businessПутеводитель предпринимателяEntrepreneur’s Guide2073-98852687-136XJSC “Publishing Agency “Science and Education”business-1361Research ArticleСтатьиПрименение нейронных сетей для прогнозирования поведения инвесторов на фондовом рынкеPrediction of stock price is an important and challenging problem for studying financial marketsРусаковВ. М.RusakovV. M.VladimirRusakoff@gmail.comРоссийский экономический университет им. Г.В. ПлехановаРоссияPlekhanov Russian University of EconomicsRussian Federation201629012020032206214Copyright © Русаков В.М., 20202020Русаков В.М.Rusakov V.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.pp-mag.ru/jour/article/view/1361

The present study is mainly based on time series of stock prices. In this article, I propose to predict the stock price based on trading behavior of investors. For each stock, we study the trade relations between the investors through the trading network. Then classified network nodes in three ways, according to their wiring diagram, we projected the stock price by the inclusion of these indicators in a neural network based time series stock prices.

нейронные сетифондовый рынокинвесторыторговые стратегииneural networksstock market investorstrading strategiesReferencesFama, E. F. Efficient capital markets II. J. Finance 46, 1575-1617 (1991).Fama, E. F. Efficient capital markets II. J. Finance 46, 1575-1617 (1991).Cootner, P. The random character of stock market prices, (MIT Press, 1964).Cootner, P. The random character of stock market prices, (MIT Press, 1964).Mantegna, R. N. & Stanley, H. E. Scaling behavior in the dynamics of an economic index.Mantegna, R. N. & Stanley, H. E. Scaling behavior in the dynamics of an economic index.Walczak, S. An empirical analysis of data requirements for financial forecasting with neural networks. J. Manage. Inform. Syst. 17, 203-222 (2001).Walczak, S. An empirical analysis of data requirements for financial forecasting with neural networks. J. Manage. Inform. Syst. 17, 203-222 (2001).Cai, S.-M., Zhou, Y-B., Zhou, T. & Zhou, P.-L. Hierarchical organization and disassortative mixing of correlation-based weighted financial networks. Int. J. Mod. Phys. C 21, 433-441 (2010).Cai, S.-M., Zhou, Y-B., Zhou, T. & Zhou, P.-L. Hierarchical organization and disassortative mixing of correlation-based weighted financial networks. Int. J. Mod. Phys. C 21, 433-441 (2010).

The authors declare that there are no conflicts of interest present.