网范文：“ Agent-based Model Construction in Financial Economic System” 本文通过经济和金融的浓缩系统略论，英语论文题目，使用基于代理模型作为一种先进的金融经济和微观仿真略论探讨。这篇金融范文讲述了人工股票的,出现类似的统计事实与实际数据。人工股票市场显示了标准的统计事实,如波动集群,多余的峰态分布的回报,和扩展属性分解的交叉。从这一点上,人工股票市场永远是为了理解印尼股市普遍的市场过程。
股票市场被广泛认为是复杂系统与许多交互的代理形成。此外,计算技术的最新发展也对此有很重要的意义,在这种情况下。有一些重要的里程碑上,如基于代理模型的一些平台。下面的范文进行详述。
ABSTRACT
The gives picture of enrichment to economic and financial system analysis using agent-based models as a form of advanced study for financial economic post-statistical-data and micro-simulation analysis. The s the construction of artificial stock market that emerges the similar statistical facts with real data in Indonesian stock market. We use the individual but dominant data, i.e.: PT TELKOM in hourly interval. The artificial stock market shows standard statistical facts, e.g.: volatility clustering, the excess kurtosis of the distribution of return, and the scaling properties with its breakdown in the crossover of Levy distribution to the Gaussian one. From this point, the artificial stock market will always be evaluated in order to have comprehension about market process in Indonesian stock market generally.
Keywords: artificial stock market, agent based model, statistical facts of stock market.
Stock market has been widely recognized as complex system with many interacting agents involve in the price formation. Furthermore, the recent development of computational technology forges analysis from any disciplines to use agent-based model as one of analytical tools for giving better understanding of social system, in this case, financial system e.g.: price fluctuations. There have been some previous and important milestones in this endeavor, e.g.: some platforms of agent-based model (Farmer, 2017), the minority model (Challet et.al, 1999), the Santa Fe model (LeBaron 2017), and the gate to the computational economics (Tesfatsion, 2017).
Simulation Results
We do several simulations in our artificial stock market in order to have some understanding points of what we discover in previous work on statistical properties of Indonesia stock market (Situngkir & Surya, 2017a; Hariadi & Surya 2017). A pattern we want to analyze is the fact of volatility clustering, in which large changes tend to follow large changes, and small changes tend to follow small changes. The volatility clustering has been widely known as an important and interesting property of the financial time-series data. The cause of this property is certainly the interaction of between the heterogeneous agents; in our case: the fundamentalists, the chartists, and the noise traders. The decisions of any strategies will be different in the sense of expectations about future prices. Other important feature of our simulation is the boundedness of each agents one another on their final decisions; as noted above we apply the influence strength of any decisions (buy, hold, or sell) as the climate of the market. Henceforth, in certain time, a climate to sell, hold, or buy among agents becomes the trigger for the volatility clustering.
Concluding Remarks
We the artificial stock market that emerges the similar statistical facts with real data. The data we use is the individual but dominant index, i.e.: hourly data of PT TELKOM in the time interval January 2017 up to September 2017. The artificial stock market shows standard statistical facts, e.g.: volatility clustering, the excess kurtosis of the distribution of return, and the scaling properties with its breakdown in the crossover of Levy distribution to the Gaussian one. The advantage we can have by the simulation is the understanding of the interaction among traders and their composition of strategies in the Jakarta Stock Exchange. Practically, this can bring us a nice intuitive tool on comprehend the market mechanism in the stock market. Nonetheless, it should need much more further work, especially empirical one, in order to bring us more understanding of the market, e.g.:
Spatial techniques utilization and social network, hence there will be a visualization of accumulating capital from interaction of each agent.
Adding “intelligence” to each agent so that each agent has evolutionary ability in changing techniques and strategies used in her decision-making.
Price enumeration as the result of direct interaction between stock sellers and buyers so they can approach reality of the stock system that will be modeled and explained its various macro-quantitative factors.
imulation that involves several stocks or other secondary products in the market so that it can simulate stock exchange indexes. With these developments, it is hoped that we can have better agent-based model that able to be utilized as an alternative tool for investment and to cope with our enthusiasm for better understanding of the stock market in general.()
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