Scaling up Market Anomalies

Avramov, Doron; Cheng, Si; Schreiber, Amnon; Shemer, Koby

doi:10.3905/joi.2017.26.3.089

Cited by 51 publications

(3 citation statements)

References 35 publications

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“…Our results relate to McLean and Pontiff (2016), Avramov et al (2017), and Zaremba and Shemer (2017) who show that anomaly returns predict the cross section of anomaly returns at the one-month and one-year lags. Arnott et al (2021) show that short-term cross-sectional factor momentum explains shortterm industry momentum.…”

supporting

confidence: 61%

Factor Momentum and the Momentum Factor

Ehsani¹,

Linnainmaa²

2022

The Journal of Finance

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Momentum in individual stock returns relates to momentum in factor returns. Most factors are positively autocorrelated: the average factor earns a monthly return of six basis points following a year of losses and 51 basis points following a positive year. We find that factor momentum concentrates in factors that explain more of the cross section of returns and that it is not incidental to individual stock momentum: momentum‐neutral factors display more momentum. Momentum found in high‐eigenvalue principal component factors subsumes most forms of individual stock momentum. Our results suggest that momentum is not a distinct risk factor—it times other factors.

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supporting

confidence: 61%

Factor Momentum and the Momentum Factor

Ehsani¹,

Linnainmaa²

2022

The Journal of Finance

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“…Factor timing, which combines factor investing from the cross section of assets and market timing from time‐series predictability, has received much attention (Arnott et al, 2021; Avramov et al, 2017; Ehsani & Linnainmaa, 2022; Gupta & Kelly, 2019; Haddad et al, 2020; Leippold & Yang, 2021). Relative to static factor investing, the factor timing strategy improves portfolio performance significantly in capturing the autocorrelation structure and predictability among factors.…”

Section: Introductionmentioning

confidence: 99%

Timing the factor zoo via deep learning: Evidence from China

Liao

Jiang

2022

Accounting & Finance

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This paper proposes a factor timing strategy with information from 146 characteristic‐based factors and a deep learning approach to capture the nonlinear predictability. The deep learning‐based factor timing strategy generates the highest economic value compared with the unconditional and alternative linear machine learning‐based portfolios and remains robust after controlling for traditional factor models and transaction costs. With the unique market structure of the Chinese stock market, we find that mispricing‐based theory helps explain the factor timing via deep learning.

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“…Second, we demonstrate that the forecasts obtained using a Markov switching model may be successfully employed to predict performance and for the purposes of selecting the most promising equity anomaly strategies. Therefore, we offer a new anomaly-allocation instrument and expand upon the existing understanding of anomaly momentum (Avramov, Cheng, Schreiber, & Shemer, 2017;Ehsani, 2017;Zaremba & Szyszka, 2016), anomaly reversals (Arnott, Beck, & Kalesnik, 2016) and cross-sectional seasonalities in anomaly returns (Keloharju, Linnainmaa, & Nyberg, 2016;Zaremba, 2017a).…”

Section: Introductionmentioning

confidence: 99%

Predicting the performance of equity anomalies in frontier emerging markets: a Markov switching model approach

Czapkiewicz

Zaremba

Szczygielski

2019

Economic Research-Ekonomska Istraživanja

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Equity anomalies in frontier markets appear and disappear over time. This article aims to demonstrate the predictability of which of these transient anomalies will be profitable using a Markov switching model. To do so, we examine 140 equity anomalies identified in the literature using a unique sample of over 3,600 stocks from 23 frontier equity markets between 1997 and 2016. The application of a Markov switching model reveals that the time-series pattern of expected returns is dependent upon the type of anomaly; some anomalies become unprofitable over time whereas profitability increases in tandem with the development of a specific stock market for other types of anomalies. Results further indicate that forecasts of the next month's return obtained from this model can translate into profitable investment strategies. We find that an anomaly selection strategy that relies on the model produces abnormal returns and outperforms a naïve benchmark that considers all the anomalies. We go onto demonstrate that our results are robust.

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Scaling up Market Anomalies

Cited by 51 publications

References 35 publications

Factor Momentum and the Momentum Factor

Factor Momentum and the Momentum Factor

Timing the factor zoo via deep learning: Evidence from China

Predicting the performance of equity anomalies in frontier emerging markets: a Markov switching model approach

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