Deducing the multi-trader population driving a financial market

Nachi Gupta; Raphael Hauser; Neil Johnson

doi:10.1117/12.638334

Deducing the multi-trader population driving a financial market

Nachi Gupta, Raphael Hauser, Neil Johnson

Physics

Research output: Contribution to journal › Conference article › peer-review

Abstract

We previously laid out a framework for predicting financial movements and pockets of predictability by deducing the heterogeneity in the multi-agent population in terms of trader types playing in an artificial financial market model.¹ This work explores extensions to this basic framework. We allow for more intelligent agents with a richer strategy set, and we no longer constrain the estimate for the heterogeneity over the agents to a probability space. We then introduce a scheme which accounts for models with a wide variety of agent types. We also discuss a mechanism for bias removal on the estimates of the relevant parameters.

Original language	English (US)
Article number	603909
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	6039
DOIs	https://doi.org/10.1117/12.638334
State	Published - 2006
Event	Complex Systems - Brisbane, Australia Duration: Dec 12 2005 → Dec 14 2005

Keywords

Econophysics
Inequality Constrained Iterative Optimization
Multi-Agent Financial Market Models

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1117/12.638334

Cite this

@article{54c0fcb4470c41359697e7532b3c12ac,

title = "Deducing the multi-trader population driving a financial market",

abstract = "We previously laid out a framework for predicting financial movements and pockets of predictability by deducing the heterogeneity in the multi-agent population in terms of trader types playing in an artificial financial market model.1 This work explores extensions to this basic framework. We allow for more intelligent agents with a richer strategy set, and we no longer constrain the estimate for the heterogeneity over the agents to a probability space. We then introduce a scheme which accounts for models with a wide variety of agent types. We also discuss a mechanism for bias removal on the estimates of the relevant parameters.",

keywords = "Econophysics, Inequality Constrained Iterative Optimization, Multi-Agent Financial Market Models",

author = "Nachi Gupta and Raphael Hauser and Neil Johnson",

year = "2006",

doi = "10.1117/12.638334",

language = "English (US)",

volume = "6039",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

issn = "0277-786X",

publisher = "SPIE",

}

TY - JOUR

T1 - Deducing the multi-trader population driving a financial market

AU - Gupta, Nachi

AU - Hauser, Raphael

AU - Johnson, Neil

PY - 2006

Y1 - 2006

N2 - We previously laid out a framework for predicting financial movements and pockets of predictability by deducing the heterogeneity in the multi-agent population in terms of trader types playing in an artificial financial market model.1 This work explores extensions to this basic framework. We allow for more intelligent agents with a richer strategy set, and we no longer constrain the estimate for the heterogeneity over the agents to a probability space. We then introduce a scheme which accounts for models with a wide variety of agent types. We also discuss a mechanism for bias removal on the estimates of the relevant parameters.

AB - We previously laid out a framework for predicting financial movements and pockets of predictability by deducing the heterogeneity in the multi-agent population in terms of trader types playing in an artificial financial market model.1 This work explores extensions to this basic framework. We allow for more intelligent agents with a richer strategy set, and we no longer constrain the estimate for the heterogeneity over the agents to a probability space. We then introduce a scheme which accounts for models with a wide variety of agent types. We also discuss a mechanism for bias removal on the estimates of the relevant parameters.

KW - Econophysics

KW - Inequality Constrained Iterative Optimization

KW - Multi-Agent Financial Market Models

UR - http://www.scopus.com/inward/record.url?scp=33645230583&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33645230583&partnerID=8YFLogxK

U2 - 10.1117/12.638334

DO - 10.1117/12.638334

M3 - Conference article

AN - SCOPUS:33645230583

VL - 6039

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

SN - 0277-786X

M1 - 603909

T2 - Complex Systems

Y2 - 12 December 2005 through 14 December 2005

ER -

Deducing the multi-trader population driving a financial market

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this