CHASM Co-evolutionary Heterogeneous Artificial Stock Markets Serafín Martínez

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CHASM Research Summary CHASM Platform Polymorphic Questions: EDDIE How does EDDIE the price change? Fundament EDDIE endogenous What is the al effect of Noise EDDIE learning by traders? Ref: AI-ECON, Giardina et al 2003, other markets 19 March 2018 All rights reserved, Edward Tsang & Serafin Martinez jaramillo Artificial Market

CHASM Overview EDDIE Agents evolve Heterogeneous beliefs Example agent Agents: technical, fundamental, noise or hybrid (mode switching) 19 March 2018 All rights reserved, Edward Tsang & Serafin in each group Experimenter controls the number of agents Martinez jaramillo

Base Case for Stylized Facts Agents learn: Periodic ¦ Red Queen No LIMIT ORDERS Buy ¦ Sell Partial ¦ Complete No FUNDAMENTALIST 100% LEARNING % TO TRADE No 50% Heterogeneity INDICATORS Homogeneity COMPUTING POWER Homogeneity TIME & RETURN Heterogeneity Seven dimensions explored 19 March 2018 Homogeneity All 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 19 March 2018 All rights reserved, Edward Tsang & Serafin Martinez jaramillo

A condition for stylized facts Limit Order Yes No Fundamental Traders Computing Power % to Trade Partial / Complete Homogeneous No 50% 100% Indicators Heterogeneous Homogeneous Time & Return Homogeneous Evolving agents No 19 March 2018 Heterogeneous Yes All 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! 19 March 2018 All 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 -437 19 March 2018 All 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 hi(t) or – cash, denoted by ci(t) The stock price at time t will be denoted by P(t) 19 March 2018 All rights reserved, Edward Tsang & Serafin Martinez jaramillo

Market Mechanism • Agent i will take decision di(t) at time t: di(t) = 1 to buy di(t) = – 1 to sell di(t) = 0 to do nothing • Agent i makes a bid or offer of a fraction qi(t) of its current holding: g is a parameter ci is cash hi is stock holding p is price of stock 19 March 2018 All 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): 19 March 2018 All 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 19 March 2018 All rights reserved, Edward Tsang & Serafin Martinez jaramillo

Traders Heterogeneity • Noise traders – Homogeneous • Fundamentalists – Departure from the fundamentals T – Threshold value t • Technical traders – – – Computational Capabilities Indicators set Rate of return and time horizon Fundamental behavior Limit orders 19 March 2018 All rights reserved, Edward Tsang & Serafin Martinez jaramillo

Traders The market is composed by technical, fundamental and noise traders. We define NT as the number of technical traders, NF as the number of fundamental traders, NN as the number of noise traders and N as the total number of traders in the market. 19 March 2018 All 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, pb, ps and pn respectively. Such probabilities are defined before the simulation and remain with the same value during the simulation. 19 March 2018 All 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 t (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 [tmin tmax] for t. – These limits of such intervals are user specified. 19 March 2018 All 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 19 March 2018 All 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. 19 March 2018 All 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 19 March 2018 All 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 19 March 2018 All rights reserved, Edward Tsang & Serafin Martinez jaramillo

GP Markets comparison • AI-ECON • CHASM – Price based on excess demand (Farmer’s work) – Each tree is an agent – Business School: Fixed retraining periodicity (exogenous) – Notion of ranking 19 March 2018 – 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 All rights reserved, Edward Tsang & Serafin Martinez jaramillo