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) |
|---|---|
| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Editors | A. Bender |
| Volume | 6039 |
| DOIs | |
| State | Published - 2006 |
| Externally published | Yes |
| Event | Complex Systems - Brisbane, Australia Duration: Dec 12 2005 → Dec 14 2005 |
Other
| Other | Complex Systems |
|---|---|
| Country | Australia |
| City | Brisbane |
| Period | 12/12/05 → 12/14/05 |
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Keywords
- Econophysics
- Inequality Constrained Iterative Optimization
- Multi-Agent Financial Market Models
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics
Cite this
Deducing the multi-trader population driving a financial market. / Gupta, Nachi; Hauser, Raphael; Johnson, Neil F.
Proceedings of SPIE - The International Society for Optical Engineering. ed. / A. Bender. Vol. 6039 2006. 603909.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Deducing the multi-trader population driving a financial market
AU - Gupta, Nachi
AU - Hauser, Raphael
AU - Johnson, Neil F
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 contribution
AN - SCOPUS:33645230583
VL - 6039
BT - Proceedings of SPIE - The International Society for Optical Engineering
A2 - Bender, A.
ER -