Portfolio management and optimal execution via convex optimization

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Abstract/Contents

Abstract
We study three related applications, in the field of finance, and in particular of multi-period investment management, of convex optimization and model predictive control. First, we look at the classical multi-period trading problem, consisting in trading assets within a certain universe for a sequence of periods in time. We develop a framework for single- and multi-period optimization: the trades in each period are found by solving a convex optimization problem that trades off expected return, risk, transaction cost and holding cost. Second, we look at the classical Kelly gambling problem, consisting in repeatedly allocating wealth among bets so as to maximize the expected growth rate of wealth. We develop a convex constraint that controls the risk of drawdown, i.e., the risk of losing a certain (high) amount of wealth. Third, we look at an optimal execution problem, consisting in buying, or selling, a given quantity of some asset on a limit-order book market. We study the case when the execution is benchmarked to the market volume weighted average price, and the objective is to minimize the mean-variance of the slippage. In all three cases, we provide extensive numerical simulations (using real-world data, whenever possible), developed as open-source software. In practice, these problems are solved to high accuracy in little time on commodity hardware, thanks to strong theoretical guarantees from modern convex optimization and a rich and growing ecosystem of open source software.

Description

Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2018; ©2018
Publication date 2018; 2018
Issuance monographic
Language English

Creators/Contributors

Author Busseti, Enzo
Degree supervisor Boyd, Stephen P
Thesis advisor Boyd, Stephen P
Thesis advisor Borland, Lisa Marina, 1963-
Thesis advisor Ye, Yinyu
Degree committee member Borland, Lisa Marina, 1963-
Degree committee member Ye, Yinyu
Associated with Stanford University, Department of Management Science and Engineering.

Subjects

Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Enzo Busseti.
Note Submitted to the Department of Management Science and Engineering.
Thesis Thesis Ph.D. Stanford University 2018.
Location electronic resource

Access conditions

Copyright
© 2018 by Enzo Busseti
License
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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