Abstract
An information-based multiasset artificial stock market characterized by different types of stocks and populated by heterogeneous agents is presented and studied so as to determine the influences of agents’ networks on the market’s structure. Agents are organized in networks that are responsible for the formation of the sentiments of the agents. In the market, agents trade risky assets in exchange for cash and share their sentiments by means of interactions that are determined by sparsely connected graphs. A central market maker (clearing house mechanism) determines the price process for each stock at the intersection of the demand and the supply curves. A set of market’s structure indicators based on the main single-assets and multiassets stylized facts have been defined, in order to study the effects of the agents’ networks. Results point out an intrinsic structural resilience of the stock market. In fact, the network is necessary in order to archive the ability to reproduce the main stylized facts, but also the market has some characteristics that are independent from the network and depend on the finiteness of traders’ wealth.
Highlights
The large availability of financial data has allowed the study of financial markets by means of the cooperation of different fields such as engineering, physics, mathematics, and economics [1,2,3,4,5]
The network is necessary in order to archive the ability to reproduce the main stylized facts, and the market has some characteristics that are independent from the network and depend on the finiteness of traders’ wealth
Following the pioneering work done at the Santa Fe Institute [11,12,13], a large number of researchers have proposed model for artificial markets populated by heterogeneous agents endowed with learning and optimization capabilities [14, 15]
Summary
The large availability of financial data has allowed the study of financial markets by means of the cooperation of different fields such as engineering, physics, mathematics, and economics [1,2,3,4,5]. The markets are populated by boundedly rational, heterogeneous agents using rule of thumb strategies. This approach fits much better with agentbased simulation models and computational and numerical methods have become an important tool of analysis [10]. For a detailed review on microscopic (“agent-based”) models of financial markets see [18, 19]
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