IEEE Trans Neural Netw Learn Syst. 2013 Mar;24(3):448-59. doi: 10.1109/TNNLS.2012.2230405.
In this paper, we consider using profit/loss histories of multiple automated trading systems (ATSs) as N input variables in portfolio management. By means of multivariate statistical analysis and simulation studies, we analyze the influences of sample size (L) and input dimensionality on the accuracy of determining the portfolio weights. We find that degradation in portfolio performance due to inexact estimation of N means and N(N - 1)/2 correlations is proportional to N/L; however, estimation of N variances does not worsen the result. To reduce unhelpful sample size/dimensionality effects, we perform a clustering of N time series and split them into a small number of blocks. Each block is composed of mutually correlated ATSs. It generates an expert trading agent based on a nontrainable 1/N portfolio rule. To increase the diversity of the expert agents, we use training sets of different lengths for clustering. In the output of the portfolio management system, the regularized mean-variance framework-based fusion agent is developed in each walk-forward step of an out-of-sample portfolio validation experiment. Experiments with the real financial data (2003-2012) confirm the effectiveness of the suggested approach.
在本文中，我们考虑将多个自动交易系统（ATS）的损益历史用作投资组合管理中的 N 个输入变量。通过多元统计分析和模拟研究，我们分析了样本量（L）和输入维度对确定投资组合权重准确性的影响。我们发现，由于 N 均值和 N（N-1）/2 相关系数的不精确估计而导致的投资组合性能下降与 N/L 成正比；但是，N 方差的估计不会使结果恶化。为了减少无益的样本量/维度效应，我们对 N 个时间序列进行聚类，并将它们分成少数几个块。每个块由相互关联的 ATS 组成。它基于不可训练的 1/N 投资组合规则生成一个专家交易代理。为了增加专家代理的多样性，我们为聚类使用不同长度的训练集。在样本外投资组合验证实验的每个向前步骤中，在投资组合管理系统的输出中开发基于正则化均值-方差框架的融合代理。使用真实财务数据（2003-2012 年）的实验证实了所提出方法的有效性。
