英文标题：
《U.S. stock market interaction network as learned by the Boltzmann
  Machine》
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作者：
Stanislav S. Borysov and Yasser Roudi and Alexander V. Balatsky
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最新提交年份：
2015
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英文摘要：
  We study historical dynamics of joint equilibrium distribution of stock returns in the U.S. stock market using the Boltzmann distribution model being parametrized by external fields and pairwise couplings. Within Boltzmann learning framework for statistical inference, we analyze historical behavior of the parameters inferred using exact and approximate learning algorithms. Since the model and inference methods require use of binary variables, effect of this mapping of continuous returns to the discrete domain is studied. The presented analysis shows that binarization preserves market correlation structure. Properties of distributions of external fields and couplings as well as industry sector clustering structure are studied for different historical dates and moving window sizes. We found that a heavy positive tail in the distribution of couplings is responsible for the sparse market clustering structure. We also show that discrepancies between the model parameters might be used as a precursor of financial instabilities.
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中文摘要：
我们使用外部场和成对耦合参数化的玻尔兹曼分布模型研究了美国股市中股票收益联合均衡分布的历史动力学。在统计推断的Boltzmann学习框架内，我们分析了使用精确和近似学习算法推断的参数的历史行为。由于模型和推理方法需要使用二元变量，因此研究了连续收益映射到离散域的效果。本文的分析表明，二值化保留了市场相关性结构。研究了不同历史日期和移动窗口大小下的外场分布、耦合特性以及产业集群结构。我们发现，耦合分布中的重正尾是稀疏市场集群结构的原因。我们还表明，模型参数之间的差异可能被用作金融不稳定的前兆。
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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：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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