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The Effectiveness Of Different Trading Strategies For Price-Takers

  • Liudmila G. Egorova

    ()

    (National Research University Higher School of Economics)

Simulation models of the stock exchange are developed to explore the dependence between a trader’s ability to predict future price movements and her wealth and probability of bankruptcy, to analyze the consequences of margin trading with different leverage rates and to compare different investment strategies for small traders. We show that in the absence of margin trading the rate of successful predictions should be slightly higher than 50% to guarantee with high probability that the final wealth is greater than the initial and to assure very little probability of bankruptcy, and such a small value explains why so many people try to trade on the stock exchange. However if trader uses margin trading, this rate should be much higher and high rate leads to the risk of excessive losses.

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File URL: http://www.hse.ru/data/2014/04/21/1319149274/29FE2014.pdf
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Paper provided by National Research University Higher School of Economics in its series HSE Working papers with number WP BRP 29/FE/2014.

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Length: 30 pages
Date of creation: 2014
Date of revision:
Publication status: Published in WP BRP Series: Financial Economics / FE, April 2014, pages - 30
Handle: RePEc:hig:wpaper:29/fe/2014
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  1. Terrance Odean, 1999. "Do Investors Trade Too Much?," American Economic Review, American Economic Association, vol. 89(5), pages 1279-1298, December.
  2. 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.
  3. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
  4. Cont, Rama & Bouchaud, Jean-Philipe, 2000. "Herd Behavior And Aggregate Fluctuations In Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 4(02), pages 170-196, June.
  5. 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.
  6. Brown, Philip & Thomson, Nathanial & Walsh, David, 1999. "Characteristics of the order flow through an electronic open limit order book," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 9(4), pages 335-357, November.
  7. 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.
  8. 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, 04.
  9. 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.
  10. 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.
  11. Carl Chiarella & Andreas Rothig, 2010. "Small Traders in Currency Futures Markets Format," Research Paper Series 278, Quantitative Finance Research Centre, University of Technology, Sydney.
  12. Aleskerov, Fuad & Egorova, Lyudmila, 2012. "Is it so bad that we cannot recognize black swans?," Economics Letters, Elsevier, vol. 117(3), pages 563-565.
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