The DOI matches its Crossref record.
Comparing Cost-Mitigation Techniques
Open accessAbstract
This article compares the efficacy of three common transaction-cost-mitigation techniques: limiting a strategy to cheap-to-trade securities, rebalancing a strategy less frequently, and “banding,” which imposes a higher hurdle for actively trading into a position than for maintaining an established position. All three strategies significantly reduce transaction costs, but the techniques that reduce turnover have a less negative impact on strategy gross performance than limiting trade to low-cost securities has. Banding is more effective than simply reducing rebalancing frequencies, because banding yields similar trading-cost reductions while maintaining a better exposure to the underlying signal used to select stocks.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 17
- 10.1111/j.1540-6261.2006.00836.x · DOI
- 10.1086/295472 · DOI
- 10.1111/j.1540-6261.2008.01416.x · DOI
- 10.1287/moor.15.4.676 · DOI
- 10.3905/jpm.2003.319894 · DOI
- 10.1111/j.1540-6261.2008.01371.x · DOI · Paper page
- 10.1093/rfs/hhv043 · DOI
- Foster, George, Chris Olsen, and Terry Shevlin. 1984. “Earnings Releases, Anomalies, and the Behavior of Security Returns.” Accounting Review, vol. 59, no. 4: 574–603. · DOI
- 10.1016/j.jfineco.2013.10.005 · DOI
- 10.1111/j.1540-6261.2009.01469.x · DOI
- 10.3905/jpm.2017.44.1.015 · DOI
- 10.1111/j.1540-6261.1993.tb04702.x · DOI · Paper page
- 10.1016/S0304-405X(03)00206-X · DOI · Paper page
- 10.1093/rfs/hhv063 · DOI · Paper page
- 10.3905/jii.2017.8.3.039 · DOI
- 10.1111/j.1540-6261.1984.tb03897.x · DOI
- 10.3905/jot.2006.628191 · DOI
Cited by
Sorted by citations
Betting against betting against beta
Novy-Marx, R., & Velikov, M. (2018) · Journal of Financial Economics · doi:10.2139/ssrn.3300965 · Cited by 74
Self-citationZeroing In on the Expected Returns of Anomalies
Chen, A. Y., & Velikov, M. (2022) · Journal of Financial and Quantitative Analysis · doi:10.1017/s0022109022000874 · Cited by 120
Self-citationAnomalies in the China A-share market
Jansen, M., Swinkels, L., & Zhou, W.-L. (2021) · Social Science Research Network · doi:10.2139/ssrn.3810114 · Cited by 57
When Equity Factors Drop Their Shorts
Blitz, D., Baltussen, G., & van Vliet, P. (2020) · Financial analysts journal · doi:10.2139/ssrn.3493305 · Cited by 28
“Furthermore, Novy-Marx and Velikov (2019) showed that costs for trading anomalies in small-cap stocks are approximately double the costs for trading anomalies in large-cap stocks.”
Brown, S. J. (2020) · Financial Analysts Journal · doi:10.1080/0015198x.2020.1734375 · Cited by 54
“Many academics who study investment strategies neglect consideration of costs, although this disregard is changing (Novy-Marx and Velikov 2019).”
BackgroundThe Impact of Proportional Transaction Costs on Systematically Generated Portfolios
Ruf, J., & Xie, K. (2019) · SIAM Journal on Financial Mathematics · doi:10.1137/19m1282313 · Cited by 28
“An 37 empirical analysis of the effects of transaction costs is provided in [25], [1], [21], and [20].”
Relative Valuation with Machine Learning
Geertsema, P., & Lu, H. (2020) · Social Science Research Network · doi:10.2139/ssrn.3740270 · Cited by 23
Beyond Fama-French Factors: Alpha from Short-Term Signals
Blitz, D., Hanauer, M., Honarvar, I., Huisman, R., & van Vliet, P. (2023) · Social Science Research Network · doi:10.1080/0015198x.2023.2173492 · Cited by 16
“Novy-Marx and Velikov (2016, 2019) show that more advanced buy and sell rules that only replace stocks if their attract-iveness drops below a certain threshold give savings in trading costs that readily outweigh the loss in gross returns.”
BackgroundWhat drives stock returns across countries? Insights from machine learning models
Cakici, N., & Zaremba, A. (2024) · International Review of Financial Analysis · doi:10.1016/j.irfa.2024.103569 · Cited by 18
Reversals and the Returns to Liquidity Provision
Dai, W., Medhat, M., Novy-Marx, R., & Rizova, S. (2023) · Social Science Research Network · doi:10.1080/0015198x.2023.2292534 · Cited by 10
“Conceptually, it does so by delaying trades that demand high-cost liquidity until the liquidity is cheaper, but delaying these trades no longer than necessary (Novy-Marx and Velikov, 2016, 2019).”
BackgroundMethodInfluentialSelf-citationThe Term Structure of Machine Learning AlphaPreprint
Blitz, D., Hanauer, M., Hoogteijling, T., & Howard, C. (2023) · Social Science Research Network · doi:10.2139/ssrn.4474637 · Cited by 7
Autoencoder asset pricing models and economic restrictions — international evidence
Nechvátalová, L. (2025) · International Review of Financial Analysis · doi:10.1016/j.irfa.2025.104642
Volatility Targeting: The Bridge Between Options-Based and Traditional Defensive Strategies
Poirier, R. (2021) · Journal of Portfolio Management · doi:10.3905/jpm.2021.1.238 · Cited by 3
Macroeconomics matter: Leading economic indicators and the cross-section of global stock returns
Long, H., Zaremba, A., Zhou, W.-Y., & Bouri, E. (2022) · Journal of financial markets · doi:10.1016/j.finmar.2022.100736 · Cited by 2
Factor models for Chinese A-shares
Hanauer, M., Jansen, M., Swinkels, L., & Zhou, W.-L. (2023) · International Review of Financial Analysis · doi:10.1016/j.irfa.2023.102975 · Cited by 1
Pätäri, E. J., Ahmed, S., Luukka, P., & Yeomans, J. S. (2023) · The North American journal of economics and finance · doi:10.1016/j.najef.2023.101884 · Cited by 1
China A-Shares: Strategic Allocation to Market and Factor Premiums
de Groot, W. D., Swinkels, L., & Zhou, W.-L. (2020) · Journal of Portfolio Management · doi:10.2139/ssrn.3702965 · Cited by 1
Factor return forecasting using cashflow spreads
Dai, Y.-Q., Haque, T. H., & Zurbruegg, R. (2020) · doi:10.1016/j.iref.2020.06.018 · Cited by 1
Empirical Asset Pricing via Machine Learning: The Role of Research Design Choices
Lalwani, V., Meshram, V., & Jindal, V. (2026) · European Financial Management · doi:10.1111/eufm.70033
Hedging with daily beta loses momentum
Annaert, J., de Ceuster, M. D., & Doninck, F. V. (2026) · International Review of Financial Analysis · doi:10.1016/j.irfa.2026.105413
Profitability Meets Investment: The Wealth Creation Effect in Stock Returns
Franzoni, F., Obrycki, D., & Resendes, R. (2026) · Financial analysts journal · doi:10.1080/0015198x.2026.2683330
“…the WC factor ’ s alphas remain economically meaningful after accounting for transaction costs estimated using the effective-spread methodology of Novy-Marx and Velikov (2016, 2019), and the strategy ’ s break-even trading cost is well above the effective spreads of the stocks in the…”
Jo, Y., & Kim, Y. H. (2026) · Financial Analysts Journal · doi:10.1080/0015198x.2026.2621646
“We implement the banding strategy using a 10%/20% buy/ hold spread, modifying the 30% hold threshold employed by Novy-Marx and Velikov (2019).”
The Many Facets of Stock Momentum: Distinguishing Factor and Stock Components
Gérard, X., & Jehl, L. (2025) · Financial analysts journal · doi:10.1080/0015198x.2025.2562790
“Such a strategy would bene fi t from techniques that aim at reducing “ unnecessary ” turnover (see, e.g., Novy-Marx and Velikov 2016, 2019), and it would make sense to consider including it in a multifactor model where diversi fi cation opportunities across factors would help bring portfolio turnover…”
True Value Investing in the Corporate Bond Market
Hoen, C. R.-J. '., Houweling, P., & Messow, P. (2025) · Financial analysts journal · doi:10.1080/0015198x.2025.2483183
“Novy-Marx and Velikov (2019) conducted a comparative analysis of various cost-mitigation techniques and concluded that the so-called “ banding ” method, which “ imposes a higher hurdle for actively trading into a position than for maintaining an established position, ” is the most effective…”
InfluentialHerculano, M. C. (2024) · Quantitative finance (Print) · doi:10.1080/14697688.2025.2517270
Cost mitigation of factor investing in emerging equity markets
Stankov, K., Schiereck, D., & Flögel, V. (2024) · Journal of Asset Management · doi:10.1057/s41260-024-00353-4
“Based on these findings, Donohue and Yip (2003), Garleanu and Pedersen (2013), Frazzini et al. (2018) and Novy-Marx and Velikov (2018) find optimal portfolio decisions in developed markets concerning transaction costs.”
InfluentialCombining low-volatility and mean-reversion anomalies: Better together?
Pätäri, E. J., Ahmed, S., Lankinen, T., & Yeomans, J. (2023) · Algorithmic Finance · doi:10.3233/af-220441
The Low-Risk Anomaly: How Much Is a Good Risk Estimate Worth?
Barchetto, T., Pascalau, R., & Poirier, R. (2020) · Journal of Portfolio Management · doi:10.3905/jpm.2020.1.183
Anomaly Premiums with Dynamic ExitsPreprint
Kumar, N., Prabhala, N. R., & Ranjan, R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6076808
Metadata
- Authors
- Novy-Marx, R., & Velikov, M.
- Affiliations
- Robert Novy-Marx is the Lori and Alan S. Zekelman Professor of Business Administration at the Simon Business School, University of Rochester, New York.
- Federal Reserve Bank of Richmond (US)
- ORCID
- Velikov, Mihail
- Venue
- Financial Analysts Journal
- Publisher
- Informa UK Limited
- Published
- 2019-01-02
- Volume
- 75
- Issue
- 1
- Pages
- 85-102
- ISSN
- 0015-198X (ISSN-L)
- 1938-3312
- OpenAlex
- W2914942385
- Cited by
- 52
- References
- 0
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 6 · 2023: 11 · 2022: 4 · 2021: 3 · 2020: 9 · 2019: 1
- FWCI
- 5.14
- Type
- journal article
- Language
- en
- Open access
- green
- papers.ssrn.com
- Locations
- Financial Analysts Journal (publishedVersion)
- SSRN Electronic Journal (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Business, Management and Accounting › Accounting › Financial Analysis and Corporate Governance
- Social Sciences › Economics, Econometrics and Finance › Finance › Corporate Finance and Governance
- Keywords
- transaction costs, cost mitigation, trading frequency, rebalancing, banding, low-cost security, portfolio turnover, active trading
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
