{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/112829"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/112829","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Tax-efficient asset management via loss harvesting","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Anastasov, Anton G"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Andrew W. Lo."],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-22T22:20:59Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (page 63)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Tax-efficient asset management via loss harvesting"]}]}],"canonical_facts":{"dc:contributor.advisor":["Andrew W. Lo."],"dc:contributor.department":["Massachusetts Institute of Technology. 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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."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/112829"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Tax-efficient asset management via loss harvesting"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:20:59Z"}