Pith. sign in

REVIEW 4 major objections 4 minor 1 cited by

Do Cryptocurrency Markets Differentiate Infrastructure from Regulatory Shocks? A Multi-Moment Event Study with Dependence-Robust Inference

T0 review · 4 major / 4 minor · reviewed 2026-08-03 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Infrastructure failures and regulatory enforcement produce statistically indistinguishable cryptocurrency market returns, according to an event study using block bootstrap inference.

desk verdict The body is an honest, low-powered null result with solid robustness; the arXiv abstract describes an entirely different GARCH-copula analysis, so the paper needs reconciliation before it can be trusted. read the letter →

arxiv 2602.07046 v4 pith:6HBCXFWV submitted 2026-02-04 q-fin.ST q-fin.CPstat.AP

Murad Farzulla This is my paper · ORCID
classification q-fin.STq-fin.CPstat.AP
keywords cryptocurrencyeventstudyblockbootstrapcumulativeabnormalreturnsinfrastructureriskregulatoryenforcementvolatilityasymmetrycross-sectionaldependence
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper asks whether cryptocurrency markets price infrastructure failures (exchange collapses, hacks, protocol depegs) differently from regulatory enforcement (lawsuits, bans). Using 31 events across four large assets from 2019–2025, it finds mean cumulative abnormal returns of –7.6% for infrastructure failures and –11.1% for regulatory enforcement; the 3.6-point difference has a block-bootstrap confidence interval of [–25.3%, +30.9%] and p=0.81. The paper argues this null is methodologically meaningful: naive i.i.d. tests that pool asset-event observations would overstate precision. Read together with a companion conditional-variance analysis reporting 5.7 times larger volatility responses to infrastructure events, the result suggests markets differentiate shock types through risk, not expected returns.

What carries the argument

The load-bearing tool is the event-level block bootstrap: resample whole events (keeping the four assets' CARs together) to build the null distribution, rather than pooling asset-event observations as independent. This corrects for the cross-sectional correlation that inflates degrees of freedom in standard event studies. The paper also introduces a 4-category classification (infrastructure/regulatory × positive/negative) so that like-valence shocks are compared, and it triangulates with the Ibragimov–Müller few-cluster t-test on event-level means.

What would settle it

A direct test would re-run the comparison on a pre-registered, news-timestamped event set of at least ~60 negative events per category (the paper's own power math says ~930 per group for 80% power at the observed effect), using intraday prices and named-token abnormal returns. If the CAR gap then cleared zero with a narrow confidence interval, the null would be overturned; if a companion intraday variance analysis found the 5.7× gap, the risk-channel interpretation would be further supported.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is a null result made informative by dependence-robust inference. When the unit of resampling is the event rather than the asset-day, the first-moment response to negative infrastructure shocks (mean CAR –7.6%) is not statistically distinguishable from the response to negative regulatory shocks (–11.1%), with a difference of +3.6 percentage points, 95% CI [–25.3%, +30.9%], p=0.81. The paper shows this null survives market-model adjustment, winsorization, permutation tests, leave-one-out analysis, and few-cluster tests. Because a companion study finds a highly significant 5.7× difference in conditional variance, the paper's conclusion is that the market

Load-bearing premise

The events are treated as dated and selected without conditioning on the return path being measured; in particular, gradual events are dated by 'first major price discontinuity' and some events enter via a 5% return screen, so the abnormal returns that feed the CARs may have influenced which events are in the sample and when.

Editorial extensions

If this is right

  • Prior parametric significance claims in crypto event studies may be artifacts of pseudoreplication across correlated assets.
  • The enforcement-capacity hypothesis (infrastructure hits returns harder) is neither confirmed nor rejected; the observed effect size is far below the minimum detectable effect of ~40 pp with 8 vs 7 events.
  • Portfolios holding crypto assets need roughly 4–5 times larger capital buffers for infrastructure-event risk than return-based models imply.
  • Event-type taxonomy should separate positive from negative valence within infrastructure and regulatory categories.
  • Future confirmatory work requires ~930 events per category to detect the observed effect at 80% power.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the return-level null is genuine, then market efficiency with respect to shock type holds for prices but not for volatility; that asymmetry could imply that option markets, not spot returns, are the place to look for differential pricing of regulatory vs infrastructure risk.
  • A testable extension: infrastructure CARs should mean-revert faster than regulatory CARs over 60–90 days as bounded uncertainty resolves; the current 35-day window cannot distinguish this.
  • The null may partly be a design artifact of spillover measurement: using only BTC/ETH/SOL/ADA captures market-wide response but omits directly named tokens (e.g., XRP in SEC v. Ripple), which could show stronger regulatory effects.
  • The paper's block-bootstrap recipe transfers to any cross-asset event study in heavy-tailed markets; applying it to other shock taxonomies (e.g., monetary vs fiscal news) would test whether the null generalizes.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This paper tests whether negative infrastructure and regulatory shocks produce different cumulative abnormal returns (CARs) in cryptocurrency markets. Using a four-category event classification and an event-level block bootstrap on 31 events (8 versus 7 analyzed negative events) across BTC, ETH, SOL, and ADA, the body reports mean CARs of -7.6% versus -11.1%, a difference of +3.6 pp with 95% CI [-25.3%, +30.9%] and p=0.81. The paper interprets this as an exploratory null: returns do not appear to distinguish the two shock classes. Robustness checks include permutation tests, market model adjustments, winsorization, leave-one-out, non-overlapping events, and Ibragimov-Müller few-cluster inference. The arXiv abstract, however, describes a different analysis: 50 events, six assets, GJR-GARCH-X/copula bootstrap inference, a +7.19 pp difference with p=0.283, and design-effect p-values of 0.07-0.15. That analysis is absent from the body. The body also relies on a companion volatility study for the claim that markets differentiate shock types through the risk channel.

Significance. Both versions of the paper make a useful methodological point: event-level/block-bootstrap inference can overturn naive i.i.d. significance in few-event, cross-sectionally correlated crypto event studies. The body is unusually honest about low power (MDE approximately 40 pp) and selection/dating concerns, and the reproducible code and data statement is a strength. If the block-bootstrap null is the record, the paper is a cautionary empirical contribution. However, the abstract and body report different primary analyses, so the central null's evidential basis is ambiguous. The interpretive claim that markets differentiate through the risk channel depends on an external paper not included here. The event-selection and event-dating procedures condition partly on the outcome being measured, which undermines causal readings unless addressed. These issues are fixable within the manuscript's scope, but they are load-bearing for the paper's central claims.

major comments (4)
  1. [Abstract vs. §5.1/Table 2; §4.3] The submitted abstract and the body report different primary analyses. The abstract claims a GJR-GARCH-X/copula bootstrap with 50 events, six assets, a +7.19 pp CAR difference (p=0.283), and design-effect p-values of 0.07-0.15. The body's Table 2 (Section 5.1) reports Δ=+3.6 pp, p=0.81, CI [-25.3%, +30.9%], from an event-level block bootstrap on 31 events/four assets; Section 4.3 describes only that bootstrap. The GARCH-X/copula model, the inference ladder, and the Monte-Carlo size study are never defined in the body. Because the central null's evidential basis changes between the two versions, the authors must reconcile them: either add the missing analysis and explain the discrepancy, or make the abstract match the body.
  2. [§3.1, §5.12, §6.4.5] Event inclusion and dating condition on the outcome that defines the CAR. Criterion 1 in §3.1 admits events with same-day or three-day |BTC| return >5%; §6.4.5 admits that gradual events are dated by 'first major price discontinuity.' The former means the sample is selected partly on the abnormal returns being measured; the latter means the event day t=0 is chosen from the return path. The 'exogenous-only' analysis in §5.12 addresses inclusion for events labeled 'Exogenous' or 'Both,' but Table 13 shows most negative events are 'Both,' and no alternative dating is implemented. Because the point estimate and the bootstrap distribution both depend on t=0, this is a load-bearing identification concern. Please re-date using news timestamps and report sensitivity, or explicitly bound the resulting bias.
  3. [§1 vs. §4.4] Section 1 states that the enforcement-capacity hypothesis was 'specified ex ante,' but Section 4.4 discloses that event selection criteria 'evolved iteratively' and that the four-category classification 'was developed after initial data exploration.' These statements are in direct tension. If the classification and selection rules were not fixed before examining the CARs, the exploratory nature of the null should be stated in the abstract and conclusion, not only in Section 6.4. Please either provide a dated pre-analysis plan or transparently describe which rules were fixed before the data were examined.
  4. [§6.1, §6.3] The interpretive conclusion that 'markets differentiate shock types through the risk channel' rests entirely on the companion paper (Farzulla, 2025a), which is not included in this manuscript. The body reports no second-moment estimation. The 5.7x variance ratio, p=0.0008, the regime F=45.23, and the flat regulatory coefficient are external claims. Moreover, the methodological note in §6.3 says the companion uses a different data source (CoinGecko, six assets) and return definition (log returns), so the direct comparability is not established. Either include a self-contained version of that analysis or explicitly demote this conclusion to an external implication. Without this, the title/abstract promise of a 'multi-moment' study is unsupported.
minor comments (4)
  1. [Title and Abstract] The arXiv title, 'Do Cryptocurrency Markets Differentiate Infrastructure from Regulatory Shocks? A Multi-Moment Event Study with Dependence-Robust Inference,' does not match the body's title, 'Same Returns, Different Risks.' The body abstract and the arXiv-side abstract also differ in sample size and method. Please harmonize titles and abstracts.
  2. [Tables 3, 14, and summary counts] Sample observation counts are inconsistent. Table 14 lists Infra_Pos N assets as 23 and Reg_Pos as 32, whereas Table 3 reports 21 and 31; the Table 14 summary says total event-asset observations = 118, but summing the listed assets gives 113. Please reconcile these counts.
  3. [§4.3 vs. Table 2 note] Section 4.3 step 2 instructs within-event averaging so each event receives equal weight, but Table 2's note says the primary results use the observation-weighted bootstrap. Since the two schemes give similar but not identical p-values (0.81 vs. 0.93), the paper should state clearly which is the primary estimator and why.
  4. [§5.9] The placebo test reports p=0.08. Describing the constant mean model as 'adequately controls' is too strong for a borderline result; the text already says 'borderline,' but the conclusion could be phrased as 'we cannot reject the null of no drift at the 5% level.'

Circularity Check

2 steps flagged · score 4.0 of 10

Central CAR null is self-contained; score driven by return-conditioned event dating and a load-bearing same-author companion citation for the 'risk channel' interpretation.

  1. self definitional [Section 3.1 (Event Sample, criterion 2) and Section 6.4, Limitation 5; CAR definition Eq. (4)]
    "Identifiable date: Event has precise announcement or occurrence date with ±1 day precision. For gradual events (e.g., Terra depeg), we use the date of first major price discontinuity. ... Event dating: For gradual events (e.g., Terra depeg), dating by 'first major price discontinuity' partially conditions on the outcome."

    The event date fixes t=0 in CAR_i(τ1,τ2)=Σ AR_it (Eq. 4). Choosing t=0 as the first major price discontinuity—and admitting events by a same-day |BTC|>5% return—makes the outcome (the CAR window) partly constitutive of the treatment date/inclusion criterion. This is an acknowledged selection-on-outcome channel rather than a fitted prediction; the exogenous-only subsample (Section 5.12) mitigates inclusion, but the dating channel is not removed. It biases category CARs and CIs, though it does not by itself force the null difference.

  2. self citation load bearing [Sections 6.1, 6.3, and 7 (companion evidence; also Introduction)]
    "Critically, this null finding acquires substantive meaning in light of the companion volatility study (Farzulla, 2025a), which finds a 5.7×larger conditional variance impact from infrastructure events (p=0.0008)."

    The headline conclusion—'markets differentiate shock types through the risk channel, not expected returns'—is not derived in this paper; it is imported entirely from Farzulla (2025a), a same-author Research Square preprint under review. The companion's variance result is not recomputed, externally verified, or shown to be independent of the same event-selection screen. The CAR null itself is computed in the body and does not reduce to the companion, so this is a load-bearing interpretive self-citation rather than a fully circular derivation.

full rationale

The paper's first-moment null is a genuine empirical computation: mean CARs are formed from constant-mean or market-model abnormal returns, and inference resamples whole events (8 vs 7), with permutation (p=0.93), Ibragimov-Muller (p=0.93), non-overlap, winsorization, and exogenous-only (p=0.61) checks all pointing the same way. No parameter is fitted to manufacture the null, so the central claim does not reduce to its inputs by construction. Two qualifications keep this from a clean 0-2. First, the sample screen and gradual-event dating condition partly on the return path being measured (same-day |BTC|>5%; 'first major price discontinuity'), which the authors admit in Limitation 5; this is a self-definitional element of the CAR construction, though the exogenous-only subsample addresses inclusion. Second, the paper's central interpretation—same returns, different risks—rests entirely on a same-author unpublished companion (Farzulla 2025a) rather than on analysis in this paper; under the review rules that is a load-bearing self-citation for the interpretive claim, even though it is not the basis of the null. I also note, without counting it as circularity, that the separately supplied arXiv abstract describes a different analysis of record (50 events, six assets, copula-GARCH, p=0.283) that is absent from the body; that is a reporting/missing-support defect, not a derivation-equals-input issue. Overall score 4 reflects partial conditioning and a load-bearing self-citation while acknowledging the null itself is independently computed.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim is empirical and relies mostly on standard statistical assumptions; the main unexamined costs are event exogeneity and the companion variance result.

free parameters (3)
  • Event impact threshold criteria = same-day |BTC|>5% OR 3-day |BTC|>5% OR impact>$100M OR users>100k
    Hand-chosen screen in §3.1 determines the 31-event sample; includes an outcome-conditional rule and thus affects both CAR estimates and inference.
  • Event window [−5,+30] = -5 to +30 days
    Primary window chosen in §4.1; robustness windows differ, so results are window-dependent.
  • Winsorization cap (baseline ±50%) = ±50%
    Applied to returns in §4.2; robustness results reported for other caps, so the specific cap is a modeling choice.
assumptions (4)
  • domain assumption Events, once classified, are independent clusters and exchangeable under bootstrap resampling
    Block bootstrap in §4.3 resamples event IDs; with 8 vs 7 clusters, the bootstrap and permutation require exchangeability, which is plausible but untestable.
  • domain assumption Constant mean model and BTC market model correctly specify expected returns over 250-day estimation and 36-day event windows
    CARs in §4.2 are defined against these models; misspecification (acknowledged via Goldsmith-Pinkham and Lyu 2025) would bias abnormal returns.
  • domain assumption Event classification and dating are independent of post-event returns
    §3.1 includes a return threshold and §6.4.5 notes gradual-event dating uses 'first major price discontinuity,' so this assumption is partly violated.
  • domain assumption Companion variance result (Farzulla 2025a) is valid and comparable
    §6.3 interprets the null CAR result through a 5.7× variance differential from a companion paper with different data source, assets, and sample period; its validity is not established in this manuscript.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Do Cryptocurrency Markets Differentiate Infrastructure from Regulatory Shocks? A Multi-Moment Event Study with Dependence-Robust Inference." pith.science (2026). https://pith.science/paper/6HBCXFWV

@misc{pith2026260207046,
  author       = {Pith},
  title        = {Pith review of: Do Cryptocurrency Markets Differentiate Infrastructure from Regulatory Shocks? A Multi-Moment Event Study with Dependence-Robust Inference},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6HBCXFWV}},
  note         = {Machine review of arXiv:2602.07046}
}
abstract

Do cryptocurrency markets process infrastructure failures differently from regulatory shocks? We study both moments of the return distribution on one shared sample (50 events, six assets, 2019-2025), fitting a GJR-GARCH-X model under matched dependence-robust inference. We treat event inclusion as a measured design parameter: rather than asserting the selection-on-the-dependent-variable objection away, we trace the variance differential across the inclusion screen and measure the selection bias directly. The result is a scope condition -- under curated, high-salience identification the differential is sizeable ($4.88\times$) but selection-conditional: a mechanical impact filter on a broad reconstructed pool collapses it to $1.3$-$1.6\times$. Identification is half the story; inference is the other. The curated multiplier is not distinguishable from zero once cross-asset dependence and heavy tails are respected: a Student-$t$-copula CCC-GARCH-X bootstrap (our inference of record) returns $p \approx 0.32$, and because the six per-asset coefficients are strongly cross-correlated the contrast's effective sample size is nearer three than six (design-effect $p \approx 0.07$-$0.15$). A naive i.i.d. test had reported an apparently decisive fivefold effect, but that significance was an artefact: pseudoreplication across correlated assets compounded by a heavy-tail-misspecified bootstrap. The first moment tells the same story -- a $+7.19$ pp cumulative-abnormal-return difference a block bootstrap cannot distinguish from zero ($p = 0.283$). Under correct inference the asymmetry is directional but unresolved. The contribution is a portable inference toolkit -- an inference ladder and a Monte-Carlo size study -- for diagnosing how cross-asset event studies in heavy-tailed markets manufacture significance, demonstrated where it dissolves a fivefold result the author had himself published.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Extremity Premium: Sentiment Regimes and Adverse Selection in Cryptocurrency Markets

    q-fin.ST 2026-02 reject novelty 5.0 of 10

    Extreme sentiment regimes show higher estimated spreads and uncertainty than neutral ones in Bitcoin data, but the effect is sensitive to controls and overlaps mechanically with volatility.

Reference graph

Works this paper leans on

31 extracted references · 14 canonical work pages · cited by 1 Pith paper

  1. [3]

    Raphael Auer and Stijn Claessens

    arXiv:2502.05054. Raphael Auer and Stijn Claessens. Regulating cryptocurrencies: Assessing market reactions.BIS Quar- terly Review, pages 51–65, September

    arXiv

  2. [6]

    A Colin Cameron, Jonah B Gelbach, and Douglas L Miller

    doi: 10.1016/j.frl.2019.101333. A Colin Cameron, Jonah B Gelbach, and Douglas L Miller. Bootstrap-based improvements for inference with clustered errors.Review of Economics and Statistics, 90(3):414–427,

    arXiv 2019

  3. [10]

    Carlos Esparcia, Ana Escribano, and Francisco Jareño

    doi: 10.1111/1467-8551.70002. Carlos Esparcia, Ana Escribano, and Francisco Jareño. Assessing the crypto market stability after the ftx collapse: A study of high frequency volatility and connectedness.International Review of Financial Analysis, 94,

    arXiv

  4. [11]

    Murad Farzulla

    doi: 10.1016/j.irfa.2024.103287. Murad Farzulla. Infrastructure vs regulatory shocks: Asymmetric volatility response in cryptocurrency markets.Research Square Preprint,

    arXiv 2024

  5. [12]

    Under review at Digital Finance

    doi: 10.21203/rs.3.rs-8323026/v1. Under review at Digital Finance. Murad Farzulla. Do whitepaper claims predict market behavior? Evidence from cryptocurrency factor analysis.arXiv preprint arXiv:2601.20336,

    arXiv

  6. [13]

    Are Whitepaper Claims Reflected in Market Structure? A Contamination-Aware Pipeline and a Power-Limited Null

    doi: 10.48550/arXiv.2601.20336. Paul Goldsmith-Pinkham and Tianshu Lyu. Causal inference in financial event studies.Working Paper, Yale University,

    work page Pith review arXiv doi:10.48550/arxiv.2601.20336

  7. [15]

    22 Da-Hea Kim

    doi: 10.1016/j.frl.2023.103670. 22 Da-Hea Kim. Effects of social media-based peer opinions on the prices of cryptocurrency options. Journal of Futures Markets,

    arXiv 2023

  8. [16]

    Frank H Knight.Risk, Uncertainty and Profit

    doi: 10.1002/fut.70004. Frank H Knight.Risk, Uncertainty and Profit. Houghton Mifflin, Boston,

    work page doi:10.1002/fut.70004

Show all 31 references
  1. [17]

    James W Kolari and Seppo Pynnonen

    doi: 10.1080/09638180.2022.2090399. James W Kolari and Seppo Pynnonen. Event study testing with cross-sectional correlation of abnormal returns.Review of Financial Studies, 23(11):3996–4025,

    2022

  2. [18]

    doi: 10.1093/rfs/hhq072. S.P. Kothari and Jerold B. Warner. Econometrics of event studies. InHandbook of Empirical Corporate Finance, pages 3–36. Elsevier,

    doi

  3. [20]

    Brian M Lucey, Samuel A Vigne, Larisa Yarovaya, and Yizhi Wang

    doi: 10.3386/w31160. Brian M Lucey, Samuel A Vigne, Larisa Yarovaya, and Yizhi Wang. The cryptocurrency uncertainty index.Finance Research Letters, 45:102147,

    doi

  4. [22]

    A Craig MacKinlay

    doi: 10.1016/j.jedc.2020.103980. A Craig MacKinlay. Event studies in economics and finance.Journal of Economic Literature, 35(1): 13–39,

    2020

  5. [24]

    Igor Makarov and Antoinette Schoar

    doi: 10.1016/j.jeconom .2022.04.001. Igor Makarov and Antoinette Schoar. Trading and arbitrage in cryptocurrency markets.Journal of Financial Economics, 135(2):293–319,

    2022 doi

  6. [25]

    Xinwen Ni, Wolfgang Karl Härdle, and Taojun Xie

    doi: 10.1016/j.jfineco.2019.07.001. Xinwen Ni, Wolfgang Karl Härdle, and Taojun Xie. A machine learning based regulatory risk index for cryptocurrencies.Computational Statistics, 40:3563–3583,

    2019 doi

  7. [26]

    Mitchell A

    doi: 10.1007/s00180-025-01629-y. Mitchell A. Petersen. Estimating standard errors in finance panel data sets: Comparing approaches.The Review of Financial Studies, 22(1):435–480,

    doi

  8. [28]

    Savva Shanaev, Satish Sharma, Binam Ghimire, and Arina Shuraeva

    doi: 10.1016/j.frl.2024.106413. Savva Shanaev, Satish Sharma, Binam Ghimire, and Arina Shuraeva. Taming the blockchain beast? reg- ulatory implications for the cryptocurrency market.Research in International Business and Finance, 51,

    2024

  9. [29]

    Sheela Sundarasen and Farida Saleem

    doi: 10.1016/j.ribaf.2019.101080. Sheela Sundarasen and Farida Saleem. From tweets to trades: A bibliometric and systematic review of social media’s influence on cryptocurrency.International Journal of Financial Studies, 13(2):87,

    2019

  10. [30]

    23 Simon Trimborn and Wolfgang Karl Härdle

    doi: 10.3390/ijfs13020087. 23 Simon Trimborn and Wolfgang Karl Härdle. CRIX an index for cryptocurrencies.Journal of Empirical Finance, 49:107–122,

    doi

  11. [1997]

    James G MacKinnon, Morten Ørregaard Nielsen, and Matthew D Webb

    doi: 10.2307/2729691. James G MacKinnon, Morten Ørregaard Nielsen, and Matthew D Webb. Cluster-robust inference: A guide to empirical practice.Journal of Econometrics, 232(2):272–299,

    doi

  12. [2007]

    Jiageng Liu, Igor Makarov, and Antoinette Schoar

    doi: 10.1016/B978-0-444-53265-7.50015-9. Jiageng Liu, Igor Makarov, and Antoinette Schoar. Anatomy of a run: The terra luna crash. NBER Working Paper 31160, National Bureau of Economic Research,

    doi

  13. [2008]

    Alessandro Casini and Adam McCloskey

    doi: 10.1162/rest .90.3.414. Alessandro Casini and Adam McCloskey. Identification, estimation and inference in high-frequency event study regressions.CEIS Research Paper No. 608, Tor Vergata University,

    doi

  14. [2009]

    Aman Saggu, Lennart Ante, and Kaja Kopiec

    doi: 10.1093/rfs/hhn053. Aman Saggu, Lennart Ante, and Kaja Kopiec. Uncertain regulations, definite impacts: The impact of the US securities and exchange commission’s regulatory interventions on crypto assets.Finance Research Letters, 72:106413,

    doi

  15. [2010]

    Akanksha Jalan and Roman Matkovskyy

    doi: 10.1198/jbes.2009.08046. Akanksha Jalan and Roman Matkovskyy. Systemic risks in the cryptocurrency market: Evidence from the ftx collapse.Finance Research Letters, 54,

    2009 arXiv

  16. [2018]

    doi: 10.1016/j.jempfin.2018.08.004. 24

    2018 doi

  17. [2019]

    Melisa Ate¸ s and Muhammet Sinan Ba¸ sarslan

    doi: 10.1007/s42521-019-00008-9. Melisa Ate¸ s and Muhammet Sinan Ba¸ sarslan. Performance comparison of traditional and contextual rep- resentations for cryptocurrency sentiment analysis on Twitter.Düzce Üniversitesi Bilim ve Teknoloji Dergisi (DUBITED), 13(3):1431–1444,

    doi

  18. [2020]

    Anton Badev and Cy Watsky

    doi: 10.24149/gwp381. Anton Badev and Cy Watsky. Interconnected defi: Ripple effects from the terra collapse. FEDS Working Paper 2023-044, Federal Reserve Board,

    2023 doi

  19. [2021]

    Thomas Conlon, Shaen Corbet, and Yang Hou

    doi: 10.1016/j.qre f.2021.05.005. Thomas Conlon, Shaen Corbet, and Yang Hou. Contagion effects of permissionless, worthless cryp- tocurrency tokens: Evidence from the collapse of ftx.Journal of International Financial Markets, Institutions and Money, 91:101940,

    2021 doi

  20. [2022]

    Štefan Lyócsa, Peter Molnár, Tomáš Plíhal, and Mária Širaˇnová

    doi: 10.1016/j.frl.2021.102147. Štefan Lyócsa, Peter Molnár, Tomáš Plíhal, and Mária Širaˇnová. Impact of macroeconomic news, regu- lation and hacking exchange markets on the volatility of bitcoin.Journal of Economic Dynamics and Control, 119,

    2021

  21. [2023]

    Ekkehart Boehmer, Jim Musumeci, and Annette B

    doi: 10.17016/FEDS.2023.044. Ekkehart Boehmer, Jim Musumeci, and Annette B. Poulsen. Event-study methodology under conditions of event-induced variance.Journal of Financial Economics, 30(2):253–272,

    2023 doi

  22. [2024]

    Douglas J

    doi: 10.1016/j.intfin.2024.101940. Douglas J. Cumming, Johannes Fuchs, and Paul P. Momtaz. Market reactions to cryptocurrency regu- lation: Risk, return and the role of enforcement quality.British Journal of Management,

    2024

  23. [2025]

    Sabrina Aufiero, Antonio Briola, Tesfaye Salarin, Fabio Caccioli, Silvia Bartolucci, and Tomaso Aste

    doi: 10.29130/dubited.1673097. Sabrina Aufiero, Antonio Briola, Tesfaye Salarin, Fabio Caccioli, Silvia Bartolucci, and Tomaso Aste. Cryptocurrencies in the balance sheet: Insights from (Micro)Strategy–Bitcoin interactions.arXiv preprint arXiv:2505.14655,

    arXiv

Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.