Decision making in financial markets

Autori

• Niklas Lampenius

Parametri

• Libri di testo
• Libri di testo universitari

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This work advances artificial markets research by modeling decision-making in financial markets. It begins with a comprehensive overview of economic and behavioral decision-making, followed by a review of stock market simulation literature. A decision-making model incorporating various behavioral components is introduced and validated through a multi-agent, multi-period stock market simulation. The simulation employs a genetic algorithm to represent individual learning and a cellular automaton for social learning, integrating behavioral aspects like changing risk aversion, overconfidence, and the disposition effect through a specific utility function. Agents learn to forecast prices based on market signals, including fundamental value, charting information, news, and social learning, leading to periodic trading decisions. These decisions are aggregated, resulting in a market-clearing process that establishes a market price. The time series generated is analyzed and compared to the statistical properties of a random walk and actual market data. Findings show that simulations with agents modeled as rational investors produce time series resembling a random walk, while those incorporating behavioral aspects replicate several stylized facts of market data, such as fat-tailed return distributions, long memory effects, and the Taylor effect.

Lingua: Inglese

Pubblicato: 2009

(In brossura)