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TPLVM: Portfolio Construction by Student’s t-Process Latent Variable Model

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MAZIN Inc., 3-29-14 Nishi-Asakusa, Taito City, Tokyo 111-0035, Japan
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NOMURA Asset Management Co. Ltd., 1-12-1 Nihonbashi, Chuo City, Tokyo 103-8260, Japan
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Author to whom correspondence should be addressed.
Mathematics 2020, 8(3), 449; https://doi.org/10.3390/math8030449
Received: 29 January 2020 / Revised: 10 March 2020 / Accepted: 12 March 2020 / Published: 19 March 2020
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 portfolio construction as an alternative of existing nonlinear factor models. To test the performance of the proposed method, 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. View Full-Text
Keywords: student’s t-process; latent variable model; factor model; Portfolio theory; global stock markets student’s t-process; latent variable model; factor model; Portfolio theory; global stock markets
MDPI and ACS Style

Uchiyama, Y.; Nakagawa, K. TPLVM: Portfolio Construction by Student’s t-Process Latent Variable Model. Mathematics 2020, 8, 449.

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