Application of Machine Learning to Systematic Strategies

Kevin Noel
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引用次数: 3

Abstract

We investigate the use of machine learning techniques into building statistically stable systematic allocation strategies. Traditionally, allocation processes usually rely on variations of Markowitz framework such as Mean Variance allocation, Maximum Diversity, Risk Allocation , Value at Risk, Expected Shortfall, in other words convex frontier optimization. Although those methods show some efficiency to allocate assets through the convex efficient frontier, they usually rely deeply on the estimation and the usage of the covariance matrix. Being no stationary and having multiple range memory (ie FIGARCH using Fractional Brownian Motion), the statistical estimation of covariance may lead to biases and errors and in the end, bias conclusions. Very extensive literature in econo-metrics, econo-physics, quantitative allocation cover this problem in order to remedy to the statistical estimation of covariance and his bias and issues.Here, our emphasis is not a new estimator of the covariance matrix, or a variant of Mean Variance framework but an application of Machine Learning techniques to infer no-linear relationships and long range memory between the assets.It has the advantage to remove the linear projection of the assets onto the covariance framework and then capture no-linear relationships between at various time periods.Recent advances in Neural Network, Deep Learning and Machine Learning allows a more efficient modeling of the no-linear statistical relationships between data (ie price, dividends,...). Among them, we can mention Restricted Boltzman Machines, Variationnal Auto-encoders and variations of Recurrent Neural Network, Attention and Highway Long Short Term Memory as well as Factorization Machines for projection on local sub-spaces.Thus, we investigate some of the techniques to develop practical systematic allocation strategies by reducing risks and estimations biases and show the results.
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机器学习在系统策略中的应用
我们研究使用机器学习技术来构建统计稳定的系统分配策略。传统的分配过程通常依赖于Markowitz框架的变体,如Mean Variance allocation、Maximum Diversity、Risk allocation、Value at Risk、Expected short,即凸边界优化。尽管这些方法通过凸有效边界显示出一定的资产配置效率,但它们通常严重依赖于协方差矩阵的估计和使用。由于协方差的统计估计不具有平稳性,并且具有多范围记忆(即使用分数阶布朗运动的FIGARCH),因此可能导致偏倚和误差,最终得出偏倚结论。为了弥补协方差的统计估计及其偏差和问题,在经济计量学、经济物理学、定量分配等方面都有大量的文献涉及到这个问题。在这里,我们的重点不是协方差矩阵的新估计器,也不是均值方差框架的变体,而是机器学习技术的应用,以推断资产之间的非线性关系和长期记忆。它的优点是将资产的线性投影移到协方差框架上,然后捕获不同时间段之间的非线性关系。神经网络、深度学习和机器学习的最新进展允许对数据之间的非线性统计关系(如价格、股息等)进行更有效的建模。其中,我们可以提到限制玻尔兹曼机,变数自编码器和递归神经网络的变体,注意和高速公路长短期记忆,以及局部子空间上投影的分解机。因此,我们研究了一些技术,通过减少风险和估计偏差来开发实用的系统配置策略,并展示了结果。
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