What You See Is Not What You Get: The Costs of Trading Market Anomalies

Abstract

Is there a gap between the profitability of a trading strategy "on paper" and that which can be achieved in practice? We answer this question by developing two new techniques to measure the real-world implementation costs of financial market anomalies.The first method extends Fama-MacBeth regressions to compare the on-paper returns to factor exposures with those achieved by mutual funds. The second method estimates average return differences between stocks and mutual funds matched on risk characteristics. Unlike existing approaches, these techniques deliver estimates of implementation costs without estimating parametric microstructure models from trading data or explicitly specifying factor trading strategies. After accounting for implementation costs, typical mutual funds earn low returns to value and no returns to momentum. JEL: G12, G14, G23

Sparse Signals in the Cross‐Section of Returns

“…Thus, theR 2,Bmk n = 7.365% out-of-sample fit for the AR(3) benchmark does not translate into economically meaningful performance. This finding is broadly consistent with the results in both Korajczyk and Sadka (2004) and Patton and Weller (2017), who give evidence that momentum profits disappear after adjusting for trading costs. The forecast-implied strategy associated with the market benchmark performs even worse than the one associated with the AR(3) benchmark.…”

When do low-frequency measures really measure transaction costs?

“…Our findings about the upward bias of the low-frequency measures raise questions about their suitability for optimal portfolio choice problems with transaction costs, where the so-called no-trade region is a function of the level of transaction costs (Constantinides, 1986), and for evaluation of trading strategies and asset pricing anomalies, where small changes in transaction costs have a large impact on performance (Novy-Marx and Velikov, 2015, Chen and Velikov, 2018, and Patton and Weller, 2018. The dependence of the lowfrequency measures on volatility limits their use in studies of the commonality in liquidity, where liquidity proxies are used to measure co-movements in liquidity across assets and markets (Chordia, Roll, and Subrahmanyam, 2000, Hasbrouck and Seppi, 2001, Korajczyk and Sadka, 2008, Karolyi, Lee, and Van Dijk, 2012, Mancini, Ranaldo, and Wrampelmeyer, 2013.…”

Methodology Of Quality Economy In The Knowledge Space

“…An agreed mode of digital transformation both in territorial aspect and sectoral aspect on a national economy scale as many interested and potentially interested participants as possible in adjustments, caused by digital transformation, creates conditions to change added-value chains and digitalization of the main economic processes (Li et al, 2019). In such conditions, traditional assets are revalued and inventoried, modifying assets' forms and content (Patton & Weller, 2020). The increasing impact of infrastructural and institutional factors resulted from such new categories as digital assets (Gagulina et al, 2020).…”