论文标题
TPLVM：学生的$ t $ - 过程潜在变量型号的投资组合构建
TPLVM: Portfolio Construction by Student's $t$-process Latent Variable Model
论文作者
论文摘要
最佳资产分配是现代金融理论中的关键主题。为了意识到对投资者风险规避的最佳资产分配，已经提出了各种投资组合建设方法。最近，在金融领域，机器学习的应用正在迅速增长。在本文中，我们提出了学生的$ t $过程潜在变量模型（TPLVM），以通过较低维度的潜在变量来描述金融时间表的非高斯波动。随后，我们将TPLVM应用于最小变异组合，以替代现有的非线性因子模型。为了测试拟议的投资组合的性能，我们基于TPLVM或Gaussian Process Litable Varible模型来构建全球股票市场指数的最小值变量投资组合。通过比较这些投资组合，我们确认所提出的投资组合优于现有的高斯流程潜伏模型的投资组合。
Optimal asset allocation is a key topic in modern finance theory. To realize the optimal asset allocation on investor's risk aversion, various portfolio construction methods have been proposed. Recently, the applications of machine learning are rapidly growing in the area of finance. In this article, we propose the Student's $t$-process latent variable model (TPLVM) to describe non-Gaussian fluctuations of financial timeseries by lower dimensional latent variables. Subsequently, we apply the TPLVM to minimum-variance portfolio as an alternative of existing nonlinear factor models. To test the performance of the proposed portfolio, we construct minimum-variance portfolios of global stock market indices based on the TPLVM or Gaussian process latent variable model. By comparing these portfolios, we confirm the proposed portfolio outperforms that of the existing Gaussian process latent variable model.
