Tensor Portfolios

Tae-Hwy Lee () and Tianyan Tu ()
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Tae-Hwy Lee: Department of Economics, University of California Riverside
Tianyan Tu: University of California, Riverside

No 202601, Working Papers from University of California at Riverside, Department of Economics

Abstract: Motivated by the multi-dimensional nature of financial data, we propose the tensor portfolios, a framework exploiting the intrinsic multi-way structure of stock returns to reduce the number of free parameters required for portfolio construction. We develop three distinct methods tailored to specific structural assumptions. We systematically compare tensor and vector portfolios through Monte Carlo simulations and empirical studies. The simulation results show tensor portfolios yield significantly higher out-of-sample Sharpe ratios whenever the data exhibits a tensor structure. Empirical analysis further corroborates the effectiveness of tensor portfolios; their general outperformance over vector portfolios in read-world markets highlights the practical significance of exploiting multi-way information.

Keywords: Tensor Portfolio Optimization; Kronecker Separability; High-Dimensionality; Tensor Factor Model; Tensor Graphical LASSO (search for similar items in EconPapers)
JEL-codes: C13 C40 C55 C58 G11 G17 (search for similar items in EconPapers)
Pages: 42 Pages
Date: 2026-03
New Economics Papers: this item is included in nep-rmg
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