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Эффективность Торговых Стратегий Мелких Трейдеров

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  • Егорова Людмила Геннадьевна

    (Национальный исследовательский университет Высшая школа экономики)

Abstract

Представлены несколько агентно-ориентированных имитационных моделей биржи для изучения зависимости между способностью трейдера правильно предсказывать направление движения цен и его благосостоянием и вероятностью банкротства. Показано, что если трейдер торгует только на собственные средства, то для гарантированного неразорения и получения дохода с высокой долей вероятности ему достаточно правильно принимать решения с вероятностью чуть выше 0,5.Simulation models of stock exchange are developed to explore the dependence between trader’s ability to predict future price change and her wealth and probability of bankruptcy. The paper shows that for the case of cautious behavior (i.e. absence of margin trading) the rate of successful predictions should be just slightly higher than 0,5 and such small value explains why so many people try to trade on the stock exchange.

Suggested Citation

  • Егорова Людмила Геннадьевна, 2014. "Эффективность Торговых Стратегий Мелких Трейдеров," Проблемы управления, CyberLeninka;Общество с ограниченной ответственностью "СенСиДат-Контрол", issue 5, pages 34-41.
    • Handle: RePEc:scn:009530:15650846
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    References listed on IDEAS

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    1. A. Corcos & J-P Eckmann & A. Malaspinas & Y. Malevergne & D. Sornette, 2002. "Imitation and contrarian behaviour: hyperbolic bubbles, crashes and chaos," Quantitative Finance, Taylor & Francis Journals, vol. 2(4), pages 264-281.
    2. Chiarella, Carl & Dieci, Roberto & Gardini, Laura, 2006. "Asset price and wealth dynamics in a financial market with heterogeneous agents," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1755-1786.
    3. Harras, Georges & Sornette, Didier, 2011. "How to grow a bubble: A model of myopic adapting agents," Journal of Economic Behavior & Organization, Elsevier, vol. 80(1), pages 137-152.
    4. Tedeschi, Gabriele & Iori, Giulia & Gallegati, Mauro, 2012. "Herding effects in order driven markets: The rise and fall of gurus," Journal of Economic Behavior & Organization, Elsevier, vol. 81(1), pages 82-96.
    5. Cont, Rama & Bouchaud, Jean-Philipe, 2000. "Herd Behavior And Aggregate Fluctuations In Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 4(2), pages 170-196, June.
    6. Brad M. Barber & Terrance Odean, 2000. "Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors," Journal of Finance, American Finance Association, vol. 55(2), pages 773-806, April.
    7. Itzhak Venezia & Amrut Nashikkar & Zur Shapira, 2011. "Firm specific and macro herding by professional and amateur investors and their effects on market volatility," Discussion Paper Series dp586, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    8. Venezia, Itzhak & Nashikkar, Amrut & Shapira, Zur, 2011. "Firm specific and macro herding by professional and amateur investors and their effects on market volatility," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1599-1609, July.
    9. Henry Penikas & Proskurin S., 2013. "How Well do Analysts Predict Stock Prices? Evidence from Russia," HSE Working papers WP BRP 18/FE/2013, National Research University Higher School of Economics.
    10. Raberto, Marco & Cincotti, Silvano & Focardi, Sergio M. & Marchesi, Michele, 2001. "Agent-based simulation of a financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 319-327.
    11. Aleskerov, Fuad & Egorova, Lyudmila, 2012. "Is it so bad that we cannot recognize black swans?," Economics Letters, Elsevier, vol. 117(3), pages 563-565.
    12. Andreas Röthig & Carl Chiarella, 2011. "Small traders in currency futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(9), pages 898-914, September.
    13. Terrance Odean, 1999. "Do Investors Trade Too Much?," American Economic Review, American Economic Association, vol. 89(5), pages 1279-1298, December.
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