dorsal/arxiv
View SchemaCompetitive Advantage for Multiple-Memory Strategies in an Artificial Market
| Authors | Kurt E. Mitman, Sehyo Charley Choe, Neil F. Johnson |
|---|---|
| Categories | |
| ArXiv ID | physics/0503031 |
| URL | https://arxiv.org/abs/physics/0503031 |
| DOI | 10.1117/12.618869 |
Abstract
We consider a simple binary market model containing $N$ competitive agents. The novel feature of our model is that it incorporates the tendency shown by traders to look for patterns in past price movements over multiple time scales, i.e. {\em multiple memory-lengths}. In the regime where these memory-lengths are all small, the average winnings per agent exceed those obtained for either (1) a pure population where all agents have equal memory-length, or (2) a mixed population comprising sub-populations of equal-memory agents with each sub-population having a different memory-length. Agents who consistently play strategies of a given memory-length, are found to win more on average -- switching between strategies with different memory lengths incurs an effective penalty, while switching between strategies of equal memory does not. Agents employing short-memory strategies can outperform agents using long-memory strategies, even in the regime where an equal-memory system would have favored the use of long-memory strategies. Using the many-body `Crowd-Anticrowd' theory, we obtain analytic expressions which are in good agreement with the observed numerical results. In the context of financial markets, our results suggest that multiple-memory agents have a better chance of identifying price patterns of unknown length and hence will typically have higher winnings.
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"abstract": "We consider a simple binary market model containing $N$ competitive agents.\nThe novel feature of our model is that it incorporates the tendency shown by\ntraders to look for patterns in past price movements over multiple time scales,\ni.e. {\\em multiple memory-lengths}. In the regime where these memory-lengths\nare all small, the average winnings per agent exceed those obtained for either\n(1) a pure population where all agents have equal memory-length, or (2) a mixed\npopulation comprising sub-populations of equal-memory agents with each\nsub-population having a different memory-length. Agents who consistently play\nstrategies of a given memory-length, are found to win more on average --\nswitching between strategies with different memory lengths incurs an effective\npenalty, while switching between strategies of equal memory does not. Agents\nemploying short-memory strategies can outperform agents using long-memory\nstrategies, even in the regime where an equal-memory system would have favored\nthe use of long-memory strategies. Using the many-body `Crowd-Anticrowd\u0027\ntheory, we obtain analytic expressions which are in good agreement with the\nobserved numerical results. In the context of financial markets, our results\nsuggest that multiple-memory agents have a better chance of identifying price\npatterns of unknown length and hence will typically have higher winnings.",
"arxiv_id": "physics/0503031",
"authors": [
"Kurt E. Mitman",
"Sehyo Charley Choe",
"Neil F. Johnson"
],
"categories": [
"physics.soc-ph",
"cond-mat.dis-nn"
],
"doi": "10.1117/12.618869",
"title": "Competitive Advantage for Multiple-Memory Strategies in an Artificial Market",
"url": "https://arxiv.org/abs/physics/0503031"
},
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