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arXiv · 2011.00572

Asset Allocation via Machine Learning and Applications to Equity Portfolio Management

Qing Yang·Zhenning Hong·Ruyan Tian·Tingting Ye·Liangliang Zhang
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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.

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Qing Yang, Zhenning Hong, Ruyan Tian, Tingting Ye, Liangliang Zhang. 2020-11-01. Asset Allocation via Machine Learning and Applications to Equity Portfolio Management. https://arxiv.org/abs/2011.00572

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