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Title: Portfolio Optimization with Mean-reverting Assets: Combining Theory with Deep Learning.
Authors: Ye, Jing
Advisors: Mulvey, John M
Contributors: Operations Research and Financial Engineering Department
Subjects: Operations research
Issue Date: 2018
Publisher: Princeton, NJ : Princeton University
Abstract: We study the finite-horizon dynamic portfolio model involving mean reverting (Ornstein-Uhlenbeck) assets in the presence of transaction costs. The goal is to maximize the expected constant relative risk aversion (CRRA) utility of terminal wealth. We investigate the problem in two parts. In the first part, we analyze the problem without transaction costs for three types of portflios: we analytically solve the problem for a single risky asset and a risk free asset, and then derive the solution to a portfolio of OU assets and a portfolio of OU and geometric Brownian motion (GBM) assets via a system of ordinary differential equations (ODEs). In the second part, we extend the problem by adding proportioanl transaction cost. The portfolio optimization problem becomes intractable both analytically and computationally when there exist transaction costs and multiple assets. To address transaction costs, we propose a novel numerical approach employing deep neural networks, building on the previous ODE solution and given the standard no-trade zone policy rules. Our method readily extends to high-dimensional portfolio problems wherein traditional methods fail. Experiments with synthetic and market data show the numerical benefits of the developed algorithms.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog:
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Operations Research and Financial Engineering

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