1. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo CHASM Co-evolutionary Heterogeneous Artificial Stock Markets Serafín Martínez Jaramillo Edward Tsang CCFEACCFEA, Essex

2. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo CHASM Research Summary Ref: AI-ECON, Giardina et al 2003, other marketsAI-ECONGiardina et al 2003other markets Questions:  How does the price change?  What is the effect of learning by traders? Artificial Market endogenous EDDIE Fundamental EDDIE Noise Polymorphic CHASM Platform

3. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo CHASM Overview EDDIE Agents Agents evolve Heterogeneous beliefs Example agent Agents: technical, fundamental, noise or hybrid (mode switching) Experimenter controls the number of agents in each group

4. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Experimenter’s controls Users have control over: Market Mechanisms Number of traders Etc.

5. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Heterogeneity LIMIT ORDERS FUNDAMENTALIST % TO TRADE INDICATORS COMPUTING POWER TIME & RETURN LEARNING No Agents learn: Periodic Periodic ¦ Red QueenRed Queen Homogeneity Heterogeneity Homogeneity Heterogeneity Homogeneity 50% 100% Partial ¦ Complete No Buy ¦ Sell No Base Case for Stylized Facts  Seven dimensions explored

6. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Summary of Base Case Agents must be competent and use balanced training data (realistic expectation) Presence of fundamental traders needed Limit orders (trading strategies) essential % traded and time & return matter Heterogeneous computing power and beliefs not essential

7. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo A condition for stylized facts Limit OrderYesNo Fundamental Traders Partial / Complete No Computing Power HomogeneousHeterogeneous % to Trade50%100% IndicatorsHeterogeneousHomogeneous Time & ReturnHomogeneousHeterogeneous Evolving agentsNoYes

8. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Learning Improves Performance

9. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Learning under Red Queen

10. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Prices and Returns

11. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Artificial Finance Market Conclusions Platform developed –It supports a wide range of experiments Conditions for stylized facts identified in endogenous, realistic market Agents must be competent and realistic –Some must observe fundamental values Learning agents (EDDIE-based): –Statistical properties of returns and wealth distribution changed –No need for fundamental trader!

12. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo CHASM Co-evolutionary Heterogeneous Artificial Stock Market Details and Explanations

13. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo CHASM Market Details Assets Market Mechanism Excess Demand Rationing Wealth Dynamics GIARDINA, I.A. and J.P.A. BOUCHAUD, 2003. Bubbles, crashes and intermittency in agent based market models. The European Physical Journal B-Condensed Matter, Vol.31, 421-437Bubbles, crashes and intermittency in agent based market models

14. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Assets The market participant i will be able to hold at time t, two different types of assets: –a risky asset, denoted by h i (t) or –cash, denoted by c i (t) The stock price at time t will be denoted by P(t)

15. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Market Mechanism Agent i will take decision d i (t) at time t : d i (t) = 1 to buy d i (t) = –1 to sell d i (t) = 0 to do nothing Agent i makes a bid or offer of a fraction q i (t) of its current holding: g is a parameter c i is cash h i is stock holding p is price of stock

16. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Excess Demand The aggregated volume of bids: B(t) The aggregated volume of offers: O(t) Excess demand: D(t) Price is calculated in the following way: ( λ is a parameter):

17. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Rationing The amount of shares that the agent i will actually buy or sell is:

18. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Wealth dynamics Finally we can update the traders’ holdings of cash and the risky asset:

19. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo CHASM Traders Details and Explanations

20. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Evolving Agents Evolution: –The wealth of an investor is the main factor to search an improvement on his prediction rules (Red Queen dynamics) Benchmark and calibration: –Stylised facts Time: –Discrete time jumps

21. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Example Agent ITE BuyITEAnd = SellHold = Not < MV_12 233 TRB_5 27.3 VOL_12-16.5

22. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Traders Heterogeneity Noise traders –Homogeneous Fundamentalists –Departure from the fundamentals T –Threshold value  Technical traders –Computational Capabilities –Indicators set –Rate of return and time horizon –Fundamental behavior –Limit orders

23. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Traders The market is composed by technical, fundamental and noise traders. We define N T as the number of technical traders, N F as the number of fundamental traders, N N as the number of noise traders and N as the total number of traders in the market.

24. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Noise traders The noise traders will take a decision to buy, sell or do nothing with different probabilities, p b, p s and p n respectively. Such probabilities are defined before the simulation and remain with the same value during the simulation.

25. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Fundamentalists They will change their position on the risky asset if the price departs from a value that they perceive as the fundamental one. These traders will continue to adjust their positions until such difference T, is lower than a certain threshold value  Farmer 1998 . The above mentioned values will be generated for each individual trader by drawing random numbers from uniform intervals [Tmin, Tmax] for T and [  min  max] for . –These limits of such intervals are user specified.

26. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Technical traders Technical analysis is a key feature of the behavior of these agents. Technical analysis is an important tool for decision making in investment. Besides, there is strong evidence that technical analysis is being used extensively in financial markets. We are not restricted to use just technical indicators. We use some momentum indicators as well. EDDIE is the basic framework to the design of the investment strategy of our agents

27. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Technical traders The technical traders forecast if the price is going to rise by a certain r% within a certain number of days n. They will be equipped with up to eight different technical and momentum indicators. Under certain circumstances they will be able to behave like fundamentalists.

28. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Limit orders A realistic investment strategy was necessary to complete the design of the agents. For that reason, limit orders were created as part of their exit strategy. –Orders to buy or sell at certain prices The technical traders can generate two types of limit orders: –Profit taking –Stop loss

29. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo CHASM Results

30. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo

31. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo

32. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo

33. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo

34. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo

35. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo

36. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo Artificial Financial Markets Santa Fe Artificial Stock Market Zero Intelligence Agents Minority Game Microscopic simulation Econo-physics Genetic Programming Neural Networks Learning Classifier Systems

37. 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo GP Markets comparison AI-ECON –Price based on excess demand (Farmer’s work) –Each tree is an agent –Business School: Fixed retraining periodicity (exogenous) –Notion of ranking CHASM –Price based on excess demand –Each Trader has a population of trees –Fixed periodicity and “Red Queen” dynamics (endogenous) –Relative Wealth drives the desire to improve

38. No Limit Order 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo

39. No Fundamental Trader 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo

40. Heterogeneous Computing Power 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo

41. Trading 100% of capital 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo

42. Homogeneous Indicators 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo

43. Heterogeneous time and return 19 October 2015All rights reserved, Edward Tsang & Serafin Martinez jaramillo