<<
>>

Библиография

Русскоязычные источники:

1. Арнольд В.И. Теория катастроф. - 3-е изд., доп. - М.: Наука, Главная редакция физико-математической литературы, 1990. - 128 с.

2. Берзон Н.И. Фондовый рынок: Учеб. Пособие для высш.

учебн. зав. экон. профиля/ Гос. унив. - Высшая Школа Экономики. Высшая Школа менеджмента./ Н.И.Берзон, А.Ю. Аршавский, Е.А.Буянова, А.С. Красильщиков. Под ред. Н.И.Берзона - 4-е изд., перераб. и доп. - М.: ВИТА-ПРЕСС, 2009. - 624 с.: ил.

3. Быкадоров Р.В., Воронин С.Ю. Вероятностные методы расчета технологического процесса ткачества. Иваново, ИГТА, 2006. - 108 с.

4. Вилленброк Х. Тайны принятия решений// GEO, июль 2009, с. 70-87.

5. Воробьев Н.Н. Теория игр для экономистов-кибернетиков - М.: Наука, Главная редакция физико-математической литературы, 1985. - 272 с.

6. Вороновский Г. К., Махотило К. В., Петрашев С. Н., Сергеев С. А. Генетические алгоритмы, искусственные нейронные сети и проблемы виртуальной реальности. — Харьков: Основа, 1997. — 112 с.

7. Евстигнеев В.Р. Прогнозирование доходности на рынке акций. - М.: Маросейка, 2009. - 192 с.

8. Ежов А. А., Шумский С.А. Нейрокомпьютинг и его применения в экономике и бизнесе: Курс лекций //М.: МИФИ. - 1998.

9. Жук В.В., Натансон Г.И. Тригонометрические ряды Фурье и элементы теории аппроксимации. — Л.: Изд-во Ленингр. ун-та, 1983. - 187 с.

10. Круглов В.В., Борисов В.В. Искусственные нейронные сети: Теория и практика. - М.: Горячая линия-Телеком, 2002 - 382 с.: ил.

11. Малинецкий Г.А., Потапов А.Б. Современные проблемы нелинейной динамики. - М.:Эдиториал УРСС, 2000. - 366 с.

12. Малинецкий Г.А., Потапов А.Б., Подлазов А.В. Нелинейная динамика: Подходы, надежды, результаты, надежды. - М.: КомКнига, 2006. - 280 с.

13. Минский М. Л., Пейперт С. Персептроны. - М. Мир. - 1971.

14. Метерлинк М. Тайная жизнь термитов. - М.: Изд-во ЭКСМО-Пресс, 2002. - 400 с.

15. Никульчев Е.В., Волович М.Е. Модели хаоса для процессов изменения курса акций.// «Exponenta pro. Математика в приложениях», №1(1), 2003, с. 49-52.

16. Пригожин И., Стенгерс И. Время. Хаос. Квант: К решении. Парадокса времени. Пер. с англ./ Под ред. В.И. Аршинова. Изд. 7-е. - М.: Книжный дом «Либроком», 2009 - 232 с.

17. Рубцов Б.Б. Мировые рынки ценных бумаг. - М.: «Издательство «Экзамен», 2002. - 448 с.

18. Уоссермен Ф. Нейрокомпьютерная техника: Теория и практика. — М.: Мир, 1992. — 240 с.

19. Хайкин С. Нейронные сети: полный курс, 2e издание. : Пер. с англ. М. Издательский дом Вильямс", 2006 - 1104 с.: ил.

20. Хакен Г. Принципы работы головного мозга: Синергетический подход к работе мозга, поведению и когнитивной деятельности. - М.: ПЭР СЭ, 2001. - 351 с.

Иностранные источники:

21. Abbod, M., &Deshpande, K. (2008). Using intelligent optimization methods to improve the group method of data handling in time series prediction. In Computational Science-ICCS 2008 (pp. 16-25). Springer Berlin Heidelberg.

22. Abdalla, I., Murinde, V. Exchange Rate and Stock Price Interactions in Emerging Financial Markets: Evidence on India, Korea, Pakistan and the Philippines, Applied Financial Economics, 7, 25-35, 1997.

23. Adya, M., &Collopy, F. (1998). How effective are neural networks at forecasting and prediction? A review and evaluation.

J. Forecasting, 17, 481­495.

24. Alam, M. R., Muttaqi, K. M., & Bouzerdoum, A. (2012, June). A short length window-based method for islanding detection in distributed generation. In Neural Networks (IJCNN), The 2012 International Joint Conference on (pp. 1­6). IEEE.

25. Amin M.S., Mamun M., Hashim F.H., Jalil J., Husain H. Design and Implementation of Novel Artificial Neural Network Based Stock Market Forecasting System on Field-Programmable Gate Arrays. American Journal of Applied Sciences 8 (10): 1054-1060, 2011.

26. Andrighetto, G., Rome, I. C., & Verhagen, H. Social Networks and Multi­Agent Systems Symposium (SNAMAS-09), 2009.

27. Ang, A., & Timmermann, A. (2011). Regime changes and financial markets (No. w17182). National Bureau of Economic Research.

28. Alfarano S., Lux T., Wagner F. Excess Volatility and Herding in an Artificial Financial Market: Analytical Approach and Estimation// University of Kiel, 2010.

29. Alfarano S, Lux T., Wagner F. Estimation of agent-based models: the case of an asymmetric herding model. Computational Economics, 26:19-49, 2005/

30. Aoki M. Modeling Aggregate Behavior and Fluctuations in Economics. University Press, Cambridge, 2004.

31. Arthur W. B. Complexity in Economic and Financial Markets," Complexity, Vol. 1, No. 1, 1995, pp. 20-25.

32. Asadi, R., Mustapha, N., & Sulaiman, N. (2009). A framework for intelligent multi agent system based neural network classification model. arXiv preprint arXiv:0910.2029.

33. Asness, C. (2003). Fight the Fed model: the relationship between stock market yields, bond market yields, and future returns. Bond Market Yields, and Future Returns (December 2002).

34. Atsalakis, G. S., &Valavanis, K. P. (2009). Surveying stock market forecasting techniques-Part II: Soft computing methods. Expert Systems with Applications, 36(3), 5932-5941.

35. Azoff, E. M. (1994). Neural network time series forecasting of financial markets. John Wiley & Sons, Inc.

36. Back A.D., Weigend A.S. A First Application of Independent Component Analysisto Extracting Structure from Stock Returns// International Journal of Neural Systems, Vol. 8, No.5 (October, 1997).

37. Bak, P., Paczuski, M., & Shubik, M. (1997). Price variations in a stock market with many agents. Physica A: Statistical Mechanics and its Applications, 246(3), 430-453.

38. Baraviera A.T., Bazzan A.L.C., da Silva R. Emerging Collective Behavior in a Simple Artificial Financial Market// S'lvio R. Dahmen nstituto de F'sica, UFRGS, 2005.

39. Barberis N., Shleifer A. Vishny R. A Model of Investor Sentiment. Journal of Financial Economics, 49 (3): 307-343, 1998.

40. Bastos, J. (2010). Predicting bank loan recovery rates with neural networks (No. 1003). Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

41. Baek, K., Draper, B. A., Beveridge, J. R., & She, K. (2002, March). PCA vs. ICA: A Comparison on the FERET Data Set. In JCIS (pp. 824-827).

42. Bell J.I., Sejnowsi T. J. An information-maximisation approach to blind separation and blind deconvolution//Neural Computation, 7, 6, 1004-1034 (1995).

43. Bellman R. Dynamic Programming. Dover Publications, 2003.

44. Bishop C.M. Neural Networks for Pattern Recognition. Oxford University Press, 1995 - 483 p.

45. Bhat, H. S., &Zaelit, D. (2013). Forecasting retained earnings of privately held companies with PCA and L1 regression. Applied Stochastic Models in Business and Industry.

46. Bloomfield, R., & Hales, J. (2002). Predicting the next step of a random walk: experimental evidence of regime-shifting beliefs. Journal of Financial Economics, 65(3), 397-414.

47. Bloomfield, R., Libby, R., & Nelson, M. (1998). Underreactions and Overreactions: The Influence of Information Reliability and Portfolio Formation Rules. Available at SSRN 132168.

48. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8.

49. Bonabeau E., Sobkowski A., Theraulaz G., Deneubourg J.-L. Adaptive Task Allocation Inspired by a Model of Division of Labor in Social Insects, 1998.

50. Bronkhorst A. W. "The cocktail party phenomenon: A review of research on speech intelligibility in multiple-talker conditions." Acta Acustica united with Acustica 86.1 (2000): 117-128.

51. Bronstein, Alexander M., Michael M. Bronstein, and Michael Zibulevsky. Blind deconvolution using the relative Newton method. Independent Component Analysis and Blind Signal Separation. Springer Berlin Heidelberg, 2004. 554-561.

52. Brunnermeier, M. K. (2003). Asset pricing under asymmetric information: Bubbles, crashes, technical analysis, and herding. Oxford University Press.

53. Burbea J., Rao C.R. On the convexity of some divergence measures based on entropy functions, IEEE Trans. Information Theory, Vol. 28, 1982.

54. Burkhart, R., Langton, C., & Askenazi, M. (1996, June). The swarm simulation system: A toolkit for building multi-agent simulations. Santa Fe: Santa Fe Institute.

55. Cao, L. J., Chua, K. S., Chong, W. K., Lee, H. P., & Gu, Q. M. (2003). A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing, 55(1), 321-336.

56. Chan T. Artificial markets and intelligent agents (Doctoral dissertation, Massachusetts Institute of Technology), 2001.

57. Cheng, L., Hou, Z. G., Tan, M., Lin, Y., & Zhang, W. (2010). Neural- network-based adaptive leader-following control for multiagent systems with uncertainties. Neural Networks, IEEE Transactions on, 21(8), 1351-1358.

58. Cherubini, U., Della Lunga, G., Mulinacci, S., & Rossi, P. (2010). Fourier transform methods in finance (Vol. 524). Wiley. com.

59. Chiang, Y. W., Borbat, P. P., & Freed, J. H. (2005). Maximum entropy: A complement to Tikhonov regularization for determination of pair distance distributions by pulsed ESR. Journal of Magnetic Resonance, 177(2), 184-196.

60. Choi Y, Duady R. Chaos and Bifurcation in 2007-09+ Financial Crisis// CNRS November 8, 2010.

61. Connor, J. T., Martin, R. D., & Atlas, L. E. (1994). Recurrent neural networks and robust time series prediction. Neural Networks, IEEE Transactions on, 5(2), 240-254.

62. Cont R., Bochaude J.-P. Herd Behavior and Aggregate Fluctuations In Financial Markets// Macroeconomic Dynamics, 4, 2000, 170-196.

63. Coste, C., Douady, R., & Zovko, I. (2009). The StressVaR: A New Risk Concept for Extreme Risk and Fund Allocation. Available at SSRN 1509503.

64. Daniel K., Hirshleifer D., Subrahmanyam A. Investor Psychology and Security Market Under- and Overreactions. Journal of Finance 53 (6): 1839­1885, 1998.

65. De Bondt W.F.M., Thaler R.H. Anomalies: A Mean-Reverting Walk Down Wall Street. Journal of Economic Perspectives, 3(1): 189-202, 1989.

66. Delac, K., Grgic, M., & Grgic, S. (2005, July). A comparative study of PCA, ICA, and LDA. In Proc. of the 5th EURASIP Conference focused on Speech and Image Processing, Multimedia Communications and Services (pp. 99­106).

67. Domingos P. Bayesian Averaging of Classifiers and the Overfitting Problem Proceeding ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning, p. 223-230, 2000.

68. Dorffner, G. (1996). Neural networks for time series processing. In Neural Network World.

69. Dorigo, M., Trianni, V., Sahin, E., Groβ, R., Labella, T. H., Baldassarre, G., ... & Gambardella, L. M. (2004). Evolving self-organizing behaviors for a swarm-bot. Autonomous Robots, 17(2-3), 223-245.

70. Draper, B. A., Baek, K., Bartlett, M. S., & Beveridge, J. R. (2003). Recognizing faces with PCA and ICA. Computer vision and image understanding, 91(1), 115-137.

71. Dubovikov N.N., Starchenko N.S., Dubikov M.S. Dimension of the minimal cover and fractal analysis of time series//Physica A 339 (2004) 591 - 608.

72. Dueker, M. (1997). Strengthening the Case for the Yield Curve as a Predictor of US Recessions. Federal Reserve Bank of St. Louis Review, (Mar), 41-51.

73. Dunis, C., & Williams, M. (2002). 'Modelling and Trading the EUR/USD Exchange Rate: Do Neural Network Models Perform Better?'. Derivatives use, trading and regulation, 8(3), 211-239.

74. Durre A., Giot P. Endorse or fight the Fed model? An international analysis of earnings, stock prices and bond yields, 2004.

75. Elster, J. (1989). Nuts and bolts for the social sciences (p. 13). Cambridge: Cambridge University Press.

76. Enke, D., & Thawornwong, S. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications, 29(4), 927-940.

77. Estrella, A., & Trubin, M. (2006). The yield curve as a leading indicator: some practical issues. Current Issues in Economics and Finance, 12(5).

78. Fama E.F. Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, Volume 25, Issue 2, pages 383-417, May 1970.

79. Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery and data mining.

80. Ferber J. Multi-Agent Systems: An Introduction to Artificial Intelligence. Addison-Wesley, 1999.

81. Filis, G., Kentzoglanakis, K., & Floros, C. (2009). VAR model training using particle swarm optimisation: evidence from macro-finance data. International Journal of Computational Economics and Econometrics, 1(1), 9-22.

82. Friedman J. H., Tukey J. W. A Projection Pursuit Algorithm for Exploratory Data Analysis. IEEE Transactions on Computers C-23 (9): 881-890, September 1974.

83. Frith C. Making up the Mind: How the Brain Creates Our Mental World. Wiley, 2007.

84. Fukumizu, K., Bach, F. R., & Jordan, M. I. (2003). Kernel dimensionality reduction for supervised learning. Advances in Neural Information Processing Systems, 16.

85. Fukunaga, K. (1990). Introduction to statistical pattern recognition. Access Online via Elsevier.

86. Fyfe C. Artificial Neural Networks and Information Theory. Department of Computing and Information Systems, The University of Paisley, 2000.

87. Gan, W. S., & Ng, K. H. (1995, November). Multivariate FOREX forecasting using artificial neural networks. In Neural Networks, 1995. Proceedings., IEEE International Conference on (Vol. 2, pp. 1018-1022). IEEE.

88. Garnier S., Combe M., Jost C., Theraulaz G. Do Ants Need to Estimate the Geometrical Properties of Trail Bifurcations to Find an Efficient Route? A Swarm Robotics Test Bed. PLoS Comput Biol 9(3), 2013.

89. Ghatge, A. R., & Halkarnikar, P. P. Ensemble Neural Network Strategy for Predicting Credit Default Evaluation. International Journal of Engineering and Innovative Technology (IJEIT), Volume 2, Issue 7, January 2013.

90. Gilbert N. Agent-Based Models. University of Surrey, Guildford, 2007.

91. Glaser, M., Langer, T., & Weber, M. (2007). On the trend recognition and forecasting ability of professional traders. Decision Analysis, 4(4), 176-193.

92. Gorriz, J. M., Luna, J. C. S., Puntonet, C. G., & Salmeron, M. (2005). A Survey of Forecasting Preprocessing Techniques using RNs. Informatica (Slovenia), 29(1), 13-32.

93. Gorriz J.M., Puntonet C.G., Moises Salmeron, E.W. Lang Time Series Prediction using ICA Algorithms//IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications 8-10 September 2003.

94. Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American economic review, 70(3), 393­408.

95. Grothmann, R. Multi-agent market modeling based on neural networks. Faculty of Economics, University of Bremen, 2002.

96. Gurney K. An Introduction to Neural Networks. London: Routledge, 1997.

97. Hageman, L. A., & Young, D. M. (2012). Applied iterative methods. Dover Publications.

98. Haken G. Synergetics, an Introduction: Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and Biology", 3rd rev. enl. ed. New York: Springer-Verlag, 1983.

99. Hassan, M. R., & Nath, B. (2005, September). Stock market forecasting using hidden Markov model: a new approach. In Intelligent Systems Design and Applications, 2005. ISDA'05. Proceedings. 5th International Conference on (pp. 192-196). IEEE.

100. Hebb D.O. The Organization of Behavior: A Neuropsychological Theory. — Wiley, 1949.

101. Henri B., Gerlach S. Does the Term Structure Predict Recessions?: the International Evidence. No. 1892. Centre for Economic Policy Research, 1998.

102. Hillebrand E. Mean Reversion Expectations and the 1987 Stock Market Crash: An Empirical Investigation. Finance 0501015, EconWPA, 2005.

103. Hong, T., & Han, I. (2002). Knowledge-based data mining of news information on the Internet using cognitive maps and neural networks. Expert systems with applications, 23(1), 1-8.

104. Hoya, T., Hori, G., Bakardjian, H., Nishimura, T., Suzuki, T., Miyawaki,

Y., ... & Cao, J. (2003, January). Classification of single trial EEG signals by a combined principal+ independent component analysis and probabilistic neural network approach. In International Symposium on Independent Component Analysis and Blind Signal Separation (pp. 197-202).Hirshleifer, D. (2001). Investor psychology and asset pricing. The Journal of Finance, 56(4), 1533­1597.

105. Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 417-441, and 498-520.

106. Huang, W., Lai, K. K., Nakamori, Y., & Wang, S. (2004). Forecasting foreign exchange rates with artificial neural networks: a review. International Journal of Information Technology & Decision Making, 3(01), 145-165.

107. Huang, W., Nakamori, Y., & Wang, S. Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513-2522.

108. Hyvarinen A., Oja E. Independent Component Analysis: Algorithms and Applications//Neural Networks, 13(4-5):411-430, 2000.

109. Islami, M. (2011). Interdependence between foreign exchange markets and stock markets in selected European countries (pp. 27-48). Springer Berlin Heidelberg.

110. Jiang J.-Q., Wei-Xing Zhou W.-X., Sornette D., Woodard R., Bastiaensen K., Cauwels P.Bubble Diagnosis and Prediction of the 2005-2007 and 2008­2009 Chinese stock market bubbles, Journal of Economic Behavior & Organization 74 (3), 149-162 (2010).

111. Joho M., Mathis H., Moschytz G.S. An Fft-Based Algorithm For Multichannel Blind Deconvolution, Signal and Information Processing Laboratory, Swiss Federal Institute of Technology Zurich, 1999.

112. Jolliffe I.T. Principal Component Analysis, Springer-Verlag (2nd Ed.), 2002.

113. Juang, C. F., & Lin, C. T. (1999). A recurrent self-organizing neural fuzzy inference network. Neural Networks, IEEE Transactions on, 10(4), 828-845.

114. Kaastra, I., & Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215-236.

115. Kahneman D.,Tversky A. Prospect Theory: An Analysis of Decision Under Risk, Econometrica, Volume 47, Issue 2 (Mar.,1979), p. 263-292.

116. Kendall G., Su Y. A Particle Swarm Optimisation Approach In The Construction of Optimal Risky Portfolios// Proceedings of the 23rd IASTED International Multi-Conference Artificial Intelligence and Applications February 14-16, 2005, Innsbruck, Austria.

117. Kendrick, D. A. (2006). Computational economics. Princeton University Press.

118. Kevin, I., Wang, K., Abdulla, W. H., & Salcic, Z. (2007). Multi-agent system with hybrid intelligence using neural network and fuzzy inference techniques. In New Trends in Applied Artificial Intelligence (pp. 473-482). Springer Berlin Heidelberg.

119. Kim, K. J., & Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert systems with applications, 19(2), 125-132.

120. Kim, S. H., & Hak Chun, S. (1998). Graded forecasting using an array of bipolar predictions: application of probabilistic neural networks to a stock market index. International Journal of Forecasting, 14(3), 323-337.

121. Kimoto, T., Asakawa, K., Yoda, M., & Takeoka, M. (1990, June). Stock market prediction system with modular neural networks. In Neural Networks, 1990., 1990 IJCNN International Joint Conference on (pp. 1-6). IEEE.

122. Коііопєп Т. "Self-organized formation of topologically ∞rrect feature maps", Bio1ogical Cybernetics, 1982, vo1. 43, р. 59-69.

123. Kohonen Т. "Exploration of very large databases Ьу self-organizing maps", 1997, Interactional Conference опNeural Networks, 1997, vol. 1, р. PL1-PL6, Houston.

124. Коііопєп Т. "Self-organized formation of topologically ∞rrect feature maps", Biological Cybernetics, 1982, vol. 43, р. 59--69.

125. Kohonen Т. "The self-organizing mар", Proceedings of the Institute of Electrical and Electronics Engineers, 1990, vo1. 78, р. 1464-1480.

126. Krink, T., VesterstrOm, J. S., & Riget, J. (2002). Particle swarm optimisation with spatial particle extension. In Evolutionary Computation, 2002. CEC'02. Proceedings of the 2002 Congress on (Vol. 2, pp. 1474-1479). IEEE.

127. Kuan, C. M., & Liu, T. (1995). Forecasting exchange rates using feedforward and recurrent neural networks. Journal of Applied Econometrics, 10(4), 347-364.

128. Kullback S., Leibler R.A. On Information and sufficiency, Annals of Mathematical Statistics 22 (1), 1951, p. 79-86.

129. Krose B., van der Smagt P. An Introduction To Neural Networks, Eight Edition, November 1996.

130. Kwak, N., Kim, C., & Kim, H. (2008). Dimensionality reduction based on ICA for regression problems. Neurocomputing, 71(13), 2596-2603.

131. Kwon Y.-K., Moon B.-R. A Hybrid Neurogenetic Approach for Stock Forecasting. IEEE Transactions On Neural Networks, vol. 18, no. 3, may 2007.

132. Lai, K. K., Yu, L., & Wang, S. (2005). A neural network and web-based decision support system for forex forecasting and trading. In Data Mining and Knowledge Management (pp. 243-253). Springer Berlin Heidelberg.

133. Lawrence J. Introduction to Neural Networks. California Scientific Software Press, 1994.

134. Lawrence, S., Giles, C. L., & Tsoi, A. C. (1997, July). Lessons in neural network training: Overfitting may be harder than expected. In AAAI/IAAI (pp. 540-545).

135. LeBaron B. A Builder's Guide to Agent Based Financial Markets// Brandeis University, February 2001.

136. LeBaron B. Agent Based Computational Finance: Suggested Readings and Early Research// Graduate School of International Economics and Finance Brandeis University, October 1998.

137. LeBaron B. Agent-Based Computational Finance. Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland/Elsevier, Amsterdam, Spring 2006.

138. LeBaron B. Building the Santa Fe Artificial Stock Market// Brandeis University, June 2002.

139. Lee, I. H. (1998). Market crashes and informational avalanches. The Review of Economic Studies, 65(4), 741-759.

140. Leng, G., Prasad, G., & McGinnity, T. M. (2004). An on-line algorithm for creating self-organizing fuzzy neural networks. Neural Networks, 17(10), 1477-1493.

141. Lin, J. C., Li, Y. H., & Liu, C. H. (2007, July). Building Time Series Forecasting Model By Independent Component Analysis Mechanism. In World Congress on Engineering (pp. 1010-1015).

142. Liu, H., & Wang, J. (2011). Integrating independent component analysis and principal component analysis with neural network to predict Chinese stock market. Mathematical Problems in Engineering, 2011.

143. Liu Y., Starzhyk J.A., Zhu Z. Optimized approximation algorithm in neural networks without overfitting. Journal IEEE Transactions on Neural Networks Volume 19 Issue 6, p. 983-995, 2008.

144. Lorenz E. N. Deterministic Nonperiodic Flow". Journal of the Atmospheric Sciences 20 (2): 130-141, March, 1963.

145. Lu C.-J., Le T.-S., Chiu C.-C. Financial time series forecasting using independent component analysis and support vector regression//Decision Support Systems 47 (2009) 115-125.

146. Majhi R., Panda G., Sahoo G. Panda A., Choubey A. Prediction of the S&P 500 and DJIA Stock Indices using Particle Swarm Optimization Technique, IEEE Congress on Evolutionary Computation, 2008.

147. Mansurov, A. K. (2008). Forecasting currency crises by fractal analysis techniques. Studies on Russian Economic Development, 19(1), 96-103.

148. McCulloch W.,Walter P. A Logical Calculus of Ideas Immanent in Nervous Activity (1943). Bulletin of Mathematical Biophysics 5 (4): 115-13.

149. McMullin B. Computational autopoiesis: The original algorithm. Santa Fe, NM 87501, USA: Santa Fe Institute, 1997.

150. McNelis, P. D. (2005). Neural networks in finance: gaining predictive edge in the market. Elsevier Acad. Press.

151. Mikhailov A.S., Loskutov A.Y. Foundations of Synergetics II. Chaos and Noise, 2nd revised and enlarged edition, Springer Series in Synergetics. Springer, Berlin — Heidelberg 1996.

152. Mithchell M. Complex Systems: Network Thinking// Portland State University and Santa Fe Institute, 2006.

153. Nenortaite J. A Particle Swarm Optimization Approach In The Construction of Decision-Making Model// ISSN 1392 - 124X Information Technology and Control, 2007, Vol.36, No.1A.

154. Ortega, L. F. (2012). A Neuro-wavelet Method for the Forecasting of Financial Time Series. In Proceedings of the World Congress on Engineering and Computer Science (Vol. 1).

155. Panait L., Luke S. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems 11.3 (2005): 387-434/

156. Parzen E. (On Estimation of a Probability Density Function and Mode. The Annals of Mathematical Statistics 33 (3): 1065, 1962.

157. Pastore, S., Ponta, L., & Cincotti, S. (2010). Heterogeneous information­based artificial stock market. New Journal of Physics, 12(5), 053035.

158. Pavelka, A., & Prochazka, A. (2004). Algorithms for initialization of neural network weights. In Sbornik prιspevku 12. rocnιku konference MATLAB 2004 (Vol. 2, pp. 453-459).

159. Peng, L., & Liu, H. Y. (2007, December). Decision-making and simulation in multi-agent robot system based on PSO-neural network. In Robotics and Biomimetics, 2007. ROBIO 2007. IEEE International Conference on (pp. 1763-1768). IEEE.

160. Poncela, P., Rodriguez, J., Sanchez-Mangas, R., & Senra, E. (2011). Forecast combination through dimension reduction techniques. International Journal of Forecasting, 27(2), 224-237.

161. Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific reports, 3.

162. Pujol, J. M., Sanguesa, R., & Delgado, J. (2002, July). Extracting reputation in multi agent systems by means of social network topology. In Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1 (pp. 467-474). ACM.

163. Qi, M. (2001). Predicting US recessions with leading indicators via neural network models. International Journal of Forecasting, 17(3), 383-401.

164. Rachev, S. T., Hsu, J. S., Bagasheva, B. S., & Fabozzi, F. J. (2008). Bayesian methods in finance (Vol. 153). Wiley. Com.

165. Railsback, S. F., & Grimm, V. (2011). Agent-based and individual-based modeling: a practical introduction. Princeton University Press.

166. Raudys, S., & Zliobaite, I. (2006). The multi-agent system for prediction of financial time series. In Artificial Intelligence and Soft Computing-ICAISC 2006 (pp. 653-662). Springer Berlin Heidelberg.

167. Refenes, A. P. (1994). Neural networks in the capital markets. John Wiley & Sons, Inc..

168. Ritter Н. "Self-organizing feature maps: Kohonen maps", in М.А. Arbib, ed., The Handbook of Brain Theory and Neural Networks, 1995, р. 846-851, Cambridge, МА: MΓΓ Press.

169. Rius, A., Ruisanchez, I., Callao, M. P., & Rius, F. X. (1998). Reliability of analytical systems: use of control charts, time series models and recurrent neural networks (RNN). Chemometrics and intelligent laboratory systems, 40(1), 1-18.

170. Romer, D. (1992). Rational asset price movements without news (No. w4121). National Bureau of Economic Research.

171. Rosenblatt M. Remarks on Some Nonparametric Estimates of a Density Function. The Annals of Mathematical Statistics 27 (3): 832, 1956.

172. Rosenblatt F. Principles of neurodynamics; perceptrons and the theory of brain mechanisms. - Washington, Spartan Books, 1962.

173. Roshan, W. D. S., Gopura, R. A. R. C., & Jayasekara, A. G. B. P. (2011, August). Financial forecasting based on artificial neural networks: Promising directions for modeling. In Industrial and Information Systems (ICIIS), 2011 6th IEEE International Conference on (pp. 322-327). IEEE.

174. Salamon T. Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes, 2001.

175. Shoham, Y., & Leyton-Brown, K. Multiagent systems: Algorithmic, game- theoretic, and logical foundations. Cambridge University Press. 2009.

176. Schoreels C., Logan B., Garibaldi J.M. Agent based Genetic Algorithm Employing Financial Technical Analysis for Making Trading Decisions Using Historical Equity Market Data

177. Shleifer A. Inefficient Markets: An Introduction to Behavioral Finance. New York: Oxford University Press, 1999

178. Shiller R. Stock prices and social dynamics. Brookings Papers on Economic Activity, 2:457-498, 1984.

179. Shiller, R., & Book, I. E. (2000). Robert J. Shiller: Irrational Exuberance.

180. Simon L. Forecasting Foreign Exchange Rates With Neural Networks. Project Report, Computer Science Institute, University of Neuchatel, 2002.

181. Smith M. Neural Networks for Statistical Modeling, Van Nostrand Reinhold, 1993.

182. Soennen, L.,Hennigar, E. An Analysis of Exchange Rate and Stock

183. Prices - The U.S. Experience Between 1980 and 1986, Akron Business and Economic Review, 19, 7-16, 1988.

184. Sole, R. V., Bonabeau, E., Delgado, J., Fernandez, P., & Marin, J. (2000). Pattern formation and optimization in army ant raids. Artificial life, 6(3), 219­226.

185. Sornette D. Dragon-Kings, Black Swans and the Prediction of Crises// International Journal of Terraspace Science and Engineering, 2009.

186. Sornette D., Woodard R., Wei-Xing Zhou W.-X. The 2006-2008 oil bubble: Evidence of speculation, and prediction// Physica A 388 (2009) 1571­1576.

187. Sornette D. Why Stock markets Crash?/ 2003 by Princeton University Press.

188. Steinhaus H. (1956). Sur la division des corps materiels en parties. Bull. Acad. Polon. Sci., C1. III vol IV: 801—804, 1956.

189. Sukittanon, S., Surendran, A. C., Platt, J. C., & Burges, C. J. (2004, October). Convolutional networks for speech detection. In INTERSPEECH. Tenti, P.. Forecasting foreign exchange rates using recurrent neural networks. Applied Artificial Intelligence, 10(6), 567-582, 1996.

190. Thom, Rene. Structural Stability and Morphogenesis: An Outline of a General Theory of Models. Reading, MA: Addison-Wesley, 1989.

191. Thomas, J.,Zhang, F.. Don’t Fight the Fed model. Unpublished paper, Yale University, School of Management, 2008.

192. Thorndike, R.L. Who belongs in the family?, Psychometrika, 1953.

193. Timmer, J. H.. Understanding the Fed Model, Capital Structure, and Then Some. Capital Structure, and Then Some (March 4, 2012).

194. Trippi, R. R., &Turban, E. (1992). Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance. McGraw-Hill, Inc.

195. Van den Bergh W.M., Boer K., de Bruin A., Kaymak U., Spronk J. On Intelligent-Agent Based Analysis of Financial Markets// Erasmus University, 2002.

196. Vasicek, O. An Equilibrium Characterisation of the Term Structure. Journal of Financial Economics 5: 177-188, 1977.

197. Vermaak, J., & Botha, E. C. (1998). Recurrent neural networks for short­term load forecasting. Power Systems, IEEE Transactions on, 13(1), 126-132.

198. Vojinovic, Z., Kecman, V., & Seidel, R. (2001). A data mining approach to financial time series modelling and forecasting. Intelligent Systems in Accounting, Finance and Management, 10(4), 225-239.

199. Voit J. The Statistical Mechanics of Financial Markets/ Springer-Verlag Berlin Heidelberg 2005.

200. Wang, J., & Chang, C. I. (2006). Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. Geoscience and Remote Sensing, IEEE Transactions on, 44(6), 1586-1600.

201. Wang, L., Liang, Y., Shi, X., Li, M., & Han, X. (2006, January). An improved OIF Elman neural network and its applications to stock market. In Knowledge-Based Intelligent Information and Engineering Systems (pp. 21­28). Springer Berlin Heidelberg.

202. Wertz, V., & Verleysen, M. (2001). Dimension reduction of technical indicators for the prediction of financial time series-Application to the BEL20 Market Index. European Journal of Economic and Social Systems, 15(2), 31­48.

203. Weston, J. F., & Copeland, T. E. (1992). Financial theory and corporate policy. Addison Wesley.

204. White, H. (1989). Learning in artificial neural networks: A statistical perspective. Neural computation, 1(4), 425-464.

205. Wiener, N. (1948). Cybernetics; or control and communication in the animal and the machine.

206. Williams, C. Taylor, J.S. (2003). The stability of kernel principal components analysis and its relation to the process eigenspectrum. In Advances in Neural Information Processing Systems: Proceedings from the 2002 Conference (Vol. 15, p. 383). The MIT Press.

207. Williams, R. J., & Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1(2), 270-280.

208. Witte, B. C. (2012). Using Agent-Based Modeling to Explore the Dynamics of Financial Markets and the Potential for Regulation (Doctoral dissertation).

209. Witten I.H., Frank H., Hall M.A. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers, 2011.

210. Wong, J.C., Lian, H. and Cheong, S.A. Detecting macroeconomic phases in the Dow Jones Industrial Average time series, no. 388, 2009.

211. Wooldridge, M. (2008). An introduction to multiagent systems. Wiley.

212. Yao, J., & Tan, C. L. (2000). A case study on using neural networks to perform technical forecasting of forex. Neurocomputing, 34(1), 79-98.

213. Yardeni, E. New, improved stock valuation model. Topical study #44, US Equity Research, Deutsche Morgan Grenfell, 1999.

214. Yardeni E. Fed's stock market model finds overvaluation. Topical study #38, US Equity Research, Deutsche Morgan Grenfell, 1997.

215. Yu, L., Wang, S., & Lai, K. K. (2005). A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates. Computers & Operations Research, 32(10), 2523-2541.

216. Yu, L., Wang, S., & Lai, K. K. (2008). Credit risk assessment with a multistage neural network ensemble learning approach. Expert Systems with Applications, 34(2), 1434-1444.

217. Zhang, G., Eddy Patuwo, B., & Y Hu, M. (1998). Forecasting with artificial neural networks: The state of the art. International journal of forecasting, 14(1), 35-62.

218. Zimmermann, H. G., Grothmann, R., & Neuneier, R. Multi-agent FX- market modeling by neural networks. In Operations Research Proceedings 2001 (pp. 413-420). Springer Berlin Heidelberg, 2002.

219. Zirilli, J. S. (1996). Financial prediction using neural networks. International Thomson Computer Press.

<< | >>
Источник: Головачев Сергей Сергеевич. Прогнозирование доходности на фондовом и валютном рынках на основе моделей искусственных нейронных сетей. Диссертация на соискание ученой степени кандидата экономических наук. Москва - 2014.

Еще по теме Библиография:

  1. Библиография
  2. § 3. Правовое положение Банка России как субъекта финансового права и органа, осуществляющего банковское регулирование
  3. Глава2. Содержание договора банковского счета
  4. Заключение
  5. ВВЕДЕНИЕ
  6. Индивидуальные функции полезности профучастников- членов СРО
  7. Контроль расчетов по возмещению материального ущерба
  8. Основные тенденции и направления развития секьюритизации.
  9. Внешнеэкономическая деятельность : учебное пособие / А. И. Дралин, С. Г. Михнева. - Изд. 2-е, перераб. и доп. - Пенза : Информационно-издательский центр ИГУ,2006. - 127 с., 2006
  10. Микова Евгения Сергеевна. Моментум эффект в динамике цен акций российского рынка. Диссертация на соискание ученой степени кандидата экономических наук. Москва - 2014, 2014
  11. ВВЕДЕНИЕ
  12. Теоретические работы по сложным опционным продуктам
  13. Структурированный стрэнгл - продажа волатильности на основе биржевых опционов на рынке FORTS
  14. ВВЕДЕНИЕ
  15. Инфраструктурная платформа управления виртуальными ор­ганизациями в сфере социальных коммуникаций
  16. Анализ динамики структуры экономики Самарской области
  17. Опыт и перспективы применения методических положений повыше­ния инновационной активности и результативности человеческого капи­тала в угольной компании «СУЭК»
  18. ГЛАВА 1. БАНКОВСКАЯ ДЕЯТЕЛЬНОСТЬ КАК ОБЪЕКТ БАНКОВСКОГО РЕГУЛИРОВАНИЯ
  19. Глава1. Общая характеристика правового РЕГУЛИРОВАНИЯ БАНКОВСКОЙ ДЕЯТЕЛЬНОСТИ