Factor Momentum and the Momentum Factor

Ehsani, Sina; Linnainmaa, Juhani T.

doi:10.1111/jofi.13131

Cited by 116 publications

(54 citation statements)

References 79 publications

(99 reference statements)

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“…We confirm the main findings in Arnott et al. (2019) and Ehsani and Linnainmaa (2021) that FMOM exists as a whole. We take a step further and examine the pervasiveness of factor momentum effect at the individual factor level.…”

Section: Introductionsupporting

confidence: 92%

“…Arnott et al. (2019) and Ehsani and Linnainmaa (2021) documented a strong and pervasive momentum effect in most financial market anomalies, called factor momentum (FMOM). A FMOM strategy is long recent top‐performing factors and short poorly performing factors.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is by nature a type of factor timing strategy. Recent literature shows that FMOM has significant investment performance compared to traditional individual stock momentum (Gupta & Kelly, 2019) and that factor momentum can explain both individual stock momentum and industry momentum (Arnott et al., 2019; Ehsani & Linnainmaa, 2021). This finding seems to contradict the conventional view that timing factors are expensive and have difficulty beating a naive buy‐and‐hold (BAH) portfolio; see, for example, Asness (2016a) and Asness (2016b).…”

Section: Introductionmentioning

confidence: 99%

“…This study examines the strength and profitability of factor momentum based on two datasets: (i) the original set of 22 factors (EL) same as in Ehsani and Linnainmaa (2021) and (ii) a more comprehensive set of 187 factors (HXZ) of Hou et al. (2021).…”

Section: Introductionmentioning

confidence: 99%

“…For both EL and HXZ samples, we construct time series FMOM portfolios following Ehsani and Linnainmaa (2021). We first present evidence supporting the thesis of Ehsani and Linnainmaa (2021) that the momentum anomaly of Jegadeesh and Titman (1993) is an aggregation of the autocorrelation of the remaining financial anomalies instead of an independent factor. However, this factor momentum effect is weak on average over the past five decades.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A reexamination of factor momentum: How strong is it?

Fan

Liao

et al. 2022

The Financial Review

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Recent studies show that most financial market anomalies exhibit a momentum effect. Based on two datasets, (i) an original 22-factor sample and (ii) a more comprehensive 187-factor sample, we find that factor momentum effect is weak at the individual factor level. In both samples, only about 22%-27% of the factors exhibit strong return continuation and dominate the factor momentum portfolio while the remaining factors do not. The factor momentum strategies do not outperform the corresponding long-only strategies in either sample. The choice of factors affects the ability of factor momentum to explain individual stock momentum.

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Section: Introductionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A reexamination of factor momentum: How strong is it?

Fan

Liao

et al. 2022

The Financial Review

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Return predictability via an long short‐term memory‐based cross‐section factor model: Evidence from Chinese stock market

Yao,

Xia,

Liu

2024

Journal of Forecasting

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This paper proposes a cross‐section long short‐term memory (CS‐LSTM) factor model to explore the possibility of estimating expected returns in the Chinese stock market. In contrast to previous machine‐learning‐based asset pricing models that make predictions directly on equity returns, CS‐LSTM estimates are based on predictions of slope terms from Fama–MacBeth cross‐section regressions using 16 stock characteristics as factor loadings. In line with previous studies in the context of the Chinese market, we find illiquidity and short‐term momentum to be the most important factors in describing asset returns. By using 274 value‐weighted portfolios as test assets, we systematically compare the performances of CS‐LSTM and three other candidate models. Our CS‐LSTM model consistently delivers better performance than the candidate models and beats the market at all different levels of transaction costs. In addition, we observe that assets with smaller cap are favored by the model. By repeating the empirical analysis based on the top 70% of stocks, our CS‐LSTM model remains robust and consistently provides significant market‐beating performance. Our findings from the CS‐LSTM model have practical implications for the future development of the Chinese stock market and other emerging markets.

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Factor timing in the Chinese stock market

2024

International Studies of Economics

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I conduct an exploratory study about the feasibility of factor timing in the Chinese stock market, covering 24 representative and well‐identified risk factors in 10 categories from the literature. The long–short portfolio of short‐term reversal exhibits strong out‐of‐sample predictability, which is robust across various models and all types of predictors. This predictability is significant both statistically and economically, with a simple investment strategy obtaining its return three times higher than the buy‐and‐hold return in the sample period and a significant annualized 20.4% CH‐3 alpha. Portfolio historical volatility and market volatility measurement predictors play crucial roles in the reversal factor premium's robust predictability. However, such results are not evident in predicting all other factors' long–short portfolios as well as all factors' long‐wing and short‐wing portfolios, and this failure cannot be attributed to their exposure to unpredictable market returns.

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Factor Momentum and the Momentum Factor

Cited by 116 publications

References 79 publications

A reexamination of factor momentum: How strong is it?

A reexamination of factor momentum: How strong is it?

Return predictability via an long short‐term memory‐based cross‐section factor model: Evidence from Chinese stock market

Factor timing in the Chinese stock market

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