Firm-specific versus systematic momentum Frank Graef a,*, Daniel Hoechle a, Markus Schmid b,c,d a University of Applied Sciences Northwestern Switzerland, Institute for Finance, CH-4002 Basel, Switzerland b Swiss Institute of Banking and Finance, University of St. Gallen, CH-9000, St. Gallen, Switzerland c Swiss Finance Institute (SFI), Geneva, Switzerland d European Corporate Governance Institute (ECGI), Brussels, Belgium A R T I C L E I N F O JEL classification: G14 Keywords: Factor momentum Firm-specific momentum Factor timing A B S T R A C T We decompose stock returns into a systematic and a firm-specific component and show that the dynamics of the firm-specific return component drive the well-known stock momentum anomaly. Our results are robust to the use of a variety of prominent factor models for return decomposition. Furthermore, we find that momentum profits are largely unaffected when the investment uni verse is restricted to stocks with inconspicuous factor loadings. Our empirical findings call into question the transmission mechanism from factor momentum to stock momentum proposed in recent research. 1. Introduction Stock momentum (e.g., Jegadeesh and Titman, 1993) appears to be at odds with the weakest form of market efficiency, according to which future returns should not be predictable based on historical price movements. Since prominent factor models fail to explain momentum profits, behavioral explanations have been proposed. However, momentum has been shown to exist in the cross-section of characteristics-sorted portfolios that contain a large number of stocks and, as noted by Lewellen (2002), biased reactions to firm-specific news arguably cannot explain momentum as a portfolio-level phenomenon. Against this background, Ehsani and Linnainmaa (2022; henceforth "EL") propose an alternative explanation for the existence of stock momentum: They find that momentum originates at the level of equity risk factors and that this factor momentum transmits into the cross-section of stock returns. In this paper, we empirically test the transmission mechanism proposed by EL, whereby factor momentum translates into stock momentum through autocovariance in factors, amplified by the cross-sectional variation in betas. To this end, we first investigate the performance of stock-level strategies, using a holding period of one month after a formation period over month t − 1 (“short-term”) or months t − 12 to t − 2 (“medium-term”). Our sample consists of monthly U.S. stock returns from CRSP over the period of July 1963 to December 2019. We then sort portfolios by systematic and idiosyncratic stock returns. To do so, we estimate Fama and French (2015) five-factor model betas on a rolling basis and compute expected (i.e., systematic) returns. Firm-specific (i.e., idiosyncratic) returns then correspond to the difference between total returns and expected returns. If EL’s We are grateful to two anonymous referees, Samuel Vigne (the Editor), Martin Brown, Bruce Grundy, Tim Kroencke, Luca Liebi, Bharat Parajuli, Paul Whelan, Gulnara Zaynutdinova and conference participants at the 2022 Financial Management Association (FMA) European Conference, the 2022 Swiss Finance Institute (SFI) Research Days, the 2022 Paris Financial Management Conference (PFMC), the 2022 European Financial Man agement Association (EFMA) Annual Meeting and seminar participants at the University of St. Gallen and the University of Konstanz for helpful comments. * Corresponding author. E-mail addresses: frank.graef@fhnw.ch (F. Graef), daniel.hoechle@fhnw.ch (D. Hoechle), markus.schmid@unisg.ch (M. Schmid). Contents lists available at ScienceDirect Finance Research Letters journal homepage: www.elsevier.com/locate/frl https://doi.org/10.1016/j.frl.2025.106963 Received 18 October 2024; Received in revised form 11 December 2024; Accepted 12 February 2025 Finance Research Letters 76 (2025) 106963 Available online 13 February 2025 1544-6123/© 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). mailto:frank.graef@fhnw.ch mailto:daniel.hoechle@fhnw.ch mailto:markus.schmid@unisg.ch www.sciencedirect.com/science/journal/15446123 https://www.elsevier.com/locate/frl https://doi.org/10.1016/j.frl.2025.106963 https://doi.org/10.1016/j.frl.2025.106963 http://crossmark.crossref.org/dialog/?doi=10.1016/j.frl.2025.106963&domain=pdf http://creativecommons.org/licenses/by/4.0/ conjecture about the transmission from factor momentum to stock momentum is correct, systematic returns should be a better pre dictor for future performance than idiosyncratic returns. We, however, find the opposite: Over a medium-term formation period, systematic returns are not informative about future performance, while idiosyncratic returns are. Alternative rolling window periods for beta estimation and variations in the factor model used for return decomposition yield qualitatively similar results. Moreover, we replicate the results of EL, who double-sort by firm size and idiosyncratic returns to construct “UMD-style” residual momentum strategies. In extensions to this replication, we then find that systematic returns also lack outperformance when using this alternative construction method. Second, we split our sample into two subsamples. The first subsample includes all stock-months with very large (> 80th percentile) or very small (< 20th percentile) loadings on at least one of the five Fama and French (2015) factors. The second subsample consists of all remaining observations. If the transmission from factor momentum to stock momentum works as EL hypothesize, we would expect momentum profits to be higher, when there is large variation in stocks’ betas. However, we find that momentum profits in the two subsamples are quite similar. We contribute to the literature on factor momentum (Gupta and Kelly, 2019; Ehsani and Linnainmaa, 2022; Arnott et al., 2023), by showing that stock momentum is driven by momentum in firm-specific returns and that the dispersion in betas does not affect mo mentum performance. Our finding that a larger fluctuation in the factor betas of a momentum strategy does not necessarily imply better performance also contributes to a literature that examines the time-varying risk exposures of momentum strategies (Wang and Wu, 2011; Daniel and Moskowitz, 2016) and to a literature that analyzes idiosyncratic momentum and short-term reversal (Gutierrez and Prinsky, 2007; Blitz et al., 2011; Da et al., 2011, 2014). Moreover, our paper relates to Grundy and Martin (2001) who conduct a thorough investigation of the risks and rewards of stock momentum, ruling out dynamic factor exposures, industry effects, or persistent cross-sectional differences in expected returns as primary causes of the strategy’s profitability. The remainder of the paper is organized as follows: Section 2 describes the transmission mechanism proposed by EL, Section 3 describes our data, Section 4 presents our analysis and results, and Section 5 concludes. 2. The transmission from factor momentum and idiosyncratic momentum to stock momentum Consider a stock momentum strategy that chooses investment weights in proportion to stocks’ past returns relative to the cross- sectional mean (Lo and MacKinlay, 1990). If stocks’ expected returns are generated by some F-factor model, EL show that the following relationship holds for the expected performance of this strategy, implemented in a cross-section of N stocks: E [ πmom t ] = ∑F f=1 [cov ( rf − t , rf t ) σ2 βf ] + 1 N ∑N i=1 [cov ( εi,− t , εi,t ) ] + ∑F f=1 ∑F g∕=f [ cov ( rf − t , rg t ) cov ( βf , βg)]+ σ2 η , (1) where rf t is the return of factor f in month t, βf are stocks’ loadings towards factor f , εi,t are stocks’ idiosyncratic returns, η represents stocks’ unconditional expected returns and − t refers to the formation period (e.g., months t − 12 to t − 2). Factor momentum may translate into stock momentum via the first term in Eq. (1), i.e., via the multiplication of factor autocor relations cov ( rf − t , rf t ) with the cross-sectional variance in betas σ2 βf . This is the channel proposed by EL. To illustrate this transmission mechanism, assume that expected returns are determined by stocks’ loadings against a serially uncorrelated market factor and a positively autocorrelated size factor (SMB). Further assume no autocorrelation in firm-specific returns and no persistent differences in unconditional mean returns. If small-cap stocks then outperform large-cap stocks in the past, they will continue to do so in the following months. Stock momentum may arise in this scenario, because stocks with a large positive (negative) SMB loading tend to be sorted into the top (bottom) momentum portfolio. The strength of this channel depends on the cross-sectional dispersion in betas. For example, one would expect a stock momentum strategy to become less profitable if it is restricted to the subsample of large-cap stocks with similar SMB betas. According to EL, it is therefore the expected (i.e., systematic) component of stock returns that picks up autocorrelation in factors and drives stock momentum. We empirically test this conjecture by running a horse race between this first component and the second component in Eq. (1), the latter representing idiosyncratic (or firm-specific) momentum. We thus directly test the relative importance of systematic (or factor) versus idiosyncratic momentum. 3. Data and factor construction We merge monthly U.S. stock returns from CRSP with annual accounting information from Compustat. We collect data for all NYSE, AMEX, and Nasdaq securities listed as ordinary common stock at any time during the sample period from July 1963 to December 2019. Following Hou et al. (2020), we exclude financial firms and firms with negative book equity. To avoid a look-ahead bias, we lag Compustat accounting information by six months, such that year-end figures become available at the end of June of next year (Fama and French, 1992). We use CSRP delisting returns. If delisting returns are missing and the delisting is performance-related, we set them to − 30 % (Shumway, 1997; Beaver et al., 2007). We retrieve factor returns of the Fama and French (1993, 2015) three- and five-factor models from Ken French’s webpage.1 Stambaugh and Yuan (2017) mispricing factors are obtained from Robert Stambaugh’s 1 https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html F. Graef et al. Finance Research Letters 76 (2025) 106963 2 webpage.2 Hou et al. (2021) augmented q-model factors are taken from the global-q data library.3 4. Empirical results 4.1. Return-based strategies using individual stocks and factor portfolios To implement the stock momentum strategy, we rank stocks by their returns over the last year, skipping the month before the ranking (rt− 12,t− 2). To test for short-term reversal, we rank stocks by their returns in the last month (rt− 1). We then sort stocks into quintile portfolios. In the value-weighted specification, we use NYSE breakpoints and compute value-weighted average returns for each quintile, using stocks’ lagged market equity. In the equal-weighted specification, we use unconditional breakpoints. To compute strategy returns, we go long (short) the top (bottom) quintile portfolio for the current month, with monthly rebalancing. Table 1 reports the results. The value-weighted momentum (short-term reversal) strategy achieves a statistically significant monthly return of 0.66 % (− 0.34 %). After adjusting for exposure to the Fama and French (2015) factors, momentum returns increase, whereas short-term reversal returns become smaller and statistically insignificant. In the equal-weighted setup, momentum (short- term reversal) performance amounts to 0.85 % (− 1.55 %) per month, with a statistically significant alpha.4 4.2. Portfolio sorts by systematic and idiosyncratic returns We decompose stock returns into a systematic and an idiosyncratic component, where the former represents the expected return, given stocks’ current factor loadings, and the latter represents the firm-specific return that is unexplained by factor loadings. We estimate firms’ loadings ̂β f i, t against the F = 5 factors of Fama and French (2015), using a five-year rolling period from month t − 60 to t − 1. Each beta estimate is based on at least 36 past-return observations. Systematic and idiosyncratic returns are computed as follows: Short − term systematic returns : r̂ i,t− 1 = ∑F f=1 β̂ f i, tr f t− 1 (2) Short − term idiosyncratic returns : ε̂i,t− 1 = re i,t− 1 − r̂ i,t− 1 (3) Medium − term systematic returns : r̂ i,t− 12,t− 2 = ∑− 2 j=− 12 r̂ i,t− j (4) Medium − term idiosyncratic returns : ε̂ i,t− 12,t− 2 = ∑− 2 j=− 12 ( re i,t− j − r̂ i,t− j ) , (5) where re i,t are monthly stock returns in excess of the risk-free rate and rf t denotes monthly factor premia. We rank stocks by one of the return measures defined in Eqs. (2) to (5) each month, sort them into value-weighted quintile portfolios and invest long (short) in the top (bottom) quintile portfolio for the subsequent month. Table 1 Return-based strategies using individual stocks. Investment universe Investment rule Short-term returns (rt− 1) Medium-term returns (rt− 12,t− 2) Individual stocks (value-weighted) High quintile - Low quintile (Q5-Q1) r ¡0.337 0.660 (− 2.19) (3.05) αFF5F − 0.190 0.755 (− 1.06) (2.83) Individual stocks (equal-weighted) High quintile - Low quintile (Q5-Q1) r ¡1.548 0.846 (− 7.87) (3.52) αFF5F ¡1.518 0.765 (− 5.21) (2.45) This table shows monthly returns and Fama and French (2015) model alphas of short-term reversal and medium-term momentum strategies using individual stocks. The sample period is July 1966 to December 2019. t-values (in parentheses) are based on Newey-West standard errors with three lags. Bold numbers indicate statistical significance at the 5 % level or higher. 2 http://finance.wharton.upenn.edu/~stambaug/ 3 http://global-q.org/index.html 4 We report short-term reversal returns with a negative sign for analytical reasons. Of course, by switching the long and short positions, we get an equivalent positive return. F. Graef et al. Finance Research Letters 76 (2025) 106963 3 Table 2 reports the returns of the quintile portfolios and of the high-low quintile strategies. Sorting by short-term idiosyncratic returns (ε̂i,t− 1) delivers a statistically significant short-term reversal return of − 0.78 % per month, which is more than twice as large compared to the conventional value-weighted short-term reversal strategy analyzed in Table 1. In contrast, sorting by short-term systematic returns (r̂ i,t− 1) results in a statistically significant positive return of 0.38 %. Sorting by medium-term idiosyncratic returns (ε̂i,t− 12, t− 2) yields a statistically significant return of 0.57 %, which is only slightly smaller than the 0.66 % return of the conventional value-weighted momentum strategy in Table 1. When we instead sort by medium-term systematic returns (r̂ i,t− 12, t− 2), the return of 0.20 % is statistically insignificant. Results are qualitatively identical for the five-factor model alphas.5 Fig. 1 tracks the cumulative performance of a $1 investment into the (value-weighted) individual-stock strategies analyzed in Tables 1 and 2. The finding of statistically insignificant returns and alphas when we sort by medium-term systematic returns is noteworthy: If stock momentum is caused by factor momentum, we would expect positive predictability for exactly this measure. However, this is not what we find. To the contrary, sorting by medium-term idiosyncratic returns largely captures the performance of a conventional momentum strategy. Taken together, the results in Table 2 suggest that stock momentum is unlikely to be driven by the transmission mechanism from factor momentum to stock momentum as proposed by EL. Stock momentum rather appears to stem from firm-specific return patterns. If there is a spillover from factor momentum to stock momentum, it is limited to a one-month time frame, as evidenced by significant short-term systematic momentum. Furthermore, Table 2 shows that this effect is dominated by strong short-term reversal in idio syncratic returns, leading to an overall negative alpha of the short-term reversal strategy in Table 1. Table 2 Portfolio sorts by systematic and idiosyncratic returns: Baseline results. Quintile returns Idiosyncratic returns Systematic returns Short-term (ε̂t− 1) Medium-term ( ε̂t− 12,t− 2 ) Short-term (r̂t− 1) Medium-term ( r̂t− 12,t− 2 ) Q1 1.357 0.635 0.828 0.831 Q2 1.227 0.985 0.930 1.001 Q3 0.926 0.864 1.063 1.064 Q4 0.789 0.926 1.083 1.084 Q5 0.575 1.206 1.211 1.031 Q5-Q1 r ¡0.783 0.571 0.383 0.200 (− 6.13) (3.24) (2.19) (1.00) αFF5F ¡0.579 0.937 0.414 0.040 (− 4.41) (5.65) (2.35) (0.17) This table shows monthly returns of quintile portfolios sorted on idiosyncratic or systematic stock returns, as well as the performance of the cor responding high-low quintile strategies. We compute systematic and idiosyncratic returns according to Eqs. (2)-(5). The sample period is July 1966 to December 2019. t-values (in parentheses) are based on Newey-West standard errors with three lags. Bold numbers indicate statistical significance at the 5 % level or higher. Fig. 1. Cumulative performance of individual-stock investment strategies. This figure shows the performance of a $1 investment into the (value-weighted) individual-stock strategies analyzed in Tables 1 and 2. We adjust the leverage of each strategy to a realized annual volatility of 10 %. The y-axis is shown in logarithmic scale. The sample period is July 1966 to December 2019. 5 For the strategies with a significant positive (negative) performance, portfolio returns increase (decrease) fairly monotonously from Q1 to Q5, making it less likely that the results are spurious (Patton and Timmermann, 2010). F. Graef et al. Finance Research Letters 76 (2025) 106963 4 4.3. Robustness In this section, we conduct a series of robustness tests. First, we test whether our results are sensitive to the estimation window over which betas are estimated. To this end, we repeat the analysis in Table 2. However, we now estimate betas using rolling window regressions over months t − 73 to t − 13 or months t − 36 to t − 1, rather than over months t − 60 to t − 1. The results, which are displayed in Panel A of Table 3, are qualitatively similar to those in Table 2. Next, we test the robustness of our results against the choice of the factor model used for the return decomposition. In particular, we use the CAPM, the Fama and French (1993) three-factor model, the augmented q-factor model of Hou et al. (2021), and the Stambaugh and Yuan (2017) mispricing factor model to derive systematic and idiosyncratic returns. We then re-estimate the analysis in Table 2. Results in Panel B of Table 3 show that medium-term systematic returns (r̂ i,t− 12, t− 2) consistently lack any return predictability, whereas medium-term idiosyncratic momentum (ε̂t− 12,t− 2) returns and alphas are positive and statistically significant for all factor models. In Table 7 of their paper, EL construct “UMD-style” residual momentum strategies. They interpret the results as evidence for idiosyncratic momentum arising due to “omitted-factor” momentum, since the performance of these strategies decreases when additional factors are added to the factor model used to compute idiosyncratic returns. Comparing our results for the Fama and French Table 3 Portfolio sorts by systematic and idiosyncratic returns: Robustness. Panel A: Alternative rolling periods Q5-Q1 Idiosyncratic returns Systematic returns Short-term (ε̂t− 1) Medium-term (ε̂t− 12,t− 2) Short-term (r̂t− 1) Medium-term (r̂t− 12,t− 2) Fama & French (2015) beta estimation from t − 72 to t − 13 r -0.730 0.476 0.570 0.126 (-5.62) (2.74) (3.56) (0.74) αFF5F -0.567 0.744 0.610 -0.056 (-4.14) (3.94) (3.72) (-0.28) Fama & French (2015) beta estimation from t − 36 to t − 1 r -0.694 0.513 0.138 0.321 (-5.36) (3.07) (0.79) (1.65) αFF5F -0.483 0.852 0.141 0.230 (-3.74) (5.30) (0.79) (1.00) Panel B: Alternative factor models Market model (CAPM) r -0.564 0.701 0.570 -0.126 (-3.87) (3.71) (2.55) (-0.50) αFF5F -0.410 0.889 0.727 -0.205 (-2.36) (3.87) (2.93) (-0.73) Fama & French (1993) 3-factor r -0.758 0.537 0.539 0.191 (-5.87) (3.11) (2.69) (0.93) αFF5F -0.607 0.738 0.681 0.145 (-4.55) (3.82) (3.12) (0.66) Hou et al. (2021) 5-factor r -0.657 0.458 0.383 0.227 (-5.11) (2.76) (2.16) (1.23) αFF5F -0.471 0.701 0.550 0.198 (-3.40) (4.01) (3.03) (0.95) Stambaugh & Yuan (2017) 4-factor r -0.690 0.496 0.476 0.047 (-5.48) (2.65) (2.50) (0.23) αFF5F -0.506 0.849 0.503 -0.156 (-3.49) (4.10) (2.50) (-0.73) Panel C: UMD-style strategies High (SC+LC) - Low (SC+LC) Estimates from EL (2022) Table 7 Our estimates Idiosyncratic returns Idiosyncratic returns Systematic returns Market model (CAPM) r 0.58 0.607 -0.141 (4.29) (4.23) (-0.77) Fama & French (1993) 3-factor r 0.44 0.478 0.016 (3.83) (3.58) (0.11) Fama & French (2015) 5-factor r 0.37 0.383 0.056 (3.39) (3.01) (0.42) The table presents the results of robustness tests for the strategies analyzed in Table 2. In Panel A, betas against the Fama and French (2015) model factors are determined using rolling window regressions over months t − 72 to t − 13 or months t − 36 to t − 1. In Panel B, we replace the Fama and French (2015) model with several widely-used factor models when computing systematic and idiosyncratic returns. In the second and third column of Panel C, we implement UMD-style strategies. We form six portfolios by size (< median; ≥ median) and past idiosyncratic returns (< P30; ≥ P30 & < P70; ≥ P70), computed against different factor models over months t − 72 to t − 13. We invest long (short) in the best (worst) performing small-cap and large-cap portfolios. The first column shows estimates from Table 7 in Ehsani & Linnainmaa (2022). t-values (in parentheses) are based on Newey- West standard errors with three lags. Bold numbers indicate statistical significance at the 5% level or higher. F. Graef et al. Finance Research Letters 76 (2025) 106963 5 Table 4 Strategy performance in modest-beta and extreme-beta subsamples. “Modest-beta” sample “Extreme-beta” sample Short-term total returns (rt− 1) Medium-term total returns ( rt− 12,t− 2 ) Short-term total returns (rt− 1) Medium-term total returns ( rt− 12,t− 2 ) Q5-Q1 r ¡0.821 0.544 ¡0.360 0.585 (− 5.45) (2.83) (− 2.27) (2.61) αFF5F ¡0.637 0.642 − 0.181 0.682 (− 3.95) (2.90) (− 1.00) (2.45) This table shows the performance of the (value-weighted) individual-stock momentum and short-term reversal strategies analyzed in Table 1, implemented within two different subsamples. The “extreme- beta” subsample is defined as all stock-months, for which at least one of the Fama and French (2015) model factor loadings ranks above the 80th or below the 20th percentile. The “modest-beta” subsample consists of all remaining observations with non-missing factor loadings. The sample period is July 1966 to December 2019. t-values (in parentheses) are based on Newey-West standard errors with three lags. Bold numbers indicate statistical significance at the 5 % level or higher. F. G raef et al. Finance Research Letters 76 (2025) 106963 6 (1993, 2015) three- and five-factor models in Tables 2 and 3, this is not what we find. To address concerns that the differences between our results and those of EL are due to differences in methodology or sample construction, we replicate their results from Table 7. To this end, we sort stocks into six portfolios by size (< median; ≥ median) and past idiosyncratic returns (< P30; ≥ P30 & < P70; ≥ P70), computed against three different factor models over months t − 73 to t − 13. We then invest long (short) in the small-cap and large-cap portfolios with the best (worst) performance. Comparing the first two columns in Panel C of Table 3, we find that our estimates for “UMD-style” idiosyncratic momentum are very similar to those of EL. However, the third column shows that “UMD-style” strategies based on systematic returns, which are not tested in EL, fail to outperform, which is at odds with the transmission mechanism propagated by EL and suggests that factor momentum may not be the root cause of stock momentum. Fig. 2. Factor loadings of momentum in modest-beta and extreme-beta subsamples. This figure shows Fama and French (2015) factor loadings of (value-weighted) momentum strategies, implemented within the “modest-beta” and “extreme-beta” subsamples (see Section 5 and Table 4). We compute the value-weighted average loadings of composite stocks against each factor for the high and low momentum quintiles (βH and βL). Net loadings then correspond to βH− L = βH − βL. The sample period is July 1966 to December 2019. F. Graef et al. Finance Research Letters 76 (2025) 106963 7 4.4. Dispersion in betas and momentum performance In this section, we examine the relationship between momentum profits and the cross-sectional dispersion in betas. The trans mission mechanism proposed by EL implies lower (higher) momentum profits when stocks in the investment universe are charac terized by similar (widely varying) factor exposures. To empirically test this hypothesis, we perform a monthly ranking of stocks by their loadings against each of the five Fama and French (2015) factors (i.e., each stock receives five different ranks). We then construct an “extreme-beta” subsample by selecting all stock-months, which rank above the 80th or below the 20th percentiles in terms of their exposures against at least one factor. All remaining observations with non-missing factor loadings are included in the “modest-beta” subsample. We then re-estimate the value-weighted momentum and short-term reversal strategies analyzed in Table 1 within these two subsamples. Results in Table 4 show that momentum returns are only marginally higher in the “extreme-beta” subsample (0.58 %), compared to the “modest-beta” subsample (0.54 %). The same holds true for the risk-adjusted returns. In contrast, the performance of the short-term reversal strategies differs substantially across the two subsamples. In other words, we find that the cross-sectional variation in betas does not significantly affect the profitability of stock momentum, even though Fig. 2 illustrates that the momentum strategy implemented in the “extreme-beta” subsample displays a much larger variation in its net factor loadings. 5. Conclusion EL find that the profitability of stock momentum strategies stems from factor momentum. They explain their finding by aid of a transmission mechanism from factor momentum to stock momentum. In this paper, we empirically test EL’s transmission mechanism and present results at odds with it. First, we show that a momentum strategy based on firm-specific (i.e., idiosyncratic) returns yields a statistically significant outperformance, while a strategy based on expected (i.e., systematic) returns does not. This outcome is the opposite of what we would expect, if factor momentum drives stock momentum as in EL’s transmission mechanism. Second, we find that the cross-sectional dispersion in factor loadings, which should be positively correlated with momentum returns, leaves stock momentum performance largely unaffected. While we do not dispute EL’s findings that point to factor momentum being related to stock momentum, our results call into question whether EL’s propagated transmission mechanism is responsible for the existence of stock momentum. Disclosure statement The authors confirm that they have received no financial support related to this article, and that they hold no position in any relevant organization with a potential interest in this article. CRediT authorship contribution statement Frank Graef: Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualiza tion. Daniel Hoechle: Writing – original draft, Validation, Supervision, Methodology, Investigation, Formal analysis, Conceptuali zation. Markus Schmid: Writing – original draft, Visualization, Validation, Supervision, Investigation, Conceptualization. Data availability Data will be made available on request. References Arnott, R., Clements, M., Kalesnik, V., Linnainmaa, J., 2023. Factor momentum. Rev. Financ. Stud. 36 (8), 3034–3070. Beaver, W., McNichols, M., Price, R., 2007. Delisting returns and their effect on accounting-based market anomalies. J. Account. Econ. 43 (2–3), 341–368. 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Finance Research Letters 76 (2025) 106963 9 http://refhub.elsevier.com/S1544-6123(25)00227-2/sbref0021 http://refhub.elsevier.com/S1544-6123(25)00227-2/sbref0021 http://refhub.elsevier.com/S1544-6123(25)00227-2/sbref0022 http://refhub.elsevier.com/S1544-6123(25)00227-2/sbref0023 http://refhub.elsevier.com/S1544-6123(25)00227-2/sbref0024 Firm-specific versus systematic momentum 1 Introduction 2 The transmission from factor momentum and idiosyncratic momentum to stock momentum 3 Data and factor construction 4 Empirical results 4.1 Return-based strategies using individual stocks and factor portfolios 4.2 Portfolio sorts by systematic and idiosyncratic returns 4.3 Robustness 4.4 Dispersion in betas and momentum performance 5 Conclusion Disclosure statement CRediT authorship contribution statement Data availability References