slides cf04

Computational Finance 2004

Artificial Agents and Speculative Bubbles

Y. Semet, S. Gelly, M. Schoenauer, M. Sebag

Optimization & Machine Learning GroupINRIA Futurs

Orsay, France

Artificial Agents and Speculative Bubbles - Y. Semet

Computational Finance 2004 2

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



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.



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



Our marketDouble Auctions, Cleared Book Convention1 financial asset2 kinds of Agents: ZI and Agents are randomly endowed with cash & shares



Auction Algorithm



ZI-Agents, Exogenous Risk

Risk set by given external functionSimulates decreasing hope for dividend collectingFinite time horizon



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



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)



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



A glimpse on the GUICode in JAVALarge number of control parametersAn even larger number of time seriesVisualization is a critical issue for ACE experimetns



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



Linear risk ; Time horizon=500p(t)

Buy&Sell

r(t)



Endogenous risk: default behavior (efficiency)

p(t)

r(t)Buy&Sell

ExuberanceComfortPanic



At t=250, F becomes 75

p(t)

r(t)Buy&Sell




Bubbles w/o krach ; R0 in [0.4;0.6]

p(t)

Close-upBuy&Sell




Bubbles w/o krach ; R0 in [0.4;0.8]

p(t)

Buy&SellExuberanceComfortPanic



Influence of fool factor

1.05

1.1

1.2

1.0



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




Bubbly behavior (R0 in [0.4;0.8])

p(t)

Close-upBuy&Sell




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]

slides cf04

Documents

sell exuberance comfort

exogenous risk endogenous

exogenous risk risk

idle sell

sell rt

time t

pt closeup

panic mode