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.