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– 1982. –Vol. 50. – No. 4. – P. 987-1007.97. Fama, E. The behavior of stock market prices / E. Fama // Business. – 1965. –Vol. 38. – No. 1. – P. 34 – 105.98. Fama, E. Efficient capital markets: A review of theory and empirical work /E. Fama // Journal of Finance. – 1970. – Vol. 25. – No. 2. – P. 383-417.99. Financial Brownian Particle in the Layered Order-Book Fluid andFluctuation-Dissipation Relations / Y. Yura, H. Takayasu, D.
Sornette, M. Takayasu //Physical Review Letters. – 2014. – Vol. 112. – No. 9. – P. 1-5.100. Frankel, J. A. Chartists, Fundamentalists, and Trading in the ForeignExchange Market / J. A. Frankel, K. A. Froot // The American Economic Review. –1990. – Vol. 80. – No. 2. – P. 181-185.101. Goldberg, D. E. Genetic Algorithms in Search, Optimization, and MachineLearning / D. E. Goldberg. – Boston, MA : Addison-Wesley, 1989. – 432 p.172102.
Gorr, W. L. Comparative study of artificial neural network and statisticalmodels for predicting student grade point averages / W. L. Gorr, D. Nagin, J. Szcypula// International Journal of Forecasting. – 1994. – Vol. 10. – P. 17-34.103. Granger, C.W. J. Spectral analysis of New York stock market prices / C.W.J. Granger, O. Morgenstem // Kyklos .
– 1963. – Vol. 16. – No. 1. – P. 1-27.104. Grossman, S. J. On the impossibility of informationally efficient markets / S.J. Grossman, J. E. Stiglitz // The American Economic Review. – 1980. – Vol. 70. –No. 3. – P. 393-408.105. Gustafsson, F. Some Relations Between Extended and Unscented KalmanFilters / F. Gustafsson, G.
Hendeby // IEEE Transactions on Signal Processing. – 2012.– Vol. 60. – No. 2. – P. 545–555.106. Harvey, A. C. Estimating missing observations in economic time series / A.C. Harvey, R. G. Pierse // Journal of the American Statistical Association. – 1984. –Vol. 79. – No. 385. – P. 125-131.107. Harvey, C.
R. Volatility in the Foreign Currency Futures Markets / C. R.Harvey, R. D. Huang // Review of Financial Studies. – 1991. – Vol. 4. – No. 3. –P. 543-569.108. Hecht-Nielsen, R. Kolmogorov's Mapping Neural Network ExistenceTheorem / R. Hecht-Nielsen // IEEE First Annual Int. Conf.
on Neural Networks. – SanDiego, 1987. – Vol. 3. – P. 11-14.109. Hodrick, R. Postwar U.S. Business Cycles: An Empirical Investigation / R.Hodrick, E.C. Prescott // Journal of Money, Credit, and Banking. – 1997. – Vol. 29. –No. 1. – P. 7-16.110. Hornik, K. Multilayer feed-forward networks are universal approximators /K. Hornik, M. Stinchcombe, H. White // Neural Networks. – 1989. – Vol. 2. – No. 5. –P. 359-366.111. Hull, J.
Options, Futures, and Other Derivatives, 7th ed. / J. Hull. – UpperSaddle River, N.J. : Pearson/Prentice Hall, 2006. – 814 p.173112. Investing.com – Stock Market Quotes & Financial News [Электронныйресурс] – Режим доступа: https://www.investing.com/ (дата обращения –19.09.2018).113. Jablonska, M. Multi-agent stochastic simulation for the electricity spotmarket price / M. Jablonska, T.
Kauranne // Lecture Notes in Economics andMathematical Systems. – 2011. – Vol. 652. – P. 3-14.114. Jain, P. C. The dependence between hourly prices and trading volume / P. C.Jain, G. H. Joh // Financial Quantitative Anal. – 1998. – Vol. 23. – P. 269-284.115. Jansen, D. W. On the frequency of large stock returns: putting booms andbusts into perspective / D.
W. Jansen, C. G. de Vries // Rev. Economics Statistics. –1991. – Vol. 73. – P. 18-24.116. Jegadeesh, N. Returns to Buying Winners and Selling Losers: Implicationsfor Stock Market Efficiency / N. Jegadeesh, S. Titman // The Journal of Finance. –1993.
– Vol. 48. – No. 1. – P. 65-91.117. Julier, S. J. Unscented filtering and nonlinear estimation / S. J. Julier, J.K.Uhlmann // Proceedings of the IEEE. – 2004. – Vol. 92. – No.3. – P. 401-422.118. Kac, M. Random Walk and the Theory of Brownian Motion / M. Kac // TheAmerican Mathematical Monthly. – 1947. – Vol. 54. – No. 7. – P. 369-391.119.
Kalman, R. E. New results in linear filtering and prediction theory / R. E.Kalman, R. S. Bucy // Trans. ASME Journal of Basic Engineering. – 1961. – Vol. 83. –No. 1 – P. 95-108.120. Kalman, R. E. A New Approach to Linear Filtering and Prediction Problems/ R. E. Kalman // Trans. ASME, Journal of Basic Engineering. – 1960. – Vol. 82. –No. 1.
– P. 35-45.121. Kanas, A. Non-linear forecasts of stock returns / A. Kanas // Journal ofForecasting. – 2003. – Vol. 22. – No. 4. – P. 299-315.122. Keim, D. Size-Related Anomalies and Stock Return Seasonality: FurtherEmpirical Evidence / D. Keim // Journal of Financial Economics. – 1983. – Vol. 12. –No. 1. – P. 13-32.174123. Kemp, A.
G. The random walk hypothesis and the recent behaviour ofequity prices in Britain / A. G. Kemp, G. C. Reid // Economica. – 1971. – Vol. 38. –No. 149. – P. 28-51.124. Kendall, M. G. The analysis of economic time – series – Part I: Prices / M.G. Kendall // Journal of the Royal Statistical Society. – 1953. – Vol. 96.
– P. 11-25.125. Khrennikov, A. Financial Heat Machine: Coupling with the PresentFinancial Crises / A. Khrennikov // Wilmott. – 2012. – Vol. 57. – P. 32-45.126. Kitagawa, G. A nonstationary time series model and its fitting by a recursivefilter / G. Kitagawa // Journal of Time Series Analvsis. – 1981. – Vol. 2. – No. 2. –P. 103-116.127. Kling, A. Animal Spirits / A. Kling //The Independent Review. – 2009. –Vol. 14. – No.
1. – P. 135-140.128. Kohn, R. Efficient Estimation and Prediction in Time Series RegressionModels / R. Kohn, C. F. Ansley // Biometrika. – 1985. – Vol. 72. – P. 694-697.129. Kohn, R. Estimation, prediction, and interpolation for ARIMA models withmissing data / R. Kohn, C. F. Ansley // Journal of the American Statistical Association.– 1986. – Vol. 81. – No. 395. – P. 751-761.130. Kohn, R. Signal extraction for finite nonstationary time series / R.
Kohn, C.F. Ansley // Biometrika. – 1987. – Vol. 74. – No. 2. – P. 411-421.131. Konak, F. The Efficiency of Developed Markets: Empirical Evidence fromFTSE 100 / F. Konak, Y. Seker // Journal of Advanced Management Science. – 2014. –Vol. 2. – No. 1. – P. 29-32.132. Koutmos, G.Modeling interest rate volatility: an extended EGARCHapproach / G. Koutmos // Managerial Finance.
– 2012. – Vol. 38. – No. 6. – P. 628-635.133. Kuan, C. M. Forecasting exchange rates using feedforward and recurrentneural networks / C. M. Kuan, T. Liu // Journal of Applied Econometrics. – 1995. –Vol. 10. – No. 4. – P. 347-364.175134. Levy, M. Microscopic simulation of the stock market – the effect ofmicroscopic diversity / M. Levy, H. Levy, S. Solomon // Journal de Physique. – 1995.
–Vol. 5. – No. 8. – P. 1087-1107.135. Lo, A. W. Fear And Greed In Financial Markets: A Clinical Study Of DayTraders / A. W. Lo, D. V. Repin, N. Brett // Steenbarger American Economic Review. –2005. – Vol. 95. – No. 2. – P. 352-359.136. Lo, A. W. Econometric models of limit-order executions / A. Lo, C.MacKinlay, J. Zhang // Journal of Financial Economics. – 2002. – Vol. 65. – No. 1. –P. 31-71.137. Long, H.
V. Application of Kalman Filter on Modelling Interest Rates / H.V. Long // Journal of Management Sciences. – 2014. – Vol. 1. – No. 1. – P. 1-15.138. Lux, T. Scaling and criticality in a stochastic multi-agent model of afinancial market / T. Lux, M. Marchesi // Nature. – 1999. – Vol. 397. – P.
498-500.139. Malkiel, B. G. The Efficient Market Hypothesis and Its Critics / B. G.Malkiel // Journal of Economic Perspectives. – 2003. – Vol. 17. – No. 1. – P. 59-82.140. Mantegna, R. N. Turbulence and Financial Markets / R. N. Mantegna, H. E.Stanley // Nature. – 1996. – Vol. 383. – P. 587-588.141. Manzan, S. Representativeness of news and exchange rate dynamics / S.Manzan, F. Westerhoff // Journal of Economic Dynamics and Control. – 2005. –Vol.
29. – No. 4. – P. 677-689.142. Matthies, L. Kalman filter-based algorithms for estimating depth from imagesequences / L. Matthies, T. Kanade, R. Szeliski // International Journal of ComputerVision. – 1989. – Vol. 3. – No. 3. – P. 209-238.143. Merton, R. Theory of Rational Option Pricing / R. Merton // The BellJournal of Economics and Management Science. – 1973. – Vol. 4. – No. 1.
–P. 141-183.144. MetaTrader 4 User Guide [Электронный ресурс] – Режим доступа:https://pepperstone.com/files/Pepperstone – Metatrader – 4 – User – Guide.pdf (датаобращения – 29.01.2018).176145. Morale, D. An interacting particle system modelling aggregation behavior:from individuals to populations / D. Morale, V. Capasso, K.
Oelschläge // Journal ofmathematical biology. – 2005. – Vol. 50. – No. 1. – P. 49-66.146. Mql – Metatrader Development Course [Электронный ресурс] – Режимдоступа: http://www.metatrader.info/book/print/34 (дата обращения – 29.01.2018).147. Muller, U. A. Volatilities of different time resolutions – analyzing thedynamics of market components / U. A. Muller, M. Dacorogna, R. D. Dave, R. B.Olsen, O. V. Pictet, J. E. von Weizsacker // Empirical Finance. – 1997. – Vol. 4.
–P. 213-239.148. Muyi, L., On Mixture Memory GARCH Models / L. Muy, K. L. Wai, L.Guodong // Journal of Time Series Analysis. – 2013. – Vol. 34. – No. 6. – P. 606-624.149. Nelson, D. B. Stationarity and Persistence in the GARCH(1,1) Model / D. B.Nelson // Econometric Theory. – 1990. – Vol. 6.
– No. 4. – P. 318-334.150. Ng, P. Forecast comparison of exchange rate models with the Kalman filter /P. Ng, A. Heidari // Technological Forecasting and Social Change. – 1992. – Vol. 41. –No. 4. – P. 435-443.151. Nison, S. Japanese Candlestick / S. Nison // Charting Techniques. – NY :Institute of Finance, 1991. – 315 p.152. Ostertagova, E. Forecasting using simple exponential smoothing method / E.Ostertagova, O. Ostertag // Acta Electrotechnica et Informatica. – 2012. – Vol. 12. –No. 3.
– P. 62–66.153. Pettit, R. R. Insider Trading and Long-Run Return Performance / R. R.Pettit, P. C. Venkatesh // Financial Management. – 1995. – Vol. 24. – No. 2. –P. 88-103.154. Pearson, K. Problem of the Random Walk / K. Pearson // Nature. – 1905. –Vol. 72. – No. 1. – P. 294-294.155. Phillips, P. C .B. Testing for a Unit Root in Time Series Regression / P. C.B. Phillips, P. Perron // Biometrika. – 1988. – Vol.
75. – No. 2. – P. 335-346.177156. Phillips, G. D. A. Maximum likelihood estimation of regression modelswith autoregressive – moving average disturbances / G. D. A. Phillips // Biometrika. –1979. – Vol. 66. – P. 49-58.157. Plasmans, J. Estimating structural exchange rate models by artificial neuralnetworks / J. Plasmans, W. Verkooijen, H. Daniels // Applied Financial Economics. –1998. – Vol. 8. – No. 5. – P. 541-551.158.
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