назад Оглавление вперед


[Старт] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [ 40 ] [41] [42]


40

179.Lewis, Р. А. W. and Stevens,]. G. (1991), Nonlinear modeling of time series using multivariate adaptive regression splines (MARS), Journal of American Statistics Association, 86, 864

180.Libby, R. (1975), Accounting ratios and the prediction of failure: Some behavioral evidence. Journal of Accounting Research, Spring, 150-161

181.Lin, F. C. and Lin, M. (1993), Analysis of financial data using neural nets, AI Expert, 36-41

182.Lintner, J. (1965), The valuation of risk assets and the selection of risky investments in stocks, portfolios and capital budgets, Review of Economics and Statistics, 47:1, 13-37

183.Lippman, R. P., (1987), An introduction to computing with neural nets, IEEE ASSP Magazine, April, 4-22

184.MacKay, D. J. C. (1991), A practical bayesian framework for backprop networks, submitted to Neural Computation

185.Manaster, S. and Rendleman, Jr. R. J. (1982), Option prices as predictors of equilibrium stock prices. Journal of Finance, 37, 1043-1057

186.Mandelbrot, B. B. (1971), When can price be arbitraged efficiently? A limit to the validity of the random walk and martingale models. Review of Economics and Statistics, Llll, 225-236

187.Markowitz, H. M. (1952), Portfolio Selection, Journal of Finance, 7, 77-91

188.Markowitz, H. M. (1959), Portfolio Selection, Efficient Diversification of Investments, NY: John Wiley

189.Maskara, A. and Noetzel, A. (1992), Forced simple recurrent neural networks and grammatical inference, preprint

190.McClelland, J. L. and Rumelhart, D. E. (eds) (1986), Parallel Distributed Processing: Explorations in the Micro structure of Cognition. Volume 2: Psychological and Biological Models, Massachusetts: MIT Press

191.McClelland, J. L. and Rumelhart, D. E. (eds) (1988), Explorations in Parallel Distributed Processing: Computational Models of Cognition and Perceptron (software manual), Massachusetts: MIT Press

192.McCord Nelson, M. and Illongworth, W. T. (1991), A Practical Guide to Neural Nets, Massachusetts: Addison-Wesley

193.Mei, J. (1993), A Semiautoregression approach to the arbitrage pricing theory. Journal of Finance, 48, 599-620

194.Mezard, М. and Nadal, J. P. (1989), Learning in feedforward networks: the tiling algorithm, Journal of Physics A, Vol. 22, 12, 2191-2203

195.Millard, B. J. (1993), Winning on the Stock Market, Chichester: John Wiley & Sons

196.Minsky, M. L. and Papert, S. A. (1988), Perceptrons: Expanded Edition, Massachusetts: MIT Press

197.MoUer, M. (1993), A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, Vol. 6, 525-533

198.Moody, J.E. (1992), The effective number of parameters: An analysis of generalization and regularization in nonlinear learning systems, ANIPS 4, 847-854

199.Moody, J.E., Levin, U. and Rehfuss, S. (1993), Predicting the US index of industrial production. Neural Network World, 3:6, 791-794

200.Miiller, A. and Neumann, J. (1990), Classification with neural networks. In: Bock, H. H. and Ihnl, P. (eds). Classification, Data Analysis, and Knowledge Organisation, 32-43

201.Murtagh, F. (1990), A short survey of neural networks for forecasting and related problems. In: Murtagh, F. (ed), PASE 1990, Neural networks for statistical and economic data, 87

202.Nazmi, N. and Leuthold, J. H. (1988), Forecasting economic time series that require a power transformation: case of state tax receipts. Journal of Forecasting, Vol. 7, 173-184

203.Neural networks: Theory and practice, (1989) Byte, Aug. 244-245

204.Nichols, N. A. (1993), Efficient? Chaotic? Whats the new finance. Harvard Business Review, Mar-Apr. 50-60

205.Niranjan, Dr Mahesan (1992), Programming a neural network. Neural network summer school, Department of Engineering, University of Cambridge, 7-11 Sep. 1-6

206.Numan, H. G. K. (1990), Implied volatility: Niet recht maar krom, Het Financieele Dagblad, 1 Oct.

207.Openshaw, Stan and Wymer, C. (1990), A neural net classifier system for handling census data. In: Murtagh, F. (ed), PASE 1990, Neural networks for statistical and economic data, 73-85

208.Ormerod, P. and Walker, T. (1990), Neural networb and the monetary base in Switzerland. In: Murtagh, F. (ed), PASE 1990, Neural networks for statistical and economic data, 71

209.Ormerod, P. and Walker, T. (1993), Macroeconomic njo4elling of complex systems. Neural Network World, 3:6, 795-814



210.Panton, D. (1976), Chicago board call options as predictors of common stock price changes. Journal of Econometrics, 101-113

211.Papadourakis, G. M., Spanoudakis, G. and Gotsias, A. (1991), Short-term stock price forecasting using neural netwoks. In: Wurtz, D. and Murtagh, F. (eds) (1991), Proceedings International Workshop on Parallel Problem Solving From Nature: Applications in Statistics and Economics PASE-91, Zurich, 151

212.Pearlmutter, B. A. (1993), Fast exact multiplication by the hessian, to appear in Neural Computation

213.Peters, E.E. (1989), Fractal structure in the capital markets. Financial Analysts Journal, Jul/Aug. 32-37

214.Peters, E. E. (1991a), Chaos and Order in the Capital Markets, A New View of Cycles, Prices and Market Volatility, New York: John Wiley & Sons

215.Peters, E. E. (1991b), A chaotic attractor for the S&P 500, Financial Analysts Journal, Mar-Apr. 55-62

216.Peters, E. E. (1994), Fractal Market Analysis, NY: Wiley

217.Piesse, J. and Wood, D. (1992), Issues in assessing MDA models of corporate failure: A research note, British Accounting Review, Vol. 24, 33-42

218.Poprzeczko, J. (1993), Our expensive credit, Polityka Financial Supplement, No. 3, March

219.Press, W. H., Flannery, B. P., Teukolsky, S. A. and Vetterling, W. T. (1992), Numerical Redpes in C, Cambridge: CUP

220.Priestley, M. B. (1980), State-dependent models: A general approach to non-linear time series analysis. Journal of Time Series Analysis, Vol. 1, 1

221.Priestley, M. B. (1988), Non-linear and Non-stationary Time Series Analysis, London: Academic Press

222.Quinlan, J. R. (1979), Discovering rules by induction from large classes of examples. In: Michie, D. (ed). Expert Systems in the Microelectronic Age, Edinburgh: Edinburgh University Press

Raghupathi, W., Schkade, L L. and Raju, B. S. (1991), A neural network approach to bankruptcy prediction. In: Trippi, R. R. and Turban, E. (eds) (1993), Neural Networb in Finance & Investment, •• Chicago: Probus Publishing Company, 141-158 224. Ramanathan, Ramu (1989), Introductory Econometrics with "i" Applications, San Diego: Harcourt Brace Jovanovich

225.Refenes,A. N. (ed) (1993), NnCM93: Proceedings of the first International Workshop on Neural Networb in the Capital Markets, London Business School, 18-19 Nov.

226.Richardson, F. M. and Davidson, L. F. (1983), An exploration into bankruptcy discriminant model sensitivity. Journal of Business Finance and Accounting, 10:2, 195-207

227.Ripley, B. D. (1992), Statistical aspects of neural networks, SemStat 1992, 21 June, 1-70

228.Ripley, B.D. (1993a), Neural networks and related methods for classification, preprint

229.Ripley, B.D. (1993b), Statistical aspects of neural networks. In: Barndorff-Nielsen, Jensen, J. L. and Kendall, W. S. (eds), Networb and Chaos: Statistical and Probabilistic Aspects, London: Chapman & Hall, 40-123

230.Ripley, B. D. (1993c), Neural networks and flexible regression and discrimination. In: Mardia, K. У, (ed) Statistics And Images, Abingdon: Carfax, 1-24

231.Rohwer, R. (1990), Neural networks for time-varying data. In: Murtagh, F. (ed), PASE 1990, Neural networks for statistical and economic data, 59-69

232.Ross, S. A. and Walsh, M. (1983), A simple approach to the pricing of risky assets with uncertain exchange rates. In: Hawkins, R. Levich, R. and Wihlberg, C. (eds) The Internationalization of Financial Markets and National Economic Policy, Greenvkdch: JAI Press

233.Ross, S. A. (1976), The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13, 341-360

234.Rumelhart, D. E. and McClelland, J. L. (eds) (1986), Parallel Distributed Processing Explorations in the Microstructure of Cognition. Volume 1: Foundations, Massachusetts: MIT Press

235.Sanger, Terence D. (1989), Optimal unsupervised learning in single-layer linear feedforward neural network, Neural Networks, 459-473

236.Saunders, Anthony (1987), The inter-bank market, contagion effects and international financial crises. In: Portes and Swoboda (eds). Threats to International Financial Stability, CEPR, New York: Cambridge University Press

237.Savit, R. (1992), Chaos on the Trading Floor. In: Nina Hall (ed). The New Scientist Guide to Chaos, London: Penguin

238.Schalkoff, R. (1992), Pattern Recognition: Statistical, Structural and Neural Approaches, NY: John Wiley & Sons Inc



239.Scheinkman, J. А. and LeBaron, В. (1989), Nonlinear dynamics and stock returns. Journal of Business, No. 3, 311-337

240.Schiffman, W., Joost, M. and Werner, R. (1992), Optimization of the backpropagation algorithm for training multilayer perceptrons, the neuroprose archive, anonymous FTP; cheops.ohio-state.edu

241.Sharpe, W. F. (1964), Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19, 425-442

242.Shkurti,W. J. and Winefordner, D. (1989),The politics of state revenue forecasting in Ohio, 1984-1987: A case study and research implications. International Journal of Forecasting 5, 361-371

243.Simon, H. (1982), Modek of Bounded Rationality, Cambridge: MIT Press

244.Sirat, J. A. and Nadal, J. P. (1990), Neural trees: a new tool for classification. Network Vol. 1, 423-438

245.Smith, C.J. (1991) A neural network- could it work for you. Financial Executive, May/June, 26-30

246.Smith, M. (1991), Neural networks: Do they compute?. Bests Review, 70-74

247.Smith, M.(1993), Neural Networks for Statistical Modelling, NY: VNR

248.Speidel, L. S. and Sappenfield, R. (1992), Global diversification in a shrinking world. Journal of Portfolio Management, 19, 57-67

249.Stephan, J. A. and Whaley, R. E. (1990), Intraday price change and trading volume relations in the stock and stock options market. Journal of Finance, 55, 191-220

250.Stein, R. (1993a), Preprocessing data for neural networks, AI Expert, 32-37

251.Stein, R. (1993b), Selecting data for neural networks, AI Expert, 42-47

252.Sterge, A. J. (1989), On the distribution of financial futures price changes. Financial Analyst Journal, May/June

253.Still, D.B. (1991), A philosophical foundation for credit analysis. The Journal of Commercial Bank Lending, Nov. 43-47

254.Sugihara, G. and May, R. M. (1990), Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series,

LNature, 19 Apr. 734-741

Surkan, A. and Singleton, J. (1991), Neural networks for bond raring ЛЧ; improved by multiple hidden layers. In: Proceedings of the IEEE International Conference on Neural Networks, Jul. II 157-162

256.Taffler, R. J. (1982). Forecasting company failure in the UK using discriminant analysis and financial ratio data. Journal of the Royal Statistical Society, Series A, Vol. 145, Part 3, 342-358

257.Taffler, R.J. (1984), Empirical models for the monitoring of UK corporations. Journal of Banking and Finance, 199-227

258.Taffler, R.J. and Tseung, M. (1984), The audit going concern in practice. The Accounting Magazine, July

259.Tam, K. Y. and Kiang, M. (1990), Predicting bank failures: a neural network approach. Applied Artificial Intelligence; an International Journal, Vol. 4, No. 4, 265-282

260.Thompson, J. M. T. and Stewart, H. B. (1991), Nonlinear Dynamics and Chaos, NY: John Wiley & Sons

261.Tong, H. (1983), Threshold Models in Non-linear Time Series Analysis, Lectures Notes Statistics, Vol. 21, Springer

262.Toussaint, G. T. (1974), Bibliography on estimation of misclassification, IEEE Transactions en Information Theory, Vol. 20, 4, 472-479

263.Treleaven,P. and Goonatilake, S. (1991), Intelligent financial technologies. In: Wurtz, D. and Murtagh, F. (eds). Proceedings International Workshop on Parallel Problem Solving From Nature: Applications in Statistics and Economics PASE-91, Zurich, 7-26

264; Trigueros, D. and Berry, R. H. (1991), Applying neural networks to the extraction of knowledge from accounting reports: A classification • i. study, School of Information Systems, University of East Anglia, Norwich, NR4 7TJ, Mar. 1-28

265.Trippi, R. R. and DeSieno, D. (1992), Trading equity index futures vwth neural network. The Journal of Portfolio Management, Fall, 27-33

266.Trippi, R.R. and Turban, E. (eds) (1993), Neural Networks in Finance & Investment, Chicago: Probus Publishing Company

267.Trzcinka, C. (1986), On the number of factors in the arbitrage pricing model. Journal of Finance, 41, 347-368

268.Tucker, A. L, Madura, J. and Chiang, T. C. (1991), International Financial Markets West, St Paul, 167

269.Unnikrishnan, K. P. and Venugopal, K. P. (1993), Alopex: a correlation based learning algorithm for feed-forward and recurrent neural networks. Technical Report GMR-7919

270.Utans, J. and Moody, J. (1991), Selecting neural network architectures via the prediction risk: Application to corporate bond

[Старт] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [ 40 ] [41] [42]