预测市场状态

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英文标题：
《Forecasting market states》
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作者：
Pier Francesco Procacci and Tomaso Aste
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最新提交年份：
2019
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英文摘要：
  We propose a novel methodology to define, analyze and forecast market states. In our approach market states are identified by a reference sparse precision matrix and a vector of expectation values. In our procedure, each multivariate observation is associated with a given market state accordingly to a minimization of a penalized Mahalanobis distance. The procedure is made computationally very efficient and can be used with a large number of assets. We demonstrate that this procedure is successful at clustering different states of the markets in an unsupervised manner. In particular, we describe an experiment with one hundred log-returns and two states in which the methodology automatically associates states prevalently to pre- and post- crisis periods with one state gathering periods with average positive returns and the other state periods with average negative returns, therefore discovering spontaneously the common classification of `bull\' and `bear\' markets. In another experiment, with again one hundred log-returns and two states, we demonstrate that this procedure can be efficiently used to forecast off-sample future market states with significant prediction accuracy. This methodology opens the way to a range of applications in risk management and trading strategies in the context where the correlation structure plays a central role.
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中文摘要：
我们提出了一种新的方法来定义、分析和预测市场状态。在我们的方法中，市场状态通过参考稀疏精度矩阵和期望值向量来识别。在我们的程序中，每个多变量观察都与给定的市场状态相关联，从而最小化惩罚马氏距离。该程序在计算上非常有效，可用于大量资产。我们证明，该程序能够以无监督的方式成功地将不同的市场状态进行聚类。特别是，我们描述了一个有100个对数回报和两个状态的实验，其中该方法自动将状态与危机前和危机后的阶段相关联，其中一个状态集合阶段具有平均正回报，另一个状态阶段具有平均负回报，因此，我们自发地发现了“牛市”和“熊市”的共同分类。在另一个实验中，又有100个对数收益和两个状态，我们证明了该方法可以有效地用于预测样本外的未来市场状态，具有显著的预测精度。在关联结构发挥核心作用的情况下，该方法为风险管理和交易策略的一系列应用开辟了道路。
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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：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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关键词：Applications Econophysics Quantitative Minimization Multivariate