Abstract
In this paper, we document a novel machine learning-based numerical framework to solve static and dynamic portfolio optimization problems, with, potentially, an extremely large number of assets. The framework proposed applies to general constrained optimization problems and overcomes many major difficulties arising in current literature. We not only empirically test our methods in U.S. and China A-share equity markets, but also run a horse-race comparison of some optimization schemes documented in (Homescu, 2014). We record significant excess returns, relative to the selected benchmarks, in both U.S. and China equity markets using popular schemes solved by our framework, where the conditional expected returns are obtained via machine learning regression, inspired by (Gu, Kelly & Xiu, 2020) and (Leippold, Wang & Zhou, 2021), of future returns on pricing factors carefully chosen.
Highlights
1.1 Outline of This PaperIn this paper, we propose a novel Monte-Carlo simulation and machine learning-based static and dynamic portfolio optimization framework
We propose a novel Monte-Carlo simulation and machine learning-based static and dynamic portfolio optimization framework
The framework consists of an input preparation module, a hierarchical clustering-based asset space decomposition module, a simulation and portfolio weights computation module and a profit and loss evaluation module
Summary
We propose a novel Monte-Carlo simulation and machine learning-based (unsupervised and supervised learning) static and dynamic portfolio optimization framework. This framework supports arbitrary objective functions and constraints, which can be either linear or nonlinear. The hierarchical or regular clustering-based asset space decomposition module tries to partition the entire asset universe, which often includes a large number of individual assets, based on a predetermined set of firm characteristics. This results in similar factor values for the assets in each cluster.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
