Risk Preference, Forecasting Accuracy and Survival Dynamics:Simulations Based on a Multi-Asset Agent-Based Artificial Stock Market

• Main Authors: 黃雅琪, Huang, Ya-Chi
• Language: 英文
• Published: 國立政治大學
• Subjects: 基因演算法; 代理人基人工股市; Genetic algorithms; Autonomous agents
• Online Access: http://thesis.lib.nccu.edu.tw/cgi-bin/cdrfb3/gsweb.cgi?o=dstdcdr&i=sid=%22G0882585022%22.

風險偏好與預測精確性對生存力的重要性吸引進來許多理論學者的注意。一個極端是認為風險偏好完全不重要，唯一重要是預測精確性。然而此乃基於柏拉圖最適配置之下。透過代理人基模型，我們發現相異的結果，即風險偏好在生存力上扮演重要角色。 === The relevance of risk preference and forecasting accuracy to the survival of investors is an issue that has recently attracted a number of recent theoretical studies. At one extreme, it has been shown that risk preference can be entirely irrelevant, and that in the long run what distinguishes the agents who survive from those who vanish is just their forecasting accuracy. Being in line with the market selection hypothesis, this theoretical result is, however, established mainly on the basis of Pareto optimal allocation. By using agent-based computational modeling, this dissertation extends the existing studies to an economy where adaptive behaviors are autonomous and complex heterogeneous, and where the economy is notorious for its likely persistent deviation from Pareto optimality. Specifically, a computational multiasset artificial stock market corresponding to Blume and Easley (1992) and Sandroni (2000) is constructed and studied. Through simulation, we present results that contradict the market selection hypothesis. Risk preference plays a key role in survivability. And agents who have superior forecasting accuracy may be driven out just because of their risk preference. Nevertheless, when all the agents are with the same preference, the wealth share is positively correlated to forecasting accuracy, and the market selection hypothesis is sustained, at least in a weak sense.