摘要翻译：
在本文中，我们从本质上重新解释了Sieczka-Ho{\l}yst(SH)模型，使其更适合于真实市场的描述。例如，这种重新解释使人们有可能认为特工是狡猾的。这些代理人鼓励他们的邻居购买一些股票，如果代理人有机会出售这些股票。此外，代理商鼓励他们出售一些股票，如果代理商有相反的机会。此外，在我们的解释中，价格变化只响应代理人的意见变化。这种反应保护了股票市场动态不受悖论的影响（存在于SH模型中），即所有代理人在相应价格保持不变的情况下购买股票。在这项工作中，我们发现了这样的情况：回报的分布（在相当不同的时间尺度上得到的）要么服从幂律，要么至少有肥尾。我们从在我们的方法框架内进行的数值模拟中获得了这些分布。
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英文标题：
《Reinterpretation of Sieczka-Ho{\l}yst financial market model》
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作者：
Mateusz Denys, Tomasz Gubiec, Ryszard Kutner
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最新提交年份：
2013
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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英文摘要：
  In this work we essentially reinterpreted the Sieczka-Ho{\l}yst (SH) model to make it more suited for description of real markets. For instance, this reinterpretation made it possible to consider agents as crafty. These agents encourage their neighbors to buy some stocks if agents have an opportunity to sell these stocks. Also, agents encourage them to sell some stocks if agents have an opposite opportunity. Furthermore, in our interpretation price changes respond only to the agents' opinions change. This kind of respond protects the stock market dynamics against the paradox (present in the SH model), where all agents e.g. buy stocks while the corresponding prices remain unchanged. In this work we found circumstances, where distributions of returns (obtained for quite different time scales) either obey power-law or have at least fat tails. We obtained these distributions from numerical simulations performed in the frame of our approach.
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PDF链接：
https://arxiv.org/pdf/1301.2535