Optimizing Sparse Mean Reverting Portfolios
Optimizing Sparse Mean Reverting Portfolios
复制标题
优化稀疏均值回归投资组合
DOI：
10.3233/af-13021
复制
发表时间：
2013
期刊：
影响因子：
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通讯作者：
J. Levendovszky
中科院分区：
文献类型：
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作者：
I. Róbert Sipos;J. Levendovszky
在本文中，我们研究交易的最佳均值回复投资组合的基数约束。首先，我们确定了资产价格的VAR（1）模型的参数，然后通过模式匹配技术估计相应的Ornstein-Uhlenbeck过程的数量。投资组合优化是根据两种方法进行的：（i）通过求解广义特征值问题来最大化可预测性或（ii）最大化平均收益。优化本身是由随机搜索算法和前馈神经网络（FFNN）。所提出的解决方案满足基数约束，从而提供稀疏的投资组合，以最大限度地减少交易成本，并最大限度地解释的结果。性能已在历史数据（SWAP利率，SP 500和FOREX）上进行了测试。所提出的交易算法已经取得了29.57%的平均年回报率，在检查的数据集。该算法被证明是适合于高频率，日内交易，因为他们可以处理金融数据的到达率每秒
In this paper we investigate trading with optimal mean reverting portfolios subject to cardinality constraints. First, we identify the parameters of the underlying VAR(1) model of asset prices and then the quantities of the corresponding Ornstein-Uhlenbeck (OU) process are estimated by pattern matching techniques. Portfolio optimization is performed according to two approaches: (i) maximizing the predictability by solving the generalized eigenvalue problem or (ii) maximizing the mean return. The optimization itself is carried out by stochastic search algorithms and Feed Forward Neural Networks (FFNNs). The presented solutions satisfy the cardinality constraint thus providing sparse portfolios to minimize the transaction costs and to maximize interpretability of the results. The performance has been tested on historical data (SWAP rates, SP 500, and FOREX). The proposed trading algorithms have achieved 29.57% yearly return on average, on the examined data sets. The algorithms prove to be suitable for high frequency, intraday trading as they can handle financial data up to the arrival rate of every second