State Transitions

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Detecting buy and sell signals for assets in a stock market. Calculates the transition from schooling to swarming with asset prices. It is NOT a technical analysis method.

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<ul><li> 1. Detecting State Transitions in a Stock Market with Many Agents Eric Van Horenbeeck PhD CNTS, University of Antwerp</li></ul> <p> 2. Detecting State Transitions in a Stock Market with Many Agents </p> <ul><li>Main thesis: </li></ul> <ul><li>Decisions based on state transitions perform better than decisions based on models of complex behavior </li></ul> <ul><li>A transition occurs when an interactive system is triggered into an alternate state of organization </li></ul> <p> 3. Detecting State Transitions in a Stock Market with Many Agents </p> <ul><li>Outline </li></ul> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <ul><li>5. Detecting State Transitions </li></ul> <ul><li>6. Results </li></ul> <ul><li>7. Summary </li></ul> <ul><li>8. Future Work </li></ul> <p> 4. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work 5. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li>Red line: US funds with a range of annual return for ten years ending December 2000. Blue line: returns from random chance </li></ul> <ul><li>The red and blue lines are on top of each other indicating that the number of above average funds is no different than if fund returns were based entirely on luck. </li></ul> <ul><li>The number of fund managerswith above-average returns over the last ten years is no different than would be by chance </li></ul> <p> 6. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work 7. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li>Technical traders assume that prices follow a pattern </li></ul> <ul><li>Fundamental analysts assume prices respond to underlying economic realities </li></ul> <ul><li>Random Walk Theory and Efficient Market Hypothesis hold that the best bet for tomorrows stock price is its value today </li></ul> <ul><li>But </li></ul> <p> 8. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li>Long term feedback effects </li></ul> <ul><li>Erratic behavior under certain conditions </li></ul> <ul><li>Fractal structure </li></ul> <ul><li>Sensitive on initial conditions </li></ul> <ul><li>Trading behavior is neither purely rational nor random </li></ul> <p> 9. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li>Distances between intraday prices are small relative to the length of the path covered, i.e. trade prices are clustered</li></ul> <ul><li>Stock market as a system shows regularity in spite of unpredictable interaction between its agents </li></ul> <ul><li>We have biological and physical models that exhibit similar behavior: ants, fish, plasma oscillations ... </li></ul> <p> 10. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li>From biological models *we learn: </li></ul> <ul><li>Strong relation exists between the constantJ,coupling individual members and the strength of noise </li></ul> <ul><li>In the swarming phase whereJ&lt; 5 , the center of the school hardly moves, whereas ifJ&gt; 5 the fish form a tighter group with a rectilinear movement</li></ul> <ul><li> characterizes the non-linearity of the system, -1/2is the steady swimming speed of fish </li></ul> <ul><li>AtJ -1/2 = 5/ -1/2the schooling structure is self-organizing </li></ul> <p>* Hiro-Sato Niwa (1994)Self-organizing Dynamic Model of Fish Schooling. InJournal of Theoretical Biology , 171,p. 23 136 11. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li> / -1/2stands for the magnitude of random movementJwhere -1/2indicates the mean strength of influence on one individual by the other individuals as a group </li></ul> <ul><li>A transition occurs when the system is no longer driven by the average behavior of individuals. At the sudden transition between incoherent and coherent interaction, the school takes over and the individual becomes a follower. </li></ul> <p> 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 99) 14. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li>Even without formal communication, traders cluster </li></ul> <ul><li>These clusters show self-organizing features (schooling) </li></ul> <ul><li>The trace of alternating swarming and schooling phases exhibits fractal characteristics </li></ul> <ul><li>Technical and fundamental analysts presume the existence of a limit circle attractor. It might exist but... </li></ul> <ul><li>The path is unstable and the time scale unknown </li></ul> <p> 15. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li>Modeling a price path is hard and unsure </li></ul> <ul><li>Knowledge of the (long term) past is not necessary when one knows to recognize a turning point </li></ul> <ul><li>Perception of the current state is sufficient </li></ul> <ul><li>Problem: how to detect a state transition? </li></ul> <p> 16. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work -2 -1 1 2 13.6% 13.6% 34.1% 34.1% Normal distribution of the variance of the observations ( Gaussian noise ) Variance outsidethe normal distribution Variance outside the normal distribution 17. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li>Gaussian noisestands for fluctuations with a probability density function of the normal distribution </li></ul> <ul><li>The observed values should have a variance that is Gaussian distributed </li></ul> <ul><li>Probability of errorerf ( x ) gives the probability that a single sample from a random process with zero-mean and unit-variance Gaussian probability density function will be greater or equal tox </li></ul> <ul><li>We assume that the variables are correlated ( schooling )</li></ul> <ul><li>However, if they behave independently &amp; random the covariance would be zero ( swarming ). </li></ul> <p> 18. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li>The autocorrelation coefficient (ACF) measures the covariance of a set with sizenattwith a setn +1att +1 </li></ul> <ul><li>The error functionerf ( x ) indicates the probability that the ACF belongs to a normally distributed population </li></ul> <ul><li>ACF &gt;erf ( x )phase wave = 1 </li></ul> <ul><li>ACF &lt; 1- erf ( x )phase wave = -1 </li></ul> <ul><li>Transition point when phase wave changes sign, indicating a loss of coherence in the current state </li></ul> <ul><li>Loss of coherence = loss of memory </li></ul> <p> 19. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work Simplified meta model 20. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work Real world phase transition wave 21. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li>Random Harvesteris a trading agent that uses phase transition signals to buy and sell stock </li></ul> <ul><li>Application of business rules: transaction costs, expiration of contracts... </li></ul> <ul><li>Tested on real (historical) data, fed one by one without recalculation of past positions </li></ul> <p> 22. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work 23. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work 24. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work 25. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work 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 27. </p> <ul><li>Random Harvesterworked onArbedfor a simulated period of 3 years</li></ul> <ul><li>17 transactions were executed (one at a loss) </li></ul> <ul><li>Accrued asset value: 5.100 - 3.979 = 1.121 </li></ul> <ul><li>Harvested:2.315 -605 = 1.710 </li></ul> <ul><li>Total return 2.831,- or 71% on the initial value </li></ul> <ul><li>60% of the return is a contribution fromRandom Harvester </li></ul> <p>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 28. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li>The main problem with long term memory is information loss about the current state </li></ul> <ul><li>Long intervals create an illusion of predictability </li></ul> <p> 29. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li>Action is induced by a phase transition (coherence breaking) </li></ul> <ul><li>Perceiving signals of imminent change is necessary to adapt behavior according to the appropriate business rules</li></ul> <ul><li>No need to model the full path</li></ul> <ul><li>Strong preliminary results </li></ul> <p> 30. </p> <ul><li>1. Example </li></ul> <ul><li>2. A Stock Market is a Complex Environment </li></ul> <ul><li>3. Self-organization </li></ul> <ul><li>4. Swarming and Schooling </li></ul> <p>5. Detecting State Transitions 6. Results 7. Summary 8. Future Work </p> <ul><li>Improve direction detection after a transition with vector analysis of local clusters </li></ul> <ul><li>Comparing with other trading agents under similar conditions </li></ul> <ul><li>Generalize use to class of decision problems based on the current state in a dynamic environment </li></ul> <p> 31. </p> <ul><li><ul><li>... for the gods perceive future things, ordinary men things in the present, but wise men perceive things about to happen ... </li></ul></li></ul> <ul><li><ul><li>Philostratus,Life of Apollonius of Tyana , VIII, 7 * </li></ul></li></ul> <p>* Quoted in Taleb, Nassim N. (2001).Fooled By Randomness. The Hidden Role of Chance in the Markets and in Life.New York, NY: Texere, p. 56 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. 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