artificial multi-agent stock markets: simple strategies

SimStockExchange

Building a more Realistic Artificial Stock Market

Arvid O.I. Hoffmann

© 2006

Visit www.arvidhoffmann.nl or www.simstockexchange.com for more information.04/08/23 2

Outline of this presentation

• Research Objectives and Methods

• Last Year’s Limitations

• Last Year’s Promise

• Theoretical Introduction on Agent-Based Computational Finance

• Four Steps in Building Empirically More Realistic Artificial Stock Markets

• An Introduction to the SimStockExchange Artificial Stock Market

• Introductory Software Demonstration

• Two Preliminary Simulation Experiments

• What have we achieved by now?

• What have we not achieved by now?

• What will we do next?

• Questions?


Research Objectives and Methods

• What do we want?

• A better understanding of micro level investor behavior• A better understanding of macro level stock market dynamics• A better understanding of the micro-macro link

• What methods will we use?

• Consumer and Investment research theories• Survey research amongst individual investors• Combine these theories and survey data to build and parameterize a

multi-agent simulation model• Compare outcomes simulation with real market data


Last Year’s Limitations

• Investors in last year’s model did not get information from their social network, but only derived it directly from stock prices

• New information arrival to the market was not incorporated• Effect of different social networks on information diffusion

processes was not studied• Market dynamics were generated by the actions of investors,

but the cognition of investors was never affected by the evolution of the market; there was no feedback mechanism

• Investors could only trade the shares of one company• Investors were not limited by a budget


• Build a new multi-agent simulation model with the following properties:

• Implementation of different social network structures• Feed news into the market about the expectation of next

period’s stock price• An agent’s success in the market feeds back into his choice

between different investment strategies• Agents have a personal budget• Agents can choose between different shares and/or cash

• This resulted in the SimStockExchange Artificial Stock Market

Last Year’s Promise


Theoretical Introduction

• Many models have been developed in Agent-Based Computational Finance (LeBaron, 2000).

• These models help to explain stylized financial market facts that are hard to explain using traditional representative agent perspectives (LeBaron, 1999).

• However, almost all of these models spring from modern finance: models based on behavioral finance (BF) theories are rare. Takahashi and Terano (2003) is an early BF paper.

• Even less models use empirical data to validate their agent’s trading and interaction rules and evaluate the macro results.


Building empirically more realistic artificial stock markets: Step 1

• In general: use multi-disciplinary theory to formulate specific hypotheses with regard to the individuals or institutions central to the study and test these hypotheses using empirical data.

• In our case:

• Perform empirical study on investor behavior (micro level),

• Perform statistical analyses on stock market data (macro level).

• We focus on investors’ (social) risk reducing strategies and stylized financial market facts like volatility clustering and fat tails of asset returns distributions.



• Discover the effect of (social) interactions amongst micro level subjects on macro level institutions.

• In our case:

• Investigating the effect of interactions amongst micro level investors on macro level stock market dynamics.

• An artificial stock market is created with empirically validated trading and interaction rules.



• Estimate the empirical plausibility of the macro level stock market price and returns data.

• More specific: compare macro level simulation data with empirically found macro level data.

• In our case:

• Compare e.g., the occurrence of stylized financial market facts in the price and returns time series of the simulation model with those of a representative empirical stock market.



• Successful execution of step 1-3 results in quantitative and qualitative agreement between the model and reality on both a micro and a macro level.

• However, often a (partial) mismatch between the simulation generated data and the empirical data remains and/or not every model component could be empirically validated.

• To reduce this mismatch, reconsider the first three steps:

1. Perform more empirical research,2. Modify existing agent rules or create new ones,3. Select a more appropriate empirical benchmark.


Introducing: SimStockExchange (SSE)

• The SSE artificial stock market features different types of investors who conduct transactions based on the investment rules as formalized for each type.

• At the start of each simulation run, investor agents are allocated a number of stocks in their portfolio and an amount of cash.

• Agents can decide to invest all or part of their budget in a number of different stocks or to keep all or part of their budget in cash.

• Different social networks can be formalized in which the agents are positioned and in which market interactions take place.


Introducing: SimStockExchange (SSE)

The SSE operates in 4 steps, of which the last one is optional:

1. Every investor in the market receives a personal signal (information on the next period’s expected price) and observes the current market price.

2. Depending on the confidence of the investor, the personal signal is weighted to a greater or lesser extent with the signal that neighboring agents have received or the other investor’s behavior is simply copied. Based on this an order is forwarded to the stock market.

3. A new market price is calculated based on the crossing of orders in the order book.

4. The agent’s rules can be updated according to their results: more successful agents become more confident.


A small software demonstration…

Start SimStockExchange

Random seed: 1147092813406

X:\Data\My Documents\Hoffmann\Powerpoint bestanden\Presentation WCSS 06_AE_2006\sse.exe.exe


General outline simulation experiments

• We studied the time series behavior of the simulated return time series in two different network situations:

• Regular torus network• Barabasi and Albert scale free network

• We have used empirically obtained settings for an investors’ level of confidence and their risk reducing investment strategies.

• The market was run for 10,000 time steps.


Simulation experiments: parameters

• The following parameter settings were used:


Simulation experiments: statistics


What have we achieved by now?

• We have built the SSE, which is a practical example of combining empirical micro and macro level data, theoretical micro and macro level perspectives, and a multi-agent based social simulation approach when building artificial stock markets.

• The SSE shows a number of strong qualitative and quantitative resemblances with real market data.

• This makes the SSE a valid and valuable platform for investigating micro-macro links in stock markets.


• Limitations of the SSE on both a micro and a macro level are:

• With regard to the micro level agent rules and news process, the SSE is a model, and therefore a simplified image of real stock markets.

• Strong qualitative resemblances with real markets makes it a fruitful avenue for future research, but quantitatively, there is room for improvement.

What have we not yet achieved by now?


What will we do next?

• We will further improve the model:

• Use empirical data to formalize the social network.

• Compare the time horizons of the simulation model with the real world.

• Include a trend in the news akin to that in real stock markets.


Questions?

Visit

www.simstockexchange.com

or

www.arvidhoffmann.nl

for a free model download,

more information and papers.

http://www.simstockexchange.com/

http://www.arvidhoffmann.nl/


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artificial multi-agent stock markets: simple strategies

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