Диссертация (Формирование портфеля акций на фондовом рынке с использованием непараметрических методов), страница 19
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(2006), “Currency Crises: Are They All the Same?”,Journal of International Money and Finance, № 25(3), pages 503–52791.Laeven L. and Valencia F. (2008), “Systemic Banking Crises: A NewDatabase”, IMF Working Paper, № 08/22492.Laeven L. and Valencia F. (2010), “Resolution of Banking Crises: TheGood, the Bad, and the Ugly”, IMF Working Paper , № 10/14693.Laeven L. and ValenciaF. (2012), “Systemic Banking CrisesDatabase: An Update”, IMF Working Paper , № 12/16394.Lee F.
(1977), “Functional form, skewness effect and the risk returnrelationship”, Journal of Financial and Quantitative Analysis, № 12,pages 55-6395.Lee Y. and Lippmann R. (1989), “Practical Characteristics of NeuralNetwork and Conventional Pattern Classifiers on Artificial andSpeech Problems”, Proceedings of the 2nd International Conferenceon Neural Information Processing Systems, pages 168-17713296.Levišauskait K.
(2010), “Investment Analysis and PortfolioManagement”, Leonardo da Vinci programme project97.Lewellen J. (2004), “Predicting returns with financial ratios”, Journalof Financial Economics, № 74, pages 209–23598.Li Q. and Racine J. (2007), Nonparametric Econometrics: Theory andPractice, Princeton University Press99.Liang F., Mukherjee S. and West M. (2007), “Understanding the useof unlabeled data in predictive modelling”, Statistical Science, №22(2), pages 198–205.100.Lo A.
and MacKinlay C. (1988), “Stock Market Prices do notFollow Random Walks: Evidence from a Simple Specification Test”,The Review of Financial Studies, № 1, pages 41-66101.Lo A. and MacKinlay C. (1990), “Where are Contrarian ProfitsDue to Stock Market Overreaction?”, The Review of FinancialStudies, № 2, pages 175-205102.Lo A.W and McKinsey G. (1991),” Long-term memory in stockmarket prices” , Econometrica , №59, pages 1279-1313103.Lopes F. and West M. (2004), “Bayesian model assessment infactor analysis”, Statistica Sinica, № 14, pages 41–67104.MacDonald L. and Zucchini W. (1997), Hidden Markov andother models for discrete valued time series, London: Chapman andHall105.MacEachern, S.
and Muller P. (1998), “Estimating mixture ofDirichlet process models”, Computing and Statistic , № 7, pages 223–238106.March L. (1995), “Supervised evolution of the neural tradercomponent of a stock portfolio trading system (part 1)”, Journal ofComputational Intelligence in Finance, № 3, pages 7–12133107.Markowitz H. (1952), “Portfolio Selection”, The Journal ofFinance, № 7, pages 77-91108.MarkowitzH.(1959),Portfolioselection:efficientdiversification of investments, New York: Wiley109.Mehrota K., Mohan C.
and Rank S. (1997), Elements ofArtificial Neural Networks, MIT Press110.Merton R. (1973), “Theory of rational option pricing”, BellJournal of Economics and Management Science, № 4 (1), pages 141183111.Merton R. (1990),. Continuous Time Finance, Basil Blackwell,Oxford112.Michelli C. and Wahba G. (1981), “ Design problems foroptimalsurfaceinterpolation,ApproximationTheoryandApplications, № 45 pages.329–348113.Mittelhammer R., Judge G.
and Miller J. (2000), EconometricFoundations, New York: Cambridge University Press114.MoallemiC. and Saglam M. (2012), “Dynamic PortfolioChoice with Linear Rebalancing Rules”, Journal of Economics andManagement Science, № 6, pages 148-187115.Monica Lam (2004), “Neural network techniques for financialperformance prediction: integrating fundamental and technicalanalysis”, Decision Support Systems, № 37, pages 567 -581116.Moskowitz T., Ooi Y., and Pedersen L.
(2009), “Time seriesmomentum”, Journal of Financial Economics, № 104, pages 228–250117.Mundell R. (1963), “Inflation and real interest“, Journal ofPolitical Economy, № 71, pages 280-283134118.Murphy J. J. (1999), “Technical analysis of the financialmarkets: a comprehensive guide to tradingmethods andapplications”, Prentice Hall Press.119.Mulvey J., Pauling W. and Madev R. (2003), “Advantages ofMultiperiodPortfolioModels”,TheJournalofPortfolioManagement, № 29 , pages 35-45120.Mwamba J. (2011), “Modelling stock price behavior: TheKernel approach”, Journal of Economics and International Finance,№ 10, pages121.418-423Odom M.
and Sharda R. (1990), “A neural network model forbankruptcy prediction”, Proceedings of the InternationalJointConference on Neural networks, pages 163–168122.Ong C., Smola A. and Williamson R. (2005), “Hyperkernels. InNeural Information Processing Systems”, MIT Press, №15, pages495–502123.OplerT. and Titman S. (1994), “Financial Distress andCorporate Performance,” Journal of Finance, № 49, pages 1015-1040124.Pagan A. and Ullah A. (1999), Nonparametric Econometrics,New York: Cambridge University Press.125.Primbs J.
and Sung C. (2008), “A Stochastic Receding HorizonControl Approach to Constrained Index Tracking,”, Asia-PacificFinancial Markets, № 15 (1), pages 3 -24126.Rahman L. and Uddin J. (2009), “ Dynamic Relationshipbetween Stock Prices and Exchange Rates: Evidence from ThreeSouth Asian Countries”, International Business Research, № 2, pages167-174.127.Rasmussen C. and Williams C. (2006), Gaussian Processes forMachine Learning.
Cambridge, MIT Press135128.Ready R. (2013), “Oil Prices and the Stock Market”, ?”, Journalof Statistic, № 15, pages 156 – 172129.Refenes D. (1993), Constructive learning and its application tocurrency exchange rate forecasting, Neural Networks in Finance andInvesting, eds.
R. R. Trippi and E. Turban, Mc- Graw-Hill, New York,pp. 465–493130.Richard M. and Lippmann R. (1992), “Neural networkclassifiers estimate Bayesian a posteriori probabilities”, NeuralComputation, № 3, pages 461-483131.Ritter J. (2005), “Economic growth and equity returns”, Pacific-Basin Finance Journal, № 13, pages 489-503132.Ronzato M., Poultney C., Chopra S. (2007), “Efficient Learningof Sparse Representations with an Energy-Based Model”, CourantInstitute of Mathematical Science New York University133.Ross S.
(1976) , “The Arbitrage Theory of Capital AssetPricing”, Journal of Economic Theory, № 13, pages 341-360134.SalchenbergerM.,CinarM.andLashA.(1992),“Neuralnetwork: A new toolfor predicting thrift failures,” DecisionSciences, № 23, pages 899–916135.Savona R. and Vezzoli M. (2012), “Multidimensional Distanceto Collapse Point and 29 Sovereign Default Prediction’, IntelligentSystems in Accounting”, Finance and Management, № 19, pages 205228136.Sharpe W. (1964), “A simplified model of portfolio analysis”,Management Science , № 13, pages 277-293137.Shiller R. (2000), Irrational Exuberance, Princeton UniversityPress138.Shirashi H.
and Taniguchi M. (2007),” Statistical Estimation ofOptimal Portfolios for Locally Stationary Returns of Assets”,136International Journal of Theoretical and Applied Finance (IJTAF), №10, pages 129 – 154139.Sollich P. (2002), “Bayesian methods for support vectormachines: Evidence and predictive class probabilities”, MachineLearning, № 46,pages 21–52140.Sori Z.
and Jalil H. (2009), “Financial Ratios, DiscriminateAnalysis and the Prediction of Corporate Distress”, Journal of Money,Investment and Banking, № 11, pages 526- 569141.Streichert F., Ulmer H. and Zell A. (2004), “Evaluating a hybridencoding and three crossover operators on the constrained portfolioselection problem”, Evolutionary Computation, № 12 pages 132- 147142.Sunden A. (2006), “Trading based on classification andregression trees”, Kungliga Tekniska högskolan Institutionen förmatematik, № 14, pages 53 - 72143.Taffler R.J.
(1982), “Forecasting company failure in the UKusing discriminant analysis and financial ratio data‟, Journal of theRoyal Statistical Society, №145, pages 342–358144.Taylor S. and Poon S. (1991), “Macroeconomic Factors and TheUK Stock Market”, Journal of Business Finance and Accounting,pages 619 -643145.Tobin J. (1958), “Liquidity Preference as Behavior TowardsRisk”, The Review of Economic Studies, № 25, pages 65-86146.TobinJ.(1965),“MoneyandEconomicGrowth”,Econometrica, № 33, pages 671-684147.Treynor, J.
L. and F. Black (1973), “How to Use SecurityAnalysis to Improve Portfolio Selection”, Journal of Business, № 11,pages 66-88148.Tsay S. (2005), “Nonliearity tests for time series”, Biometrika,№ 73 (2), pages 461–466137149.Turner T. (2007), “A Beginner's Guide to Day Trading Online”,Adams Media, 2nd edition.150.Vanstone B., Finnie G. and Hahn T. (2010), “ Stockmarkettrading using fundamental variables and neural networks”, Paperpresented at the ICONIP 2010: 17th International Conference onNeural Inforamtion Processing. Sydney, Australia151.Wong Q.
and Long A.(1995), “ A neural network approach tostock market holding period returns”, American Business Review, №13.(2), pages 61–64152.Wu S. N. and Huang Z. G. (1997), “ An Empirical Analysis onReport of Earnings Information Stock Price Changes and StockMarket Efficiency”, Accounting Research, №4, pages 12-17153.Yonis M.(2013), “Trading Volume and Stock Return:Empirical Evidence for Asian Tiger Economies”, Master Thesis, №30, pages 1-68138Приложение 1Таблица откликов, полученная при использовании непараметрических методов. Прогнозный период 2016 годНейронные сетиМетод ядерного сглаживанияДеревья классификацийАкцияянв.16 апр.16 июл.16 окт.16 янв.16 апр.16 июл.16 окт.16 янв.16 апр.16 июл.16 окт.16Akron-1-1-1-1-1-1-1-1001-1Alrosa-1-10000000000Aeroflot1-11-110101101Bashneft010-11-1000-100VSMPO - Avismo0-1000-10-10-1-10VTB001000011-100Gazprom1-10100010111NorNik000000000000Diksy-100001-101-11-1Inter RAO-100000000000LSR000000000000Lukoil00001-1001001Magnit0-11-10010001-1M-video11-1101-10011-1Megafon001-100010001Mechel-1-1000-100-1000MKB000000000000MMK000000000000Micex000-100000000Mosenergo000000-10001-1MTS1-1101-1010001NLMK-100101100001NMTP000001-100-100NOVATEK000101101-100139OPKPIKPolimetalPolusRosneftRossetiRostelRusagroRUSALRusGidroSberbankSev StalSistemaSurgutTatneftTransneftUralkaliyFosagroFSK EASUniproYandex0000110-1-10101000-1-10000-100100-10-1100010000-11-100-1-1-1-1-1001000-100-10010-1-10-11000-1-10-1000-100000000110-100100000000-10-1000-1-1000-1101-1001100114000-1-111000000-1-1000-100-10-1-1-1010000000010-100010000000-1-10100100000-1100-1010-1000100-10-10-10-1000-10-10-100-110000-10-10-1-100-1010-1000-1000101-10-11Таблица откликов, полученная при использовании непараметрических методов.
Прогнозный период 2008 годАкцияAeroflotAkronDiksyFSK EASGazpromInter RAOLSRLukoilMagnitMechelMMKMosenergoMTSM-videoNLMKNMTPNorNikNOVATEKPIKPolusRosneftRossetiRostelRusGidroSberbankянв.1610001-1-1110001100-11-1010-1-10Нейронные сетиапр.16 июл.16-111-100-1-1-1-10000011-1000000-1-1-10-1-10000-11-1-101000001-1-1-10окт.16-100-100001-110010-1-1-10-110-100Метод ядерного сглаживанияянв.16 апр.16 июл.16 окт.161-11-1-100001-1-1-10-1-10001-1000-10000-1-1101-11-100-1-100-10-1-1101-1-11-101-1001-1000-1000011-10-1000-11-110-1000-10101-100001-1-1-1141Деревья классификацийянв.16 апр.16 июл.16 окт.161-11-101-101000-1-1-101-1-10000-1-10-10-11010001000-1-1-1-11-100010-111-10100-1-100-1-11-11-1-1100-1-100001-101-1100001-11-10-10000-1-1Sev StalSistemaSurgutTatneftUralkaliyVSMPO AvismoVTB0-1-100-100110-11-1-11000010-100-100010010-110-1-1-1-1-1100-1-1-101000-1-1101-1-1-100000-1-10-10-10000-1-1-1-10000142Приложение 2Полный перечень переменных, использованный для отбора оптимального вектора переменных для каждогометодаПеременнаяТипПеременнаяТипЦена нефтиМакроэкономическаяDebt RatioФундаментальнаяИнфляцияМакроэкономическаяFinancial LeverageФундаментальнаяROEФундаментальнаяROAФундаментальнаяEPS/PФундаментальнаяNet Profit MarginФундаментальная∆EPS/PФундаментальнаяBook Value per ShareФундаментальнаяMomentum (T = 30)ТехническаяBook Value per Share/PriceФундаментальнаяMomentum (T = 90)ТехническаяFCFEФундаментальнаяMomentum (T = 180)Техническая∆FCFEФундаментальнаяMomentum (T = 360)ТехническаяFCFFФундаментальнаяMA/P (T=40)Техническая∆FCFFФундаментальнаяMA/P (T=80)ТехническаяMACD (T=30)ТехническаяMA/P (T=120)ТехническаяMACD (T=90)ТехническаяMA/P (T=240)ТехническаяMACD (T=180)ТехническаяMA/P (T=360)ТехническаяMACD (T=360)ТехническаяBollinger Bands (T = 30)ТехническаяStochastic (T = 30)ТехническаяBollinger Bands (T = 90)ТехническаяStochastic (T = 90)ТехническаяBollinger Bands (T = 180)ТехническаяStochastic (T = 180)ТехническаяBollinger Bands (T = 360)ТехническаяStochastic (T = 360)ТехническаяMA St.