IDEAS home Printed from https://ideas.repec.org/a/scn/009530/15650846.html
   My bibliography  Save this article

Эффективность Торговых Стратегий Мелких Трейдеров

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://cyberleninka.ru/article/n/effektivnost-torgovyh-strategiy-melkih-treyderov
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liudmila G. Egorova, 2014. "The Effectiveness Of Different Trading Strategies For Price-Takers," HSE Working papers WP BRP 29/FE/2014, National Research University Higher School of Economics.
    2. A. Belenky & L. Egorova, 2016. "Two approaches to modeling the interaction of small and medium price-taking traders with a stock exchange by mathematical programming techniques," Papers 1610.05703, arXiv.org.
    3. Hackethal, Andreas & Haliassos, Michael & Jappelli, Tullio, 2012. "Financial advisors: A case of babysitters?," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 509-524.
    4. Matthew Oldham, 2019. "Understanding How Short-Termism and a Dynamic Investor Network Affects Investor Returns: An Agent-Based Perspective," Complexity, Hindawi, vol. 2019, pages 1-21, July.
    5. Chou, Robin K. & Wang, Yun-Yi, 2011. "A test of the different implications of the overconfidence and disposition hypotheses," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 2037-2046, August.
    6. Itzhak Venezia, 2018. "Lecture Notes in Behavioral Finance," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 10751.
    7. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    8. Tubbenhauer, Tobias & Fieberg, Christian & Poddig, Thorsten, 2021. "Multi-agent-based VaR forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    9. Vivien Lespagnol & Juliette Rouchier, 2018. "Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals," Post-Print hal-02084910, HAL.
    10. Galariotis, Emilios C. & Krokida, Styliani-Iris & Spyrou, Spyros I., 2016. "Bond market investor herding: Evidence from the European financial crisis," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 367-375.
    11. Simon Cramer & Torsten Trimborn, 2019. "Stylized Facts and Agent-Based Modeling," Papers 1912.02684, arXiv.org.
    12. Hsieh, Shu-Fan & Chan, Chia-Ying & Wang, Ming-Chun, 2020. "Retail investor attention and herding behavior," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 109-132.
    13. Torsten Trimborn & Philipp Otte & Simon Cramer & Max Beikirch & Emma Pabich & Martin Frank, 2018. "SABCEMM-A Simulator for Agent-Based Computational Economic Market Models," Papers 1801.01811, arXiv.org, revised Oct 2018.
    14. Litimi, Houda & BenSaïda, Ahmed & Bouraoui, Omar, 2016. "Herding and excessive risk in the American stock market: A sectoral analysis," Research in International Business and Finance, Elsevier, vol. 38(C), pages 6-21.
    15. Vivien Lespagnol & Juliette Rouchier, 2018. "Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 991-1020, April.
    16. Jiahua Wang & Hongliang Zhu & Dongxin Li, 2018. "Price Dynamics in an Order-Driven Market with Bayesian Learning," Complexity, Hindawi, vol. 2018, pages 1-15, November.
    17. Maximilian Beikirch & Simon Cramer & Martin Frank & Philipp Otte & Emma Pabich & Torsten Trimborn, 2020. "Robust Mathematical Formulation And Probabilistic Description Of Agent-Based Computational Economic Market Models," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 23(06), pages 1-41, September.
    18. Kurz, Claudia & Kurz-Kim, Jeong-Ryeol, 2013. "What determines the dynamics of absolute excess returns on stock markets?," Economics Letters, Elsevier, vol. 118(2), pages 342-346.
    19. Zheng, Zhigang & Tang, Ke & Liu, Yaodong & Guo, Jie Michael, 2021. "Gender and herding," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 379-400.
    20. Maximilian Beikirch & Simon Cramer & Martin Frank & Philipp Otte & Emma Pabich & Torsten Trimborn, 2019. "Robust Mathematical Formulation and Probabilistic Description of Agent-Based Computational Economic Market Models," Papers 1904.04951, arXiv.org, revised Mar 2021.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:scn:009530:15650846. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CyberLeninka (email available below). General contact details of provider: http://cyberleninka.ru/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.