英文标题：
《Winning Investment Strategies Based on Financial Crisis Indicators》
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作者：
Antoine Kornprobst
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最新提交年份：
2017
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英文摘要：
  The aim of this work is to create systematic trading strategies built upon several financial crisis indicators based on the spectral properties of market dynamics. Within the limitations of our framework and data, we will demonstrate that our systematic trading strategies are able to make money, not as a result of pure luck but, in a reproducible way and while avoiding the pitfall of over fitting, as a result of the skill of the operators and their understanding and knowledge of the financial market. Using singular value decomposition (SVD) techniques in order to compute all spectra in an efficient way, we have built two kinds of financial crisis indicators with a demonstrable power of prediction. Firstly, there are those that compare at every date the distribution of the eigenvalues of a covariance or correlation matrix to a distribution of reference representing either a calm or agitated market reference. Secondly, we have those that merely compute at every date a chosen spectral property (trace, spectral radius or Frobenius norm) of a covariance or correlation matrix. Aggregating the signals provided by all the indicators in order to minimize false positive errors, we then build systematic trading strategies based on a discrete set of rules governing the investment decisions of the investor. Finally, we compare our active strategies to a passive reference as well as to random strategies in order to prove the usefulness of our approach and the added value provided by the out-of-sample predictive power of the financial crisis indicators upon which our systematic trading strategies are built.
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中文摘要：
这项工作的目的是根据市场动态的光谱特性，在几个金融危机指标的基础上制定系统的交易策略。在我们的框架和数据的限制范围内，我们将证明我们的系统交易策略能够赚钱，这不是纯粹的运气，而是以可复制的方式，同时避免过度拟合的陷阱，这是由于运营商的技能以及他们对金融市场的理解和知识。为了有效地计算所有谱，我们使用奇异值分解（SVD）技术构建了两种具有明显预测能力的金融危机指标。首先，有人在每个日期将协方差或相关矩阵的特征值分布与代表平静或动荡市场参考的参考分布进行比较。其次，我们有那些只在每个日期计算协方差或相关矩阵的选定光谱特性（轨迹、光谱半径或Frobenius范数）。综合所有指标提供的信号，以最大限度地减少误报，然后我们基于一组管理投资者投资决策的离散规则，构建系统的交易策略。最后，我们将我们的主动策略与被动参考策略以及随机策略进行比较，以证明我们的方法的有用性，以及我们构建系统交易策略所依据的金融危机指标的样本外预测能力所提供的附加值。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Mathematical Finance        数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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