The existence of sunspots or sunspot equilibria has been debated for several decades on its influence in the field of Economics. While models of sunspots or sunspot equilibria have fitted well for some subsets of empirical features, it comes at a cost of moving further away from economic believability and robustness. Studies on the theoretical plausibility of sunspot equilibria have been addressed extensively in several different economic models, but exist almost entirely within the framework of the homogeneous rational expectations equilibrium devised of representative agents. This framework shapes later arising learning approaches to sunspot equilibria. These models have proposed various ways of learning, but they deal mainly with the learning of representative agents. Models of adaptive learning with heterogeneous agents, however, enable us to explicitly tackle coordination issues, such as the coordination mechanism of expectations. This is certainly desirable since sunspots are often used as a coordination device of expectations. In this dissertation, we continue this line of research, investigating the plausibility of sunspot equilibria in stock markets within the framework of heterogeneous agents and the dynamic relationship between sunspot variables and stock returns. We adopt an Agent-based Computational Approach, now known as Agent-based Computational Economics or ACE, to study the plausibility of sunspot equilibria. More specifically, we deal with this issue in the context of an Agent-based Artificial Stock Market (AASM). We contemplate AASMs to be highly suitable to the issue we examine here. Currently, none of the theoretical, empirical, experimental, or simulation models of sunspot equilibria directly capture sunspots within a stock market composed of heterogeneous agents. We conducted three series of experiments to examine this issue. From the results of these three series of simulations, we observed that sunspot variables generally do not have influence on market dynamics. This indicates that sunspot variables remain largely exogenous to the system. Furthermore, we traced the evolution of agents' beliefs and examined their consistency with the observed aggregate market behavior. Additionally, this dissertation takes the advantage of and investigates the micro-macro relationship within the market. We argue that a full understanding of the dynamic linkage between sunspot variables and stock returns cannot be accomplished unless the feedback relationship between individual behaviors, at the micro view, and aggregate phenomena, at the macro view, is well understood / The existence of sunspots or sunspot equilibria has been debated for several decades on its influence in the field of Economics. While models of sunspots or sunspot equilibria have fitted well for some subsets of empirical features, it comes at a cost of moving further away from economic believability and robustness. Studies on the theoretical plausibility of sunspot equilibria have been addressed extensively in several different economic models, but exist almost entirely within the framework of the homogeneous rational expectations equilibrium devised of representative agents. This framework shapes later arising learning approaches to sunspot equilibria. These models have proposed various ways of learning, but they deal mainly with the learning of representative agents. Models of adaptive learning with heterogeneous agents, however, enable us to explicitly tackle coordination issues, such as the coordination mechanism of expectations. This is certainly desirable since sunspots are often used as a coordination device of expectations. In this dissertation, we continue this line of research, investigating the plausibility of sunspot equilibria in stock markets within the framework of heterogeneous agents and the dynamic relationship between sunspot variables and stock returns. We adopt an Agent-based Computational Approach, now known as Agent-based Computational Economics or ACE, to study the plausibility of sunspot equilibria. More specifically, we deal with this issue in the context of an Agent-based Artificial Stock Market (AASM). We contemplate AASMs to be highly suitable to the issue we examine here. Currently, none of the theoretical, empirical, experimental, or simulation models of sunspot equilibria directly capture sunspots within a stock market composed of heterogeneous agents. We conducted three series of experiments to examine this issue. From the results of these three series of simulations, we observed that sunspot variables generally do not have influence on market dynamics. This indicates that sunspot variables remain largely exogenous to the system. Furthermore, we traced the evolution of agents' beliefs and examined their consistency with the observed aggregate market behavior. Additionally, this dissertation takes the advantage of and investigates the micro-macro relationship within the market. We argue that a full understanding of the dynamic linkage between sunspot variables and stock returns cannot be accomplished unless the feedback relationship between individual behaviors, at the micro view, and aggregate phenomena, at the macro view, is well understood.

我們把經濟體視為一個複雜適應系統(complex adaptive system), 強調系統中異質性(heterogeneous)agent的學習適應行為與agent之間的互動性交互作用, 此時主流經濟學裡的分析架構, 如:代表性個人模型(represesentive agent model)、理性預期(rational expectation)、固定點均衡分析(fixed-point equilibrium analysis)等將不再適用, 取而代之的是演化經濟學(evolutionary economics)的研究典範, 這樣的研究架構下, 並沒有適當的數學分析工具可資運用, 因此我們改以agent-based建模(agent-based modelng)的社會模擬(social simulation)來建構一個人工的經濟體(artificial economy), 以此為主要研究方法, 這就是agent-based計算經濟學(agent-based computational economics)或稱人工經濟生命(artificial economic life)。 本文中以股票市場為主要的研究課題, 我們以遺傳規劃(genetic programming)的人工智慧(artificial intelligence)方法來模擬股市中有限理性(bounded rational)異質交易者的交易策略學習行為, 建構出一個人工股票市場(artificial stock market), 在這樣的架構下, 我們成功地產生出類似真實股票市場的股價時間序列特性, 我們同時也檢定了人工股票市場中價量的因果關係, 說明了在沒有外生因素之下, 人工股票市場的複雜系統可自發地產生出雙向的價量因果關係, 進一步地, 我們研究下層agent(交易者)行為與上層股價時間序列行為的關聯性, 我們也發現個體的行為並不能直接加總或推論出複雜適應系統的總體行為, 這就是突現性質(emergent property)的發生, 最後, 本文描述了agent-based計算經濟學研究架構的優勢與缺點, 再附帶介紹一個用以進行agent-based建模相關研究的軟體程式庫-SWARM。

知訊者與非知訊者資訊不對稱之議題在學術殿堂一直廣為學者所研究討論，且各國證管機關為了維持證券市場公平性、保護非知訊者權益並且維持股票市場的穩定運作，適度的資訊揭露以維持證券市場的公平性一直都是各國證券交易所重視的政策目標。 是故，本研究利用代理人基人工股票市場來探討資訊揭露對於金融市場之影響。在此架構下之交易者皆已有限理性方式來呈現。他們是以遺傳規劃(genetic programming)之方式來學習並修正他們對於未來之金融市場之預期。在透過即時的模擬價格之資訊揭露，我們嘗試探討此資訊揭露之金融政策措施對於市場之波動性、市場之流動性之影響。 / The topic of asymmetric information between the informed traders and uninformed traders has been widely discussed by researchers in academics. To maintain the fairness of securities market, an appropriate information disclosure is quite important for authorities of securities regulation to protect the rights and interests of uninformed traders, and to maintain the operations of securities market stable. Based on these reasons, we construct an agent-based artificial stock market to investigate how information disclosure affects a financial market. In this framework of artificial stock market, all traders are characterized by bounded rationality. The traders are able to learn and adjust their predictions of financial market by means of a genetic programming algorithm. We try to understand how market transparency affects the volatility and the liquidity of a securities market.