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arxiv: 2404.17227 · v3 · submitted 2024-04-26 · 💰 econ.GN · cs.CE· cs.CR· cs.CY· q-fin.EC· q-fin.RM

Trust Dynamics in Cryptocurrency Markets: Centralized vs. Decentralized Exchanges

Xintong Wu , Wanlin Deng , Yutong Quan , Lin William Cong , Luyao Zhang This is my paper

Pith reviewed 2026-05-24 02:25 UTC · model grok-4.3

classification 💰 econ.GN cs.CEcs.CRcs.CYq-fin.ECq-fin.RM
keywords cryptocurrencytrust dynamicscentralized exchangesdecentralized exchangesFTX collapsenatural experimenttopic modeling
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The pith

The FTX collapse caused significant price declines on centralized exchanges and capital reallocation toward decentralized ones.

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

The paper investigates how trust operates differently in centralized versus decentralized cryptocurrency exchanges and how those differences play out after a major institutional failure. It treats the FTX collapse as a natural experiment and applies causal inference to market data alongside computational text analysis of Discord communities. The results show measurable price drops and fund movements away from centralized platforms, even though raw sentiment scores did not jump, because topic models uncovered trust-related discussions that holiday topics had masked. A sympathetic reader would care because the findings point to concrete differences in how governance structures absorb or transmit shocks in crypto markets.

Core claim

The FTX collapse produced significant price declines and capital reallocation from centralized to decentralized exchanges; sentiment metrics showed no sharp discontinuities, yet topic modeling and network analysis of Discord data revealed that seasonal holiday discourse had obscured underlying trust concerns in centralized exchange forums.

What carries the argument

The FTX collapse treated as a natural experiment, paired with causal inference on prices and flows plus topic modeling of community discourse to isolate trust dynamics.

If this is right

  • Institutional trust in centralized exchanges proves fragile enough to trigger observable capital flight during crises.
  • Decentralized exchanges receive measurable inflows when centralized platforms lose trust.
  • Mixed quantitative and text-analytic methods can surface behavioral patterns that single-metric sentiment analysis misses.
  • Exchange operators and regulators gain concrete signals for assessing platform-specific risk during systemic events.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The pattern implies decentralized architectures may serve as a partial hedge against failures in centralized crypto infrastructure.
  • Similar trust-redistribution effects could be tested around other documented exchange failures or regulatory announcements.
  • Market-design choices that separate custody from trading may alter how quickly capital responds to trust shocks.

Load-bearing premise

That the FTX collapse functions as a clean exogenous shock whose effects on prices, flows, and discourse can be isolated from concurrent market or seasonal factors.

What would settle it

Finding no statistically significant difference in price declines or net capital flows between centralized and decentralized exchanges after matching on concurrent market-wide events and seasonal controls.

Figures

Figures reproduced from arXiv: 2404.17227 by Lin William Cong, Luyao Zhang, Wanlin Deng, Xintong Wu, Yutong Quan.

Figure 1
Figure 1. Figure 1: Coherence Score vs. Number of Topics Before and After Event [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Regression Discontinuity Design for Price [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Difference-in-Differences for WETH Prices (Gold as Control) [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Regression Discontinuity Design for NetFlow [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 7
Figure 7. Figure 7: Top 50 Words in Binance Community Pre-FTX Collapse [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Top 50 Words in Binance Community Post-FTX Collapse [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Intertopic Distance Map and Top-30 Terms Pre-FTX Collapse [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Network Analysis of Top 100 Nodes Pre-FTX Collapse [PITH_FULL_IMAGE:figures/full_fig_p008_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Network Analysis of Top 100 Nodes Post-FTX Collapse [PITH_FULL_IMAGE:figures/full_fig_p008_12.png] view at source ↗
read the original abstract

Trust mechanisms diverge between centralized and decentralized exchanges, representing distinct sociotechnical governance paradigms. However, quantifying trust dynamics and their redistribution between these architectures remains empirically challenging, limiting understanding of how institutional shocks affect market behavior. The FTX collapse offers a natural experiment to bridge this gap. Through an interdisciplinary approach combining causal inference and computational text analysis, we find significant price declines and capital reallocation from centralized to decentralized exchanges following the event. While sentiment metrics showed no sharp discontinuities, topic modeling and network analysis of Discord communities reveal that seasonal holiday discourse obscured underlying trust concerns in centralized exchange forums. These findings underscore the fragility of institutional trust architectures and demonstrate how mixed methods can illuminate behavioral patterns during systemic crises, offering insights for exchange risk management and regulatory assessment.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The manuscript claims that the FTX collapse functions as a natural experiment to quantify trust dynamics between centralized (CEX) and decentralized (DEX) cryptocurrency exchanges. Combining causal inference on price and capital flow data with computational text analysis of Discord communities, it reports significant price declines and reallocation of capital from CEX to DEX after the event. Sentiment metrics show no sharp discontinuities, but topic modeling and network analysis indicate that seasonal holiday discourse obscured underlying trust concerns in CEX forums. The study concludes that institutional trust architectures are fragile and that mixed-methods approaches can illuminate behavioral responses during systemic crises.

Significance. If the identification strategy is valid and the text analysis reliably isolates latent trust concerns, the results would contribute to understanding how institutional shocks redistribute trust and capital across different exchange architectures. The mixed-methods design is a potential strength for illuminating behavioral patterns not captured by price data alone, with possible implications for risk management and regulation.

major comments (3)
  1. [Methods] The abstract and methods section provide no information on the identification strategy, control variables, statistical significance tests, or robustness checks for the claimed price declines and capital reallocation; without these the central causal claim cannot be evaluated.
  2. [Results (topic modeling)] No details are given on how holiday periods were handled in the topic model or how concurrent market factors were isolated from the FTX event, which is load-bearing for the claim that holiday discourse obscured trust concerns.
  3. [Identification and Data] The assumption that the FTX collapse is a clean exogenous shock whose effects on flows and discourse can be isolated is not supported by any discussion of identification or falsification tests in the provided text.
minor comments (1)
  1. [Abstract] The abstract could specify the exact time window, data sources for flows, and quantitative metrics (e.g., volume or TVL changes) used to measure reallocation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which identify key areas for improving the clarity and rigor of our methods, results, and identification strategy. We address each major comment below and will incorporate the suggested details in a revised manuscript.

read point-by-point responses

  1. Referee: [Methods] The abstract and methods section provide no information on the identification strategy, control variables, statistical significance tests, or robustness checks for the claimed price declines and capital reallocation; without these the central causal claim cannot be evaluated.

    Authors: We agree that the current manuscript does not provide adequate detail on these elements in the abstract or methods section. In the revision, we will expand the methods section to describe the causal inference framework (including the specific econometric specification for price and flow analysis), list the control variables employed, report statistical significance tests with standard errors, and present robustness checks such as alternative event windows and placebo tests. The abstract will also be updated to reference the identification approach. revision: yes

  2. Referee: [Results (topic modeling)] No details are given on how holiday periods were handled in the topic model or how concurrent market factors were isolated from the FTX event, which is load-bearing for the claim that holiday discourse obscured trust concerns.

    Authors: The referee is correct that explicit details on these aspects are missing. We will revise the results section on topic modeling to specify how holiday periods were identified and controlled (e.g., via separate modeling or exclusion of December posts) and to describe steps for isolating the FTX event from concurrent factors, such as additional covariates or sensitivity analyses comparing holiday and non-holiday windows. revision: yes

  3. Referee: [Identification and Data] The assumption that the FTX collapse is a clean exogenous shock whose effects on flows and discourse can be isolated is not supported by any discussion of identification or falsification tests in the provided text.

    Authors: We acknowledge the lack of explicit discussion on this point. The revised manuscript will include a new subsection on the identification strategy that justifies treating the FTX collapse as exogenous (citing its idiosyncratic timing and firm-specific triggers) and reports falsification tests, including pre-trend checks and placebo event dates, to support isolation of effects on flows and discourse. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents an empirical mixed-methods study using the FTX collapse as an external dated event for causal inference on prices, flows, and Discord text data. No equations, fitted parameters, or derivation chains are described in the provided text; outcomes are tied to observable market data and external sources rather than being defined in terms of quantities constructed from the same fitted values. No self-citation load-bearing steps, ansatz smuggling, or renaming of known results appear. The central claims rest on identification from an exogenous shock and computational text analysis, which are independent of the reported patterns by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that the FTX collapse constitutes a sufficiently clean natural experiment and that computational text methods can recover latent trust dynamics from public chat data.

axioms (2)
  • domain assumption The FTX collapse serves as an exogenous shock suitable for causal inference on trust dynamics.
    Invoked when the abstract describes the event as a natural experiment.
  • domain assumption Topic modeling and network analysis of Discord communities can surface underlying trust concerns even when aggregate sentiment metrics show no discontinuity.
    Required for the interpretation that holiday discourse obscured trust issues.

pith-pipeline@v0.9.0 · 5679 in / 1307 out tokens · 21152 ms · 2026-05-24T02:25:52.664846+00:00 · methodology

discussion (0)

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Reference graph

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