Abstract
Using virtual stock markets with artificial interacting software investors, aka agent-based models, we present a method to reverse engineer real-world financial time series. We model financial markets as made of a large number of interacting boundedly rational agents. By optimizing the similarity between the actual data and that generated by the reconstructed virtual stock market, we obtain parameters and strategies, which reveal some of the inner workings of the target stock market. We validate our approach by out-of-sample predictions of directional moves of the Nasdaq Composite Index.
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Abbreviations
- EMH:
-
Efficient Market Hypothesis
- ABM:
-
Agent-based model
- GA:
-
Genetic algorithm
- SGA:
-
Simple Genetic Algorithm
- 3PG:
-
Third Party Game
- MG:
-
Minority Game
- GCMG:
-
“Grand Canonical” Minority Game
- GCMjG:
-
“Grand Canonical” Majority Game
- delGCMG:
-
Delayed “Grand Canonical” Minority Game
- delGCMjG:
-
Delayed “Grand Canonical” Majority Game
- MixG:
-
Mixed Game
- ISD:
-
Initial strategy distribution
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“What I cannot create, I cannot understand”: On physicist Richard Feynman’s blackboard at time of death in 1988; as quoted in The Universe in a Nutshell by Stephen Hawking.
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Wiesinger, J., Sornette, D. & Satinover, J. Reverse Engineering Financial Markets with Majority and Minority Games Using Genetic Algorithms. Comput Econ 41, 475–492 (2013). https://doi.org/10.1007/s10614-011-9312-9
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DOI: https://doi.org/10.1007/s10614-011-9312-9