Tax-efficient asset management via loss harvesting

In this thesis, we study loss-harvesting--an investment strategy that realizes capital losses immediately but defers realizing capital gains as long as possible. We begin by describing a computational framework for studying the properties of loss-harvesting empirically. The main advantage of our framework is flexibility. In particular, our framework is independent of any particular choice of a source for stock return time series. After combining the framework with the Capital Asset Pricing Model as a source for simulated stock returns data, we perform a thorough sensitivity analysis and study the performance of loss-harvesting under various conditions of the financial market. By combining the framework with historical stock return time series from the S&P 500 Index, we study the performance of loss-harvesting from a different and more practical, point of view. Through this empirical exploration, we identify three new findings about loss-harvesting: (1) introducing a transaction cost rate of 1% reduces alpha by about 50% after taxes; (2) introducing regular cash contributions reduces alpha after taxes; and (3) under specific market conditions, a simple passive buy-and-hold investment strategy outperforms loss-harvesting.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Anastasov, Anton G

Advisor dc:contributor.advisor

• Andrew W. Lo.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/112829

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/112829