The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Non-standard errors in the cryptocurrency world
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
… and the Cross-Section of Expected Returns
Harvey, C. R., Liu, Y., & Zhu, H. (2016) · Review of Financial Studies · doi:10.1093/rfs/hhv059 · Cited by 2,119
Does Academic Research Destroy Stock Return Predictability?
MCLEAN, R. D., & PONTIFF, J. (2016) · The Journal of Finance · doi:10.1111/jofi.12365 · Cited by 1,566
Significance testing in empirical finance: a critical review and assessment
Kim, J. H., & Ji, P. I. (2015) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2015.08.006 · Cited by 86
Research works printed but not linked: 76
- Alexander, 2020, A critical investigation of cryptocurrency data and analysis, Quantitative Finance, 20, 173, 10.1080/14697688.2019.1641347 · DOI
- Amihud, 2002, Illiquidity and stock returns: cross-section and time-series effects, Journal of Financial Markets, 5, 31, 10.1016/S1386-4181(01)00024-6 · DOI
- Ang, 2006, The cross-section of volatility and expected returns, The Journal of Finance, 61, 259, 10.1111/j.1540-6261.2006.00836.x · DOI
- Ante, 2023, The ChatGPT effect on AI-themed cryptocurrencies, SSRN Electronic Journal
- Babiak, 2022, Variations in trading activity, costly arbitrage, and cryptocurrency returns, SSRN Electronic Journal, 10.2139/ssrn.4291073 · DOI
- Bali, 2011, Maxing out: Stocks as lotteries and the cross-section of expected returns, Journal of Financial Economics, 99, 427, 10.1016/j.jfineco.2010.08.014 · DOI
- Banz, 1981, The relationship between return and market value of common stocks, Journal of Financial Economics, 9, 3, 10.1016/0304-405X(81)90018-0 · DOI
- Barry, 1984, Differential information and the small firm effect, Journal of Financial Economics, 13, 283, 10.1016/0304-405X(84)90026-6 · DOI
- Baur, 2021, The volatility of Bitcoin and its role as a medium of exchange and a store of value, Empirical Economics, 61, 2663, 10.1007/s00181-020-01990-5 · DOI
- Bianchi, 2021, A risk-based explanation of cryptocurrency returns, SSRN Electronic Journal
- Bianchi, 2022, Trading volume and liquidity provision in cryptocurrency markets, Journal of Banking & Finance, 142, 10.1016/j.jbankfin.2022.106547 · DOI
- Borri, 2019, Conditional tail-risk in cryptocurrency markets, Journal of Empirical Finance, 50, 1, 10.1016/j.jempfin.2018.11.002 · DOI
- Borri, 2022, Crypto risk premia, SSRN Electronic Journal
- Bouri, 2022, Microstructure noise and idiosyncratic volatility anomalies in cryptocurrencies, Annals of Operations Research, 1
- Brauneis, 2021, How to measure the liquidity of cryptocurrency markets?, Journal of Banking & Finance, 124, 10.1016/j.jbankfin.2020.106041 · DOI
- Burggraf, 2021, Cryptocurrencies and the low volatility anomaly, Finance Research Letters, 40, 10.1016/j.frl.2020.101683 · DOI
- Cai, 2021, Salience theory and cryptocurrency returns, SSRN Electronic Journal
- Cakici, 2022, Machine learning and the cross-section of cryptocurrency returns, SSRN Electronic Journal
- Chen, 2022, Can salience theory explain investor behaviour? Real-world evidence from the cryptocurrency market, International Review of Financial Analysis, 84, 10.1016/j.irfa.2022.102419 · DOI
- Chen, 2022, Explaining cryptocurrency returns: A prospect theory perspective, Journal of International Financial Markets, Institutions and Money, 79, 10.1016/j.intfin.2022.101599 · DOI
- Chordia, 2001, Trading activity and expected stock returns, Journal of Financial Economics, 59, 3, 10.1016/S0304-405X(00)00080-5 · DOI
- Cong, 2022, Value premium, network adoption, and factor pricing of crypto assets, SSRN Electronic Journal
- Coqueret, 2023, Forking paths in empirical studies, SSRN Electronic Journal
- Corwin, 2012, A simple way to estimate bid-ask spreads from daily high and low prices, The Journal of Finance, 67, 719, 10.1111/j.1540-6261.2012.01729.x · DOI
- Cosemans, 2021, Salience theory and stock prices: Empirical evidence, Journal of Financial Economics, 140, 460, 10.1016/j.jfineco.2020.12.012 · DOI
- Dobrynskaya, 2021, Cryptocurrency momentum and reversal, SSRN Electronic Journal
- Dong, 2022, Liquidity in the cryptocurrency market and commonalities across anomalies, International Review of Financial Analysis, 81, 10.1016/j.irfa.2022.102097 · DOI
- Enoksen, 2020, Understanding risk of bubbles in cryptocurrencies, Journal of Economic Behaviour and Organization, 176, 129, 10.1016/j.jebo.2020.05.005 · DOI
- Fama, 1993, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics, 33, 3, 10.1016/0304-405X(93)90023-5 · DOI · Paper page
- Fama, 1973, Risk, return, and equilibrium: Empirical tests, Journal of Political Economy, 81, 607, 10.1086/260061 · DOI · Paper page
- Farag, 2022, Returns from liquidity provision in cryptocurrency markets, SSRN Electronic Journal, 10.2139/ssrn.4057510 · DOI
- Feng, 2020, Taming the factor zoo: A test of new factors, The Journal of Finance, 75, 1327, 10.1111/jofi.12883 · DOI
- Garfinkel, 2023, Disagreement and the cross section of cryptocurrency returns, SSRN Electronic Journal
- Garratt, 2023, Why fixed costs matter for proof-of-work based cryptocurrencies, Management Science, 69, 6417, 10.1287/mnsc.2023.4901 · DOI
- George, 2004, The 52-week high and momentum investing, The Journal of Finance, 59, 2145, 10.1111/j.1540-6261.2004.00695.x · DOI
- Grobys, 2021, Speculation and lottery-like demand in cryptocurrency markets, Journal of International Financial Markets, Institutions and Money, 71, 10.1016/j.intfin.2021.101289 · DOI
- Grobys, 2019, Cryptocurrencies and momentum, Economics Letters, 180, 6, 10.1016/j.econlet.2019.03.028 · DOI
- Gunay, 2019, Seeking a chaotic order in the cryptocurrency market, Mathematical and Computational Applications, 24, 36, 10.3390/mca24020036 · DOI
- Hanauer, 2023, Enhanced momentum strategies, Journal of Banking & Finance, 148, 10.1016/j.jbankfin.2022.106712 · DOI
- Harvey, 2017, Presidential address: The scientific outlook in financial economics, The Journal of Finance, 72, 1399, 10.1111/jofi.12530 · DOI
- Harvey, 2019, Replication in financial economics, SSRN Electronic Journal, 10.2139/ssrn.3409466 · DOI
- Hollstein, 2022, The world of anomalies: Smaller than we think?, Journal of International Money and Finance, 129, 10.1016/j.jimonfin.2022.102741 · DOI
- Hou, 2005, Market frictions, price delay, and the cross-section of expected returns, The Review of Financial Studies, 18, 981, 10.1093/rfs/hhi023 · DOI
- Hou, 2020, Replicating anomalies, The Review of Financial Studies, 33, 2019, 10.1093/rfs/hhy131 · DOI
- Ince, 2006, Individual equity return data from Thomson datastream: Handle with care!, Journal of Financial Research, 29, 463, 10.1111/j.1475-6803.2006.00189.x · DOI
- Jegadeesh, 1993, Returns to buying winners and selling losers: Implications for stock market efficiency, The Journal of Finance, 48, 65, 10.1111/j.1540-6261.1993.tb04702.x · DOI · Paper page
- Jensen, 1967, The performance of mutual funds in the period 1945–1964 (May 1, 1967), The Journal of Finance, 23, 389
- Jensen, 2023, Is there a replication crisis in finance?, The Journal of Finance, 78, 2465, 10.1111/jofi.13249 · DOI
- Kamiński, 2018, A framework for sensitivity analysis of decision trees, Central European Journal of Operations Research, 26, 135, 10.1007/s10100-017-0479-6 · DOI
- Kyriazis, 2020, A systematic review of the bubble dynamics of cryptocurrency prices, Research in International Business and Finance, 54, 10.1016/j.ribaf.2020.101254 · DOI
- Li, 2021, MAX momentum in cryptocurrency markets, International Review of Financial Analysis, 77, 10.1016/j.irfa.2021.101829 · DOI
- Liebi, 2022, Is there a value premium in cryptoasset markets?, Economic Modelling, 109, 10.1016/j.econmod.2022.105777 · DOI
- Linnainmaa, 2018, The history of the cross-section of stock returns, The Review of Financial Studies, 31, 2606, 10.1093/rfs/hhy030 · DOI
- Liu, 2021, Risks and returns of cryptocurrency, The Review of Financial Studies, 34, 2689, 10.1093/rfs/hhaa113 · DOI
- Liu, 2021, Accounting for cryptocurrency value, SSRN Electronic Journal
- Liu, 2022, Common risk factors in cryptocurrency, The Journal of Finance, 77, 1133, 10.1111/jofi.13119 · DOI
- Long, 2022, Is geopolitical risk priced in the cross-section of cryptocurrency returns?, Finance Research Letters, 49, 10.1016/j.frl.2022.103131 · DOI
- Long, 2020, Seasonality in the cross-section of cryptocurrency returns, Finance Research Letters, 35, 10.1016/j.frl.2020.101566 · DOI
- Mandelbrot, 2001, Scaling in financial prices: IV. Multifractal concentration, Quantitative Finance, 1, 641, 10.1088/1469-7688/1/6/306 · DOI
- Miller, 1982, Dividends and taxes: Some empirical evidence, Journal of Political Economy, 90, 1118, 10.1086/261114 · DOI
- Mitton, 2022, Methodological variation in empirical corporate finance, The Review of Financial Studies, 35, 527, 10.1093/rfs/hhab030 · DOI
- Morey, 2018, Documentation of the file drawer problem in academic finance journals, The Journal of Investing, 27, 143, 10.3905/joi.2018.27.1.143 · DOI
- Ozdamar, 2021, Lottery-like preferences and the MAX effect in the cryptocurrency market, Financial Innovation, 7, 1, 10.1186/s40854-021-00291-9 · DOI
- Shen, 2020, A three-factor pricing model for cryptocurrencies, Finance Research Letters, 34, 10.1016/j.frl.2019.07.021 · DOI
- Simonsohn, 2020, Specification curve analysis, Nature Human Behaviour, 4, 1208, 10.1038/s41562-020-0912-z · DOI · Paper page
- Soebhag, 2022, Mind your sorts, SSRN Electronic Journal, 10.2139/ssrn.4136672 · DOI
- Steegen, 2016, Increasing transparency through a multiverse analysis, Perspectives on Psychological Science, 11, 702, 10.1177/1745691616658637 · DOI
- Thoma, 2020, A prospect theory model for predicting cryptocurrency returns, SSRN Electronic Journal
- Tzouvanas, 2020, Momentum trading in cryptocurrencies: Short-term returns and diversification benefits, Economics Letters, 191, 10.1016/j.econlet.2019.108728 · DOI
- Vidal-Tomás, 2022, Which cryptocurrency data sources should scholars use?, International Review of Financial Analysis, 81, 10.1016/j.irfa.2022.102061 · DOI
- Walter, 2022, Non-standard errors in portfolio sorts, SSRN Electronic Journal, 10.2139/ssrn.4164117 · DOI · Paper page
- Yang, 2000, Drift independent volatility estimation based on high, low, open, and close prices, Journal of Business, 73, 477, 10.1086/209650 · DOI
- Yarovaya, 2022, Intraday volume-return nexus in cryptocurrency markets: Novel evidence from cryptocurrency classification, Research in International Business and Finance, 60, 10.1016/j.ribaf.2021.101592 · DOI
- Zhang, 2020, Is idiosyncratic volatility priced in cryptocurrency markets?, Research in International Business and Finance, 54, 10.1016/j.ribaf.2020.101252 · DOI
- Zhang, 2021, Liquidity risk and expected cryptocurrency returns, International Journal of Finance & Economics
- Zhang, 2021, Downside risk and the cross-section of cryptocurrency returns, Journal of Banking & Finance, 133, 10.1016/j.jbankfin.2021.106246 · DOI
Cited by
Sorted by citations
Machine Learning and the Cross-Section of Cryptocurrency Returns
Cakici, N., Shahzad, S. J. H., Będowska-Sójka, B., & Zaremba, A. (2022) · International Review of Financial Analysis · doi:10.1016/j.irfa.2024.103244 · Cited by 34
Self-citationA Trend Factor for the Cross-Section of Cryptocurrency ReturnsPreprint
Fieberg, C., Liedtke, G., Poddig, T., Walker, T., & Zaremba, A. (2025) · Social Science Research Network · doi:10.2139/ssrn.4601972 · Cited by 17
“Even seemingly irrelevant methodological choices may lead to vastly differing conclusions — in the stock (Menkveld et al. (2024), Walter, Weber, and Weiss (2023), and Soebhag, van Vliet, and Verwijmeren (2024)) and cryptocurrency (Fieberg, Günther, Poddig,and Zaremba (2023)) markets alike.”
Self-citationCryptocurrency anomalies and economic constraints
Fieberg, C., Liedtke, G., & Zaremba, A. (2024) · International Review of Financial Analysis · doi:10.1016/j.irfa.2024.103218 · Cited by 11
Self-citationAli, F., Du, A., & Majeed, M. A. (2025) · Finance Research Letters · doi:10.1016/j.frl.2025.107016 · Cited by 10
Cryptocurrency as an Investable Asset Class: Coming of Age
Borri, N., Liu, Y.-K., Tsyvinski, A., & Wu, X. (2025) · Social Science Research Network · doi:10.2139/ssrn.5612870 · Cited by 7
Metadata
- Authors
- Fieberg, C., Günther, S., Poddig, T., & Zaremba, A.
- Affiliations
- Hochschule Bremen (DE)
- University of Bremen (DE)
- Poznań University of Economics and Business (PL)
- Corresponding author
- Fieberg, Christian
- Venue
- International Review of Financial Analysis
- Publisher
- Elsevier BV
- Published
- 2024-03
- Volume
- 92
- Pages
- 103106
- ISSN
- 1057-5219 (ISSN-L)
- OpenAlex
- W4391544797
- Cited by
- 21
- References
- 0
- Citations by year
- 2026: 9 · 2025: 8 · 2024: 3
- FWCI
- 7.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Review of Financial Analysis (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Narodowe Centrum Nauki (2021/41/B/HS4/02443)
- Narodowym Centrum Nauki
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Business, Management and Accounting › Management Information Systems › FinTech, Crowdfunding, Digital Finance
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Keywords
- cryptocurrency, portfolio construction, data sources, sample preparation, size effect, momentum effect, portfolio performance
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Non-standard errors in the cryptocurrency world. https://app.cochat.ai/papers/10.1016/j.irfa.2024.103106
- The paper
- Fieberg, C., Günther, S., Poddig, T., & Zaremba, A. (2024). Non-standard errors in the cryptocurrency world. International Review of Financial Analysis. https://doi.org/10.1016/j.irfa.2024.103106