سیستم شبیه سازی سفارش عامل هوشمند سازگار در بازار سهام مصنوعی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|16164||2012||9 صفحه PDF||سفارش دهید||6373 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 39, Issue 10, August 2012, Pages 8890–8898
Agent-based computational economics (ACE) has received increased attention and importance over recent years. Some researchers have attempted to develop an agent-based model of the stock market to investigate the behavior of investors and provide decision support for innovation of trading mechanisms. However, challenges remain regarding the design and implementation of such a model, due to the complexity of investors, financial information, policies, and so on. This paper will describe a novel architecture to model the stock market by utilizing stock agent, finance agent and investor agent. Each type of investor agent has a different investment strategy and learning method. A prototype system for supporting stock market simulation and evolution is also presented to demonstrate the practicality and feasibility of the proposed intelligent agent-based artificial stock market system architecture.
The financial turmoil triggered by the US subprime mortgage crisis has swept the world since 2007. Many banks, real estate investment trusts (REIT) and hedge funds have suffered significant losses as a result of mortgage payment defaults or mortgage asset devaluation. Some even collapsed, such as Bear Stearns and Lehman Brothers (Sorkin, 2008 and White and Anderson, 2008). Jan Hatzius estimates that in the past year, financial institutions around the world have already written down $408 billion worth of assets and raised $367 billion worth of capital (Hilsenrath, Ng, & Paletta, 2008). The crisis has severely shaken people’s faith in traditional economic theory. “We have had a massive failure of the dominant economic model”, Eric Weinstein said. In 2009, Nature journal published two articles on agent-based modeling to study the economics and prevent the financial crisis (Buchanan, 2009 and Farmer and Foley, 2009). Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents (Wu, 2001). ACE is a bottom-up culture dish approach to the study of economic systems (Tesfatsion, 2011). It has been applied to research areas such as asset pricing, stock market simulation, industry dynamics, and macroeconomics. China’s economy has developed rapidly in the past 30 years. The healthy development of the stock market is very important for the national economy. However, changes in the stock trading mechanism may have a greater impact on the market. Thus, the Shanghai Stock Exchange launched China’s first innovation R&D and experimental platform based on finance simulation and modeling technology in 2011. The short-term goal of the innovation experimental platform is to construct a table-top exercises environment for business innovation research. The long-term goal is to build an open and professional R&D experimental environment that can provide support and service for continuous trading mechanism innovation. To achieve this goal, we need to build an innovation experimental platform by designing an adaptive simulation system based on intelligent agents. In practice, some researchers have already developed agent-based simulation systems of the stock market in past years (LeBaron, 2002, Nadeau, 2009 and Wang et al., 2004). However, there are still two limitations in today’s practical simulation systems that need to be addressed. These are: (1) The simulation of market news in the investment decision-making process. In general, investors make investment decisions through comprehensive analysis of various information in which the financial magazine is an important information source. However, little work has been done about the utilization of such information in the decision process of fundamentals investors. Thus, how to simulate the decision-making process of fundamentals investors based on financial information is one major challenge. (2) The learning mechanism of fundamentals investors. In the long practice of investing, each investor will continue to learn to improve their profitability. Investors improve and optimize their strategies based on investment return. Everyone is willing to believe information sources that have strong predictive power. Thus, the predictive ability evaluation of the various information sources is a key problem. How to design and implement the learning mechanism of fundamentals investors is another challenge. To address these challenges, we studied the conceptual model of the stock market in-depth. It includes stocks, investors, financial information, trading mechanisms and other participants. We use a different agent to represent each type of participant. The relationships among the agents are embodied using the agent hierarchy. We describe in detail the design of the stock agent, investor and financial agent, which shows how market news is used in the decision-making process. We study the investment strategy and learning algorithm of fundamentals investors and other types of investors. Finally, we design and implement one system to simulate the real stock market, i.e., Intelligent Agent-assisted Order Simulation System (IAOSS), and evaluate the reasonableness of the system design through some technical indicators. The rest of the paper is organized as follows. Section 2 discusses related work. Section 3 describes the system design, including the conceptual model, agent hierarchy, agent design, and system architecture. Section 4 describes system implementation, including agent implementation and the investor learning algorithm. Section 5 describes the application and evaluation of the system and conducts short selling experiments. Finally, we draw conclusions to end this paper.
نتیجه گیری انگلیسی
The artificial stock market is becoming a hot topic in the finance domain. It allows us to test the feasibility of the reform with minimum cost. The adaptive order simulation system is the core of the artificial stock market. This paper has identified two key issues, namely, that financial information simulation and the learning mechanism of fundamentals investors are crucial to the artificial stock market. We have proposed an intelligent agent-based novel architecture, IAOSS, which takes advantage of the intelligent, autonomous aspects of intelligent agent technology. In particular, the IAOSS architecture has the following novel features: (1) It introduces financial information to the stock market conceptual model. The process of financial information acquirement and utilization has been implemented through the interaction among stock agents, finance agent and investor agents. Because the Chinese stock market is dominated by fundamental investors, the rational and effective use of stock fundamentals reported in the financial information is very important for stock market simulation. (2) It introduces the learning mechanism for fundamentals investors. Fundamental investors can adjust their trust against different finance agents based on the earning rate. Through learning and evolution, fundamental investors can determine the most appropriate finance agents. Thus, market news can be received by the fundamentals investor through the finance agent, and eventually be embodied by their investment behaviors. To demonstrate the technical feasibility of the proposed IAOSS architecture and to serve as an experimental platform for trading mechanism innovation, a prototype implementation of the IAOSS framework for the artificial stock market was constructed. Results from preliminary prototype evaluations have shown that: (1) The artificial stock market has the macro-economic characteristics of the real stock market. Their volatility curves are very similar; thus, the artificial stock market can serve as an innovation experimental platform. (2) The overheating of the economy and the economic crisis has little effect on volatility. If short selling is allowed, the effects will be relatively bigger. (3) Short selling will have a great impact on volatility in the Chinese stock market. In other words, our experiments support the first view in Section 2.3. It should be noted that as our experiment scenario is setup based on Chinese stock market statistics, these conclusions do not necessarily apply to other countries’ stock markets. Overall, IAOSS provides the means for policy makers to predict the consequence of trading mechanisms innovations and take corresponding precautions. It will thus have great application value. In future research, we still have much work to do in certain aspects, such as investor learning evolution, investor interaction, and investor psychology.