Did the Profitability of Momentum and Reversal Strategies Decline with Arbitrage Costs After the Turn of the Millennium?

Abstract

is a professor of finance in the School of Management at SUNY-Buffalo in Buffalo, NY. joeogden@buffalo.edu A rbitrageurs identify and trade on mispriced securities, and their trades contribute to the efficiency of security prices (Samuelson [1965], Fama [1970], Kyle [1985]). Arbitrage is costly, however, and these costs drive a wedge between actual market prices and efficient prices; i.e., pricing anomalies can exist even if all market participants are rational, though their size should be bounded by arbitrage costs (Fama [1991], Shleifer and Vishny [1997]).This article investigates the effect of arbitrage costs on the profitability of three well-known arbitrage strategies: momentum, long-term reversal, and short-term reversal. All three strategies involve the development of hedge portfolios based on past returns. The momentum strategy involves long and short positions, respectively, in stocks with the highest and lowest intermediate-term (three to twelve months) past returns; the long-term reversal strategy involves long and short positions, respectively, in stocks with lowest and highest long-term (three to five years) past returns; and the short-term reversal strategy involves long and short positions, respectively, in stocks with the lowest and highest one-month past returns.Jegadeesh and Titman [1993] were first to document evidence of the profitability of the momentum strategy, using data on NYSE

Discontinued Positive Feedback Trading and the Decline of Momentum Profitability

“…In the second part of the analysis, we zoom in on a narrow window around the June 3 Prior work has identified many other mechanisms that may cause factor returns to decline: changes in liquidity (Khandani and Lo, 2011;Chordia, Subrahmanyam, and Tong, 2014;Lee and Ogden, 2015), and increased arbitrage activity (Marquering, Nisser, and Valla, 2006;Green, Hand, and Soliman, 2011;Hanson and Sunderam, 2013;McLean and Pontiff, 2016;Calluzzo, Moneta, and Topaloglu, 2019;Cho, 2020). Several studies propose that some factors may result from possible data-mining or overfitting (Harvey, Liu, and Zhu, 2016;Harvey, 2017;Hou, Xue, and Zhang, 2020;Huang, Song, and Xiang, 2020b;Falck, Rej, and Thesmar, 2021).…”