Fast and Slow Arbitrage: Fund Flows and Mispricing in the Frequency Domain

Dong, Xi; Kang, Namho; Peress, Joël

doi:10.2139/ssrn.3675163

Cited by 6 publications

(4 citation statements)

References 88 publications

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“…As Pedersen (2013, 2016) and Dong, Kang, and Peress (2020) argue, frictions such as limited risk-bearing capacity and transaction costs induce arbitrageurs to slowly respond to mispricing, resulting in MCP. If our predictability finding is driven by arbitrageurs slowly correcting mispricing in the presence of asymmetric limits of arbitrage and stronger MCP for overpricing vis-à-vis underpricing, then long-short anomaly portfolio returns should contain more relevant information for predicting the market excess return during times of high frictions.…”

Section: Market Frictionsmentioning

confidence: 99%

“…Limits of arbitrage emerge from a variety of frictions (Gromb and Vayanos (2010)). In the models of Pedersen (2013, 2016) and Dong, Kang, and Peress (2020), frictions such as limited risk-bearing capacity and transactions costs induce arbitrageurs to correct mispricing only slowly, resulting in MCP. To examine the relevance of limits of arbitrage, we analyze how various frictions are related to the out-of-sample predictive ability of longshort anomaly portfolio returns.…”

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confidence: 99%

“…From a behavioral perspective,Chan, Jegadeesh, and Lakonishok (1996);Daniel, Hirshleifer, and Subrahmanyams (1998);Hong and Stein (1999); andDa, Gurun, and Warachka (2014) explain return momentum as underreaction to news. Pedersen (2013, 2016) andDong, Kang, and Peress (2020) show that risk and/or cost considerations lead arbitrageurs to slowly allocate capital to correct mispricing.11 When the mispricing correction assumption in equation (6) does not hold, mispricing in response to a shock can be exacerbated after anomaly portfolio formation. Equation (9) still applies, so that cov( u l,t+1 , u l,t ) > 0 again requires the expression in brackets-which depends on the successive pairs of return responses to a mispricing shock-to be positive.…”

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confidence: 99%

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Anomalies and the Expected Market Return

Dong¹,

Li²,

Rapach³

et al. 2021

The Journal of Finance

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115

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We provide the first systematic evidence on the link between long-short anomaly portfolio returns-a cornerstone of the cross-sectional literature-and the timeseries predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high-dimensional setting. We find that longshort anomaly portfolio returns evince statistically and economically significant outof-sample predictive ability for the market excess return. The predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing correction persistence. STOCK RETURN PREDICTABILITY IS A fundamental topic in finance. Two major-and voluminous-lines of research focus on this subject. The first examines whether firm characteristics can predict the cross-sectional dispersion in stock returns. These studies identify numerous equity market anomalies (e.g., Fama and French (2015), Harvey, Liu, and Zhu (2016), Pontiff (2016), Hou, Xue, andZhang (2020)). The second line of research investigates the time-series predictability of the aggregate market excess return based on a variety of economic and financial variables, such as valuation ratios, interest rates, and inflation (e.g., Nelson (1976), Campbell (1987, Fama and French

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Section: Market Frictionsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Anomalies and the Expected Market Return

Dong¹,

Li²,

Rapach³

et al. 2021

The Journal of Finance

Self Cite

115

View full text Add to dashboard Cite

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“…A 1-standard-deviation increase in the aggregate AUM of all hedge funds leads to an increase in their quarterly trading intensity in the anomaly by up to 0.8% of the total shares outstanding. These effects are economically significant, especially given recent evidence that hedge fund transactions are most effective, among all investors, in affecting asset prices (Dong, Kang, and Peress (2020), Koijen, Richmond, and Yogo (2022)). We also separately analyze the effects on the long and short legs of the anomalies.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Discovery and Arbitrage Trading

Dong¹,

Liu²,

Lu³

et al. 2023

J. Financ. Quant. Anal.

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We analyze a model in which an anomaly is unknown to arbitrageurs until its discovery, and test the model implications on both asset prices and arbitrageurs’ trading activities. Using data on 99 anomalies documented in the existing literature, we find that the discovery of an anomaly reduces the correlation between the returns of its decile-1 and decile-10 portfolios. This discovery effect is stronger if the aggregate wealth of hedge funds is more volatile. Finally, hedge funds increase (reverse) their positions in exploiting anomalies when their aggregate wealth increases (decreases), further suggesting that these discovery effects operate through arbitrage trading.

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