A Comprehensive Machine Learning Framework for Dynamic Portfolio Choice With Transaction Costs

Luca Gaegauf, Simon Scheidegger and Fabio Trojani
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Luca Gaegauf: University of Zurich
Fabio Trojani: University of Geneva; University of Turin; Swiss Finance Institute

No 23-114, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We introduce a comprehensive computational framework for solving dynamic portfolio choice problems with many risky assets, transaction costs, and borrowing and short-selling constraints. Our approach leverages the synergy between Gaussian process regression and Bayesian active learning to efficiently approximate value and policy functions with a novel, formal way of characterizing the irregularly-shaped no-trade region; we then embed this into a discrete-time dynamic programming algorithm. This combination allows us to study dynamic portfolio choice problems with more risky assets than was previously possible. Our results indicate that giving the agent access to more assets may alleviate some illiquidity resulting from the presence of transaction costs.

Keywords: Machine learning; computational finance; computational economics; Gaussian process regression; dynamic portfolio optimization; transaction costs; liquidity premia (search for similar items in EconPapers)
JEL-codes: C61 C63 C68 E21 (search for similar items in EconPapers)
Pages: 70 pages
Date: 2023-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-dcm
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp23114

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