Asset Allocation via Machine Learning and Applications to Equity Portfolio Management

Qing Yang, Zhenning Hong, Ruyan Tian (), Tingting Ye and Liangliang Zhang

Papers from arXiv.org

Abstract: In this paper, we document a novel machine learning based bottom-up approach for static and dynamic portfolio optimization on, potentially, a large number of assets. The methodology applies to general constrained optimization problems and overcomes many major difficulties arising in current optimization schemes. Taking mean-variance optimization as an example, we no longer need to compute the covariance matrix and its inverse, therefore the method is immune from the estimation error on this quantity. Moreover, no explicit calls of optimization routines are needed. Applications to equity portfolio management in U.S. and China equity markets are studied and we document significant excess returns to the selected benchmarks.

Date: 2020-11, Revised 2020-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-rmg and nep-upt
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Journal Article: Asset Allocation via Machine Learning (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2011.00572

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