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Detecting State Transitions in a Stock Market with Many Agents Eric Van Horenbeeck PhD CNTS, University of Antwerp
Detecting State Transitions in a Stock Market with Many Agents ,[object Object],[object Object]
Detecting State Transitions in a Stock Market with Many Agents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object],[object Object],[object Object],[object Object],Hiro-Sato Niwa. 1994. Self-organizing Dynamic Model of Fish Schooling. In  Journal of Theoretical Biology , 171, 23 โ€“ 136
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object]
Always clustering (no ego trips) Always self-organizing (no leaders) Sometimes polarized behavior (schooling) Sometimes random (swarming) 1. Example 2. A Stock Market is a Complex Environment 3. Self-organization 4. Swarming and Schooling 5. Detecting State Transitions 6. Results 7. Summary 8. Future Work
Monday Tuesday Wednesday Thursday Friday 5 days of swarming and schooling by  Philips (Nov. 29 - Dec. 3 โ€˜99)
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work -2 ๏ณ   -1 ๏ณ   ๏ญ   1 ๏ณ   2 ๏ณ   13.6% 13.6% 34.1% 34.1% Normal distribution of the variance of the observations ( Gaussian noise ) Variance outside  the normal distribution Variance outside the normal distribution
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work Simplified meta model
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work Real world phase transition wave
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work
5. Detecting State Transitions 6. Results 7. Summary 8. Future Work 1. Example 2. A Stock Market is a Complex Environment 3. Self-organization 4. Swarming and Schooling
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],1. Example 2. A Stock Market is a Complex Environment 3. Self-organization 4. Swarming and Schooling 5. Detecting State Transitions 6. Results 7. Summary 8. Future Work
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],5. Detecting State Transitions 6. Results 7. Summary 8. Future Work ,[object Object],[object Object],[object Object]
[object Object],[object Object]
References W. B . Arthur, S. N. Durlauf and D . A. Lane, eds.1997.  The Economy as an Evolving Complex System II . Addison-Wesley. Reading, Mass. P. Bak, M Paczuski and M. Shubik. 1996. Price Variations in a Stock Market with Many Agents. Working Paper for the Santa Fรฉ Institute Economics Research Program, submitted to the  Journal of Mathematical Economics . P. De Grauwe, H. Dewachter and M. Embrechts.  1993.  Exchange Rate Theory , Blackwell, Oxford. J. L. Deneubourg, S. Goss, N. R. Franks, A.Sendova-Franks, C. Detrain and L. Chretien.1990. The Dynamics of Collective Sorting:Robot-like Ants and Ant-like Robots. In J-A Meyer and S. Wilson eds,  Simulation of Adaptive Behaviour: from Animals to Animats , MIT Press, Cambridge, Mass. E.F. Fama and K.R. French. 1992. The Cross-Section of Expected Stock Returns. In the  Journal of Finance , 2. K..R. French and R. Roll. 1986. Stock Return Variances, The Arrival of Information and the Reaction of Traders. In  Journal of Financial Economics , 17. L. Harris. 1986. A Transaction Data Study of Weekly and Intradaily Patterns in Stock Returns. In  Journal of Financial Economics , 16. A. W. Lo and A. C. MacKinlay 1999.  A Non-Random Walk Down Wall Street . Princeton University Press, Princeton. B. Mandelbrot. 1966. Forecast of future prices, unbiased markets and martingale models. In  The Journal of Business of the University of Chicago ,39 . B. Mandelbrot. 1998.  Fractals and Scaling in Finance: Discontinuity, Concentration, Risk . Springer-verlag, New York. R.N. Mantegna and H.E. Stanley. 1995. Scaling Behavior in the Dynamics of an Economic Index. In  Nature , 376. Hiro-Sato Niwa. 1994. Self-organizing Dynamic Model of Fish Schooling. In  Journal of Theoretical Biology , 171. E. P. Peters. 1991.  Chaos and Order in Capital Markets . J. Wiley and Son, New York. R. J. Schiller. 1984. Stock Prices and Social Dynamics In  The Brookings Papers on Economic  Activity , 2.

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State Transitions

  • 1. Detecting State Transitions in a Stock Market with Many Agents Eric Van Horenbeeck PhD CNTS, University of Antwerp
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  • 12. Always clustering (no ego trips) Always self-organizing (no leaders) Sometimes polarized behavior (schooling) Sometimes random (swarming) 1. Example 2. A Stock Market is a Complex Environment 3. Self-organization 4. Swarming and Schooling 5. Detecting State Transitions 6. Results 7. Summary 8. Future Work
  • 13. Monday Tuesday Wednesday Thursday Friday 5 days of swarming and schooling by Philips (Nov. 29 - Dec. 3 โ€˜99)
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  • 26. 5. Detecting State Transitions 6. Results 7. Summary 8. Future Work 1. Example 2. A Stock Market is a Complex Environment 3. Self-organization 4. Swarming and Schooling
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  • 32. References W. B . Arthur, S. N. Durlauf and D . A. Lane, eds.1997. The Economy as an Evolving Complex System II . Addison-Wesley. Reading, Mass. P. Bak, M Paczuski and M. Shubik. 1996. Price Variations in a Stock Market with Many Agents. Working Paper for the Santa Fรฉ Institute Economics Research Program, submitted to the Journal of Mathematical Economics . P. De Grauwe, H. Dewachter and M. Embrechts. 1993. Exchange Rate Theory , Blackwell, Oxford. J. L. Deneubourg, S. Goss, N. R. Franks, A.Sendova-Franks, C. Detrain and L. Chretien.1990. The Dynamics of Collective Sorting:Robot-like Ants and Ant-like Robots. In J-A Meyer and S. Wilson eds, Simulation of Adaptive Behaviour: from Animals to Animats , MIT Press, Cambridge, Mass. E.F. Fama and K.R. French. 1992. The Cross-Section of Expected Stock Returns. In the Journal of Finance , 2. K..R. French and R. Roll. 1986. Stock Return Variances, The Arrival of Information and the Reaction of Traders. In Journal of Financial Economics , 17. L. Harris. 1986. A Transaction Data Study of Weekly and Intradaily Patterns in Stock Returns. In Journal of Financial Economics , 16. A. W. Lo and A. C. MacKinlay 1999. A Non-Random Walk Down Wall Street . Princeton University Press, Princeton. B. Mandelbrot. 1966. Forecast of future prices, unbiased markets and martingale models. In The Journal of Business of the University of Chicago ,39 . B. Mandelbrot. 1998. Fractals and Scaling in Finance: Discontinuity, Concentration, Risk . Springer-verlag, New York. R.N. Mantegna and H.E. Stanley. 1995. Scaling Behavior in the Dynamics of an Economic Index. In Nature , 376. Hiro-Sato Niwa. 1994. Self-organizing Dynamic Model of Fish Schooling. In Journal of Theoretical Biology , 171. E. P. Peters. 1991. Chaos and Order in Capital Markets . J. Wiley and Son, New York. R. J. Schiller. 1984. Stock Prices and Social Dynamics In The Brookings Papers on Economic Activity , 2.