TPLVM: Portfolio Construction by Student's $t$-process Latent Variable Model
This is an incremental improvement for finance practitioners seeking better portfolio optimization methods.
The authors tackled the problem of non-Gaussian fluctuations in financial timeseries by proposing the Student's $t$-process latent variable model (TPLVM) for portfolio construction, and found that it outperformed the Gaussian process latent variable model in minimum-variance portfolios of global stock market indices.
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.