slides cf04
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Computational Finance 2004
Artificial Agents and Speculative Bubbles
Y. Semet, S. Gelly, M. Schoenauer, M. Sebag
Optimization & Machine Learning GroupINRIA Futurs
Orsay, France
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RoadmapAgent-based Computational Economics (ACE)A financial asset trading problem: speculative bubblesA simple model studied in two contexts:
Exogenous risk, ZI, finite time horizon;Endogenous risk with elementary strategy.
A set of conditions for the appearance of speculative bubbles
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BackgroundEuropean Project: DREAM (Distributed Evolutionary Algorithms…)Social and Economic problems are interesting to AI: high frequency data, distributed interactions, stochasticity, cognition and rationality, emergent computation.Market efficiency: simple models for a critical debate.
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Previous WorksGode & Sunder, 93:
Rationality and market efficiencyTool: « Zero-Intelligence » tradersConclusions: aggregate rationality
The Santa Fe Artificial Stock Market, 96Complexity and inductive reasonningTool: Genetic AlgorithmsConclusions: a hint on the efficiency/speculation boundary
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Our marketDouble Auctions, Cleared Book Convention1 financial asset2 kinds of Agents: ZI and Agents are randomly endowed with cash & shares
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Auction Algorithm
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ZI-Agents, Exogenous Risk
Risk set by given external functionSimulates decreasing hope for dividend collectingFinite time horizon
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Evaluating Risk Endogenously
Risk is high when:P is far above FP is going down
Weights = controlA smoothing sigmoïd
),(1
1),( tiaree
tir
dt
dpwFtpvtir iiie )1(,
•r(i,t): risk at time t for agent i
•p(t): asset price
•a, v, w: controlling weights
•F: fundamental value
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1 1 1
Buy
Idle
Sell
0
eT0
eT1
Exuberance
Buy
Idle
Sell
0
cT0
cT1
Comfort
BuyIdle
Sell
0
pT0
pT1
Panic
Choosing a strategy…Exuberance Comfort Panic
R0R00 1r(i,t)
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Making an offerAnchoring effect: offers are uniformly distributed around previous price.In most cases:An asymetric possibility for the panic mode:
]01.1,99.0[ 11 ttt ppUp
],95.0[ 11 ttt ppUp
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A glimpse on the GUICode in JAVALarge number of control parametersAn even larger number of time seriesVisualization is a critical issue for ACE experimetns
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ExperimentsValue Tuning + HeterogenizationMany cross dependenciesReminder: w, alpha, R0, F, Pricing policy.Typical values:…Around 1 minute of computation time, grows very quickly with # of agents
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Linear risk ; Time horizon=500p(t)
Buy&Sell
r(t)
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Endogenous risk: default behavior (efficiency)
p(t)
r(t)Buy&Sell
ExuberanceComfortPanic
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At t=250, F becomes 75
p(t)
r(t)Buy&Sell
ExuberanceComfortPanic
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Bubbles w/o krach ; R0 in [0.4;0.6]
p(t)
Close-upBuy&Sell
ExuberanceComfortPanic
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Bubbles w/o krach ; R0 in [0.4;0.8]
p(t)
Buy&SellExuberanceComfortPanic
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Influence of fool factor
1.05
1.1
1.2
1.0
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Introducing asymetrypt-1-5% in panic vs pt-1 +/- 1% comfort/exb.
bubbly behavior (R0 in [0.4;0.6])
p(t)
Close-upBuy&Sell
ExuberanceComfortPanic
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Bubbly behavior (R0 in [0.4;0.8])
p(t)
Close-upBuy&Sell
ExuberanceComfortPanic
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ConclusionsSpeculative bubbles in two contexts
With exogenous riskEndogenous risk in conjunction with:
High sensitivity to recent trendsBiased heterogeneityAsymetric pricing strategy
Future work: a Game Theory takeContact: [email protected]