, TR LA-UR-87-2662, Los Alamos
172.Larrain,M. (1991), Testing Chaos and Nonlinearities In T-Blll Rates, Financial Analysts Journal, Sep/Oct. 51-62
173.LeCun,Y., Boser, B. and Denker, J. S. (1989), Backpropagation applied to handwritten Zip code recognition. Neural Computation, Vol. 1, 541-551
174.LeCun,Y., Denker, J. S. and Solla S. A. (1990), Optimal brain damage. In: Touretzky, D. S. (ed). Advances In Neural Information Processing Systems 2, San Mateo, California: Kaufman Publishing, 598-605
175.LeCun, Y. (1989), Constrained networks for handwritten numeral recognition. Snowbird conference on Neural Networks for Computing, Snowbird
176.Legler, J. B. and Shapiro, P. (1968), The responsiveness of state tax revenue to economic growth. National Tax Journal XXI, 46-56
177.LeRoy,S. F. (1989), Efficient capital markets and martingales. Journal of Economic Literature, XXVII, 1583-1621
178.Levy, H. and Sarnat, M. (1970), International diversification of investment portfolios, American Economic Review, Sep. 668-692