英文标题：
《Detecting intraday financial market states using temporal clustering》
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作者：
Dieter Hendricks, Tim Gebbie, Diane Wilcox
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最新提交年份：
2017
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英文摘要：
  We propose the application of a high-speed maximum likelihood clustering algorithm to detect temporal financial market states, using correlation matrices estimated from intraday market microstructure features. We first determine the ex-ante intraday temporal cluster configurations to identify market states, and then study the identified temporal state features to extract state signature vectors which enable online state detection. The state signature vectors serve as low-dimensional state descriptors which can be used in learning algorithms for optimal planning in the high-frequency trading domain. We present a feasible scheme for real-time intraday state detection from streaming market data feeds. This study identifies an interesting hierarchy of system behaviour which motivates the need for time-scale-specific state space reduction for participating agents.
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中文摘要：
我们提出了一种利用日内市场微观结构特征估计相关矩阵的高速最大似然聚类算法来检测暂时性金融市场状态。我们首先确定事前的日内时间集群配置来识别市场状态，然后研究已识别的时间状态特征来提取状态特征向量，从而实现在线状态检测。状态特征向量作为低维状态描述符，可用于高频交易领域的最优规划学习算法。我们提出了一种从流媒体市场数据源中实时检测日内状态的可行方案。这项研究确定了一个有趣的系统行为层次结构，它激发了参与代理对特定于时间尺度的状态空间缩减的需求。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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