## 3.1    The GENIUS Act

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### Event selection and identification
- Shock criteria:
  - (a) affects future use of stablecoins in payments, and (b) is sufficiently important to dominate background noise in stock price returns (Casini and McCloskey, 2025).
- Legislative event used: approval of Senate Bill 1582 (S.1582), the Guiding and Establishing National Innovation for U.S. Stablecoins Act (the GENIUS Act).
- Key features of the GENIUS Act (as described):
  - requirements for 100% reserve backing with liquid assets;
  - issuers to publish monthly disclosures of these reserves;
  - independent audits for issuers exceeding certain size thresholds (The White House, 2025).
- Rationale for focusing on the decisive House vote on July 17, 2025:
  - Occurred during “Crypto Week,” the GENIUS Act was the only crypto-related law passed that week and attracted by far the most attention.
  - Senate had approved the bill; the House took up the Senate’s version without amendments; the president publicly endorsed and immediately signed the bill into law.
  - House passage was not certain ex ante—there were intra-Republican and cross-party disagreements and competing measures (e.g., H.R.2392, the STABLE Act); contemporaneous news highlighted last-minute haggling and presidential intervention.
- Empirical validation:
  - Vote coincided with a sudden increase in prediction market-implied probability of stablecoin legislation passing before year-end and increases in prices of native assets of the Ethereum and Solana blockchains (Figure 1a referenced).

### Data
- Primary data sources and constructs:
  - (i) Stock prices, balance sheets, and income statements from Bloomberg for three firm sets: payment firms (GICS code 40201060), other financial sector firms (GICS prefix 40 excluding payment firms), and S&P 500 firms excluding the previous sets; balance sheet and income statement data also from Bloomberg.
  - (ii) Legislative events: minute-level timestamps constructed using C-SPAN’s congressional vote archive; baseline focuses on House approval of S.1582 on July 17, 2025.
  - (iii) Payment firm characteristics:
    - payment firms defined as GICS code 40201060, “Transaction & Payment Processing Services.”
    - sample yields 35 listed U.S. payment firms with combined market capitalization of $1.5 trillion in January 2025—equivalent to 65% of the aggregate market capitalization of all listed U.S. commercial banks.
    - Three sub-groups for heterogeneity tests:
      - G_X: firms whose primary business focuses on cross-border payments.
      - G_N: firms whose primary business involves operating one or more networks.
      - G_E: firms that had engaged with crypto assets prior to passage of the GENIUS Act (defined as either (i) issuing a crypto asset or (ii) offering services that use an existing crypto asset).
    - American Express included despite GICS classifying it as a bank because it operates one of the largest card networks.
  - (iv) Prediction market-implied probabilities from Polymarket market “U.S. enacts stablecoin bill in 2025?” used as a high-frequency numerical proxy for market expectations.

### Empirical specifications
- Objective: estimate γ_t as empirical counterparts of hypothetical quantities ̄η_t from Section 2.
- Baseline difference-in-differences (equation (1)):
  - Outcome: ln(P_i,t) with firm (α_i) and time (α_t) fixed effects.
  - Indicator for payment firms 1{i∈G_P}; 15-minute intervals during regular trading hours; t* denotes interval containing decisive vote.
  - Estimates ˆγ_τ × 100 reflect differential cumulative return, in percentage points, of payment firms relative to other financial firms between pre-vote and period τ.
  - Standard errors clustered by firm i and period t.
- Average differential return over post-vote interval (equation (2)): ln(P_i,t) = α_i + α_t + γ 1{t≥t*} × 1{i∈G_P} + ε_i,t.
- Heterogeneity specifications (equations (3) and (4)):
  - Allow γ_g,τ for subset g (G_X, G_N, G_E) to differ from γ_¬g,τ for other payment firms.
  - For Hypothesis 3 (g = G_N), cross-border firms (i ∈ G_X) are dropped to compare domestic network operators cleanly.

### Results — baseline and robustness
- Pre-event: differential returns in the pre-event window are not statistically significant.
- Post-vote average effects (five-hour window):
  - Incumbent payment firms’ stock returns are on average 0.75 percentage points lower than those of other financial firms across five hours.
  - Statistical significance: differences significant at the 1% level (Table 1, Column 1).
  - Market-cap weighted average difference: 1.3 percentage points (Table 1, Column 2).
  - Multiplying the market-cap weighted difference by ex-ante market capitalization yields approximately $21.5 billion of lost market capitalization.
- Robustness checks (Appendix C):
  - Results similar when using alternative control groups, controlling for differential trends by firm size and profitability, and varying event window width.
  - Similar findings when examining a broader set of congressional events advancing the GENIUS Act.
  - Differential decline specifically triggered by passage of S.1582 rather than other crypto-related legislation that week.
  - Similar relationship when regressing firm value directly on market-implied probability of passage over the bill’s House tenure.
  - Placebo tests confirm results are specific to legislative windows with stablecoin news shocks.

### Mechanisms
- Distinguishing channels: decline in expected level of future cash flows vs increase in expected uncertainty.
- Approach: repeat baseline using firms’ 30-day and 24-month put-implied volatility as outcomes (Appendix D).
- Finding:
  - no significant relative changes in option-implied volatility (Figure D.1a), consistent with the decline being driven primarily by a change in expected level of incumbents’ future cash flows.

### Heterogeneity of responses
- Cross-border payment firms:
  - Experienced larger declines.
  - On average, non-cross-border payment firms’ returns were 0.67 percentage points below other financial firms post-event, while cross-border firms were an additional 0.64 percentage points lower (Table 1, Column 3).
  - Consistent with Hypothesis 2 and stablecoins’ comparative advantage for cross-border payments.
- Network operators (G_N) and crypto-engaged firms (G_E):
  - Firms operating networks and those already engaged with crypto did not fall significantly relative to other financial firms in the post-event window.
  - Their returns were significantly higher than those of other payment firms (Table 1, Columns 4 and 5).
  - Incremental interactions:
    - Network operators interaction: 0.885 ∗∗ (2.03).
    - Crypto-engaged interaction: 0.845 ∗∗ (2.21).
  - Findings consistent with Hypotheses 3 and 4: network effects insulate incumbents; prior crypto engagement improves ability to exploit or respond to opportunities.
- Robustness:
  - Results for G_N hold even when excluding crypto-engaged firms; results for G_E hold even when excluding network firms (Figures D.1c and D.1d).
  - Cross-border result is not driven by a single firm (Figure D.1b).

### Benchmarking and overall impact estimation
- Conceptual scaling:
  - Denote p_{t*−1} the prior probability of the GENIUS Act becoming law before House vote, p_t the posterior probability after.
  - Assume linear relationship ̄η_t = ̄η_G (p_t − p_{t*−1}).
- Empirical inputs:
  - Market-cap-weighted estimate ̄η_t ≈ −0.013 (i.e., −1.3 percentage points).
  - Polymarket-implied prior probability p_{t*−1} ≈ 0.93 and posterior p_t ≈ 1 in the five trading hours before and after the House vote.
- Back-of-the-envelope overall impact estimate (equation (5)):
  - ̄η_G = ̄η_t / (p_t − p_{t*−1}) ≈ −0.013 / (1 − 0.93) = −18%.
  - Multiplying −18% by market capitalization of payment firms the day before the vote yields an overall estimate of approximately $300 billion in lost market value.
- Linearity assumption and sensitivity:
  - Caveat: if linearity does not hold, scaling could over- or under-estimate the true overall effect.
  - Sensitivity checks: estimates vary from 13–27%, or $220–470 billion, when (i) examining other legislative milestones with different initial probabilities or (ii) using alternative window widths producing different probability updates (Figure 3).
- Comparative benchmarks (Figure 4 and Appendix E):
  - Impact of U.S. stablecoin legislation on U.S. payment firms was larger than impacts of recent pro-competitive regulatory shocks: (a) Durbin Amendment (2010) capping debit card interchange fees; (b) President Trump’s announced support for the Credit Card Competition Act in January 2026; (c) anticipated launch of the digital euro (2016–2022).
  - Impact was smaller than severe regulatory actions targeting specific non-U.S. payment firms: (d) Reserve Bank of India directing Paytm Payments Bank to stop accepting fresh deposits in January 2024; (e) suspension of a major global mobile payments platform IPO with subsequent stringent regulation.

### Incumbent responses and descriptive evidence
- Post-passage behavior (Appendix F):
  - Increases in both the share of payment firms offering crypto-based services and mention frequency of stablecoins on earnings calls in months immediately after the GENIUS Act passed.
  - Corpus of more than 10,000 transcripts covering 97% of payment firms used to count mentions; mentions peaked near nearly one mention per call in September 2025 (Figure F.1).

### Conclusion and implications
- Market participants expect stablecoins to play an important role in payments despite lack of a widely adopted digital-cash equivalent to date.
- The decisive congressional vote on the GENIUS Act—designed to support payment stablecoins—led to a significant decline in market value of incumbent payment firms, signaling expectations of heightened competition in payments.
- Heterogeneous effects:
  - Larger impact on firms specializing in cross-border payments.
  - Smaller impact on incumbents protected by network effects.
  - Smaller impact on incumbents already offering crypto-related services.
- Back-of-the-envelope scaling suggests the GENIUS Act could have a large overall impact on payment firms (central estimate −18%, sensitivity range 13–27%, or $220–470 billion), though interpretation should be cautious due to linearity assumptions.
- Future research:
  - If stablecoin usage in payments grows and direct data on stablecoin payment transactions become available, future studies could directly test these predictions.

*Source: wpiea2026052-source-pdf — 3.1 The GENIUS Act*

### 3.1    The GENIUS Act

### 3.1    The GENIUS Act

### Event selection and identification
- Shock criteria: (a) affects future use of stablecoins in payments, and (b) is sufficiently important to dominate background noise in stock price returns (Casini and McCloskey, 2025).
- Legislative event used: approval of Senate Bill 1582 (S.1582), the Guiding and Establishing National Innovation for U.S. Stablecoins Act (the GENIUS Act).
- Key features of the GENIUS Act (as described): requirements for 100% reserve backing with liquid assets; issuers to publish monthly disclosures of these reserves; independent audits for issuers exceeding certain size thresholds (The White House, 2025).
- Rationale for focusing on the decisive House vote on July 17, 2025:
  - Occurred during “Crypto Week,” with elevated attention to crypto-related legislation; the GENIUS Act was the only crypto-related law passed that week and attracted by far the most attention.
  - The House vote resolved the last significant uncertainty: the Senate had already approved the bill and the House took up the Senate’s version without amendments; the president had publicly endorsed the bill and it was immediately presented to the president and signed into law.
  - The House passage was not certain ex ante—there were intra-Republican and cross-party disagreements and competing measures (e.g., H.R.2392, the STABLE Act); contemporaneous news highlighted last-minute haggling and presidential intervention.
- Empirical validation: Figure 1a (referenced) shows the vote was not perfectly anticipated; the vote coincided with a sudden increase in prediction market-implied probability of stablecoin legislation passing before year-end and increases in prices of native assets of the Ethereum and Solana blockchains.

### Data
- Data sources and constructs:
  - (i) Stock prices, balance sheets, and income statements from Bloomberg for three firm sets: payment firms (GICS code 40201060), other financial sector firms (GICS prefix 40 excluding payment firms), and S&P 500 firms excluding the previous sets; balance sheet and income statement data also from Bloomberg.
  - (ii) Legislative events: minute-level timestamps constructed using C-SPAN’s congressional vote archive; baseline focuses on House approval of S.1582 on July 17, 2025.
  - (iii) Payment firm characteristics: payment firms defined as GICS code 40201060, “Transaction & Payment Processing Services.” Sample yields 35 listed U.S. payment firms with combined market capitalization of $1.5 trillion in January 2025—equivalent to 65% of the aggregate market capitalization of all listed U.S. commercial banks.
    - Three sub-groups for heterogeneity tests:
      - set G_X: firms whose primary business focuses on cross-border payments.
      - set G_N: firms whose primary business involves operating one or more networks.
      - set G_E: firms that had engaged with crypto assets prior to passage of the GENIUS Act (defined as either (i) issuing a crypto asset or (ii) offering services that use an existing crypto asset).
    - Note: American Express is included despite GICS classifying it as a bank, because it operates one of the largest card networks.
  - (iv) Prediction market-implied probabilities from Polymarket market “U.S. enacts stablecoin bill in 2025?” used as a high-frequency numerical proxy for market expectations.

### Empirical specifications
- Objective: estimate γ_t as empirical counterparts of hypothetical quantities ̄η_t from Section 2.
- Baseline difference-in-differences (equation (1)):
  - Outcome: ln(P_i,t) with firm (α_i) and time (α_t) fixed effects; indicator for payment firms (1{i∈G_P}); 15-minute intervals during regular trading hours; t* denotes interval containing decisive vote.
  - Estimates ˆγ_τ × 100 reflect differential cumulative return, in percentage points, of payment firms relative to other financial firms between pre-vote and period τ.
  - Standard errors clustered by firm i and period t.
- Average differential return over post-vote interval (equation (2)): ln(P_i,t) = α_i + α_t + γ 1{t≥t*} × 1{i∈G_P} + ε_i,t.
- Heterogeneity specifications (equations (3) and (4)):
  - Allow γ_g,τ for subset g (G_X, G_N, G_E) to differ from γ_¬g,τ for other payment firms.
  - For Hypothesis 3 (g = G_N), cross-border payment firms (i ∈ G_X) are dropped to compare domestic network operators cleanly.

### Results — baseline and robustness
- Pre-event: differential returns in the pre-event window are not statistically significant.
- Post-vote average effects (five-hour window):
  - Incumbent payment firms’ stock returns are on average 0.75 percentage points lower than those of other financial firms across five hours.
  - Statistical significance: differences significant at the 1% level (Table 1, Column 1).
  - Market-cap weighted average difference: 1.3 percentage points (Table 1, Column 2).
  - Multiplying the market-cap weighted difference by ex-ante market capitalization yields approximately $21.5 billion of lost market capitalization.
- Robustness checks (Appendix C):
  - Results similar when using alternative control groups, controlling for differential trends by firm size and profitability, and varying event window width.
  - Similar findings when examining a broader set of congressional events advancing the GENIUS Act.
  - Differential decline specifically triggered by passage of S.1582 rather than other crypto-related legislation that week.
  - Similar relationship when regressing firm value directly on market-implied probability of passage over the bill’s House tenure.
  - Placebo tests confirm results are specific to legislative windows with stablecoin news shocks.

### Mechanisms
- Distinguishing channels: decline in expected level of future cash flows vs increase in expected uncertainty.
- Approach: repeat baseline using firms’ 30-day and 24-month put-implied volatility as outcomes (Appendix D).
- Finding: no significant relative changes in option-implied volatility (Figure D.1a), consistent with the decline being driven primarily by a change in expected level of incumbents’ future cash flows.

### Heterogeneity of responses
- Cross-border payment firms:
  - Experienced larger declines. On average, non-cross-border payment firms’ returns were 0.67 percentage points below other financial firms post-event, while cross-border firms were an additional 0.64 percentage points lower (Table 1, Column 3).
  - Consistent with Hypothesis 2 and stablecoins’ comparative advantage for cross-border payments.
- Network operators (G_N) and crypto-engaged firms (G_E):
  - Firms operating networks and those already engaged with crypto did not fall significantly relative to other financial firms in the post-event window.
  - Their returns were significantly higher than those of other payment firms (Table 1, Columns 4 and 5).
  - Findings are consistent with Hypotheses 3 and 4: network effects insulate incumbents; prior crypto engagement improves ability to exploit or respond to opportunities.
- Robustness: results for G_N hold even when excluding crypto-engaged firms; results for G_E hold even when excluding network firms (Figures D.1c and D.1d). The cross-border result is not driven by a single firm (Figure D.1b).

### Benchmarking and overall impact estimation
- Conceptual scaling: denote p_{t*−1} the prior probability of the GENIUS Act becoming law before House vote, p_t the posterior probability after. Assume linear relationship ̄η_t = ̄η_G (p_t − p_{t*−1}) (following Snowberg et al., 2011; Hazell and Hobler, 2024).
- Empirical inputs:
  - Market-cap-weighted estimate ̄η_t ≈ −0.013 (i.e., −1.3 percentage points).
  - Polymarket-implied prior probability p_{t*−1} ≈ 0.93 and posterior p_t ≈ 1 in the five trading hours before and after the House vote.
- Back-of-the-envelope overall impact estimate (equation (5)):
  - ̄η_G = ̄η_t / (p_t − p_{t*−1}) ≈ −0.013 / (1 − 0.93) = −18%.
  - Multiplying −18% by market capitalization of payment firms the day before the vote yields an overall estimate of approximately $300 billion in lost market value.
- Linearity assumption and sensitivity:
  - Acknowledged caveat: if linearity does not hold, scaling could over- or under-estimate the true overall effect.
  - Sensitivity checks: estimates vary from 13–27%, or $220–470 billion, when (i) examining other legislative milestones with different initial probabilities or (ii) using alternative window widths producing different probability updates (Figure 3).
- Comparative benchmarks (Figure 4 and Appendix E):
  - Impact of U.S. stablecoin legislation on U.S. payment firms was larger than impacts of recent pro-competitive regulatory shocks: (a) Durbin Amendment (2010) capping debit card interchange fees; (b) President Trump’s announced support for the Credit Card Competition Act in January 2026; (c) anticipated launch of the digital euro (2016–2022).
  - Impact was smaller than severe regulatory actions targeting specific non-U.S. payment firms: (d) Reserve Bank of India directing Paytm Payments Bank to stop accepting fresh deposits in January 2024; (e) suspension of a major global mobile payments platform IPO with subsequent stringent regulation.
- Incumbent responses:
  - Descriptive evidence (Appendix F) shows increases in both the share of payment firms offering crypto-based services and mention frequency of stablecoins on earnings calls in months immediately after the GENIUS Act passed.

### Conclusion
- Despite lack of a digital-cash equivalent achieving widespread adoption to date, market participants expect stablecoins to play an important role in payments.
- The decisive congressional vote on the GENIUS Act—designed to support payment stablecoins—led to a significant decline in market value of incumbent payment firms, signaling expectations of heightened competition in payments.
- Heterogeneous effects:
  - Larger impact on firms specializing in cross-border payments.
  - Smaller impact on incumbents protected by network effects.
  - Smaller impact on incumbents already offering crypto-related services.
- Back-of-the-envelope scaling suggests the GENIUS Act could have a large overall impact on payment firms (central estimate −18%, sensitivity range 13–27%, or $220–470 billion), though this should be interpreted with caution due to linearity assumptions.
- Future research: if stablecoin usage in payments grows and direct data on stablecoin payment transactions become available, future studies could directly test these predictions.

*Source: wpiea2026052-source-pdf — 3.1 The GENIUS Act*

### References

### wpiea2026052-source-pdf - References

### Bibliographic references
- Comprehensive reference list covering literature on stablecoins, cryptocurrencies, payment systems, prediction markets, monetary policy high-frequency identification, blockchain technology, CBDCs, cross-border payments, and related empirical and theoretical topics.
- Key recurring authors and institutions: ACEMOGLU; AGUR; AHMED; AIELLO et al.; ALLAIRE; ALVAREZ et al.; ARROW et al.; AUER et al.; BACK; BAUER & SWANSON; BERG et al.; BERTSCH; BUDISH; BURLON et al.; CASINI & MCCLOSKEY; CATALINI et al.; CERUTTI et al.; CHAUM; CHIU & KOEPPL; CHO et al.; CONG & HE; COPESTAKE et al.; CROUZET et al.; DAI; D’AVERNAS et al.; DIERCKS et al.; DRECHSEL & MIURA; DU et al.; EBRAHIMI et al.; FERRARI MINESSO & SIENA; FINANCIAL STABILITY BOARD; FINNEY; FRINGUELLOTTI & KROEN; GOLDSTEIN; GORTON et al.; GRAF VON LUCKNER et al.; GÜRKAYNAK et al.; HAZELL & HOBLER; HE et al.; HIGGINS; HINZEN et al.; JAROCIŃSKI & KARADI; KAY et al.; KOGAN et al.; KRUGMAN; KUTTNER; KÄNZIG; LI & MANN; LI & MAYER; LIAO et al.; MA et al.; MUKHARLYAMOV & SARIN; NAKAMOTO; NAKAMURA & STEINSSON; ONE 97 COMMUNICATIONS LIMITED; RESERVE BANK OF INDIA; REUTER; RHODE & STRUMPF; ROCHET & TIROLE; ROGOFF; SACKS; SALEH; SMETS; SNOWBERG et al.; SWANSON; SZABO; THE WHITE HOUSE; WANG; WEBER et al.; WEINBERG; WEITZMAN; WOLFERS & ZITZEWITZ.
- Publication types include Journal of Financial Economics, NBER Working Papers, IMF Working Papers, BIS Working Papers, American Economic Review, The Quarterly Journal of Economics, Journal of Monetary Economics, Review of Financial Studies, Management Science, Journal of Finance, Journal of Financial and Quantitative Analysis, SSRN papers, technical reports, press releases, and online resources.

### Figures (captions and key notes)
- Figure 1: Evolution of market values around the House passage of S.1582
  - Panel (a): Prediction market-implied probability (Polymarket: “U.S. enacts stablecoin bill in 2025?”) and percent changes in native asset prices for ETH and SOL relative to the period directly prior to the vote. Trading hours shown: event-time axis from -5 to 5 trading hours since event; secondary axis includes 90%, 95%, 100%.
  - Panel (b): Percent change in average prices of payment firms and other financial firms over trading hours since event (-5 to 5).
  - Panel (c): Baseline results estimated using equation (1). Shaded regions indicate 95% confidence intervals. Note: prices are shown for the same set of stock-market trading hours even though crypto and prediction markets operate 24/7.
- Figure 2: Differential return around House passage of S.1582, by firm group (percentage points)
  - Panels (a)–(c): Event-time coefficients from equation (3) at 15-minute frequency around decisive House vote. Groups: cross-border, networks, crypto-engaged. Baseline coefficients from Figure 1c plotted as blue dashed line. Shaded regions indicate 95% confidence intervals. Event-time axis shown from -5 to 5 trading hours since event.
- Figure 3: Sensitivity of implied overall-impact estimates
  - Baseline estimate of 18% (implied aggregate impact on incumbent payment firms’ market value) derived by scaling market-cap-weighted coefficient from equation (2) by the change in prediction-market-implied probability over the event window (see equation (5)).
  - Alternative windows shown: 4h, 8h, 12h, 24h, 48h. Other events: All events, Senate only.
  - Robustness procedures: (i) repeat baseline with different window widths; (ii) pooled specification across successful roll-call floor votes weighting by probability update (equation (C.4)); (iii) baseline specification for Senate passage scaled by corresponding probability update. See Appendix C for details.
- Figure 4: Comparing results to other historical shocks to payment firms
  - Benchmarks include: IPO suspended (major mobile payments platform); RBI blocks new deposits (One97 Communications); Digital euro (EU payment firms, positive); GENIUS Act (US cross-border payment firms; US payment firms); Digital euro (US payment firms); Durbin Amendment (US payment firms); Trump endorses CCCA (US payment networks).
  - Horizontal axis: Estimated negative impact on market value (percent). Red bars show estimates for U.S. stablecoin legislation; blue bars show other shocks. In case (iv) estimated impact is positive. Case (v) shows lower and upper bounds denoted by end of dark (light) blue bar. Durbin Amendment and digital euro estimates sourced from Berg et al. (2024). Figure axis range displayed from 0 to 60 percent for benchmarking.

### Table 1 (key coefficients and statistics)
- Title: Average differential return of payment firms relative to other financial firms after the House passage of S.1582 (percentage points)
- Main reported coefficients (with t-statistics in parentheses; * p <0.10, ** p <0.05, *** p <0.01):
  - All payment firms:
    - Column (1): -0.747 ∗∗∗ (−3.17)
    - Column (2): -1.272 ∗∗∗ (−3.73) — market-cap weighting applied
    - Column (3): -0.672 ∗∗ (−2.60)
    - Column (4): -1.055 ∗∗∗ (−2.80)
    - Column (5): -0.996 ∗∗∗ (−3.34)
  - Cross-border interaction (incremental difference): -0.642 ∗∗ (−2.22)
  - Network operators interaction (incremental difference): 0.885 ∗∗ (2.03)
  - Crypto-engaged interaction (incremental difference): 0.845 ∗∗ (2.21)
- Controls and sample:
  - Firm fixed effects included in all columns.
  - Period fixed effects included in all columns.
  - Market capitalization weighting applied in Column (2).
  - N: 27,839; 27,716; 27,839; 27,675; 27,839 across columns (1)–(5).
- Notes:
  - G_P denotes the set of all U.S.-listed payment firms.
  - G_X denotes firms primarily in cross-border payments.
  - G_N denotes firms primarily operating one or more networks.
  - G_E denotes firms early to engage with crypto assets (Appendix B definition).
  - Coefficients reflect average differential return in percent over the five hours after the decisive House vote on S.1582. Column (1) equally weighted; Column (2) market-cap weighted. Columns (3)–(5) show incremental differences for named categories. Column (4) excludes cross-border payment firms for comparison of domestic network vs non-network firms.

### Online Appendix (contents and selected findings)
- Appendix sections listed:
  - A Additional figures
  - B Payment firm groups
  - C Robustness
  - D Mechanisms and heterogeneity
  - E Benchmarks
  - F Additional descriptive evidence
- Appendix A highlights:
  - Figure A.1: Votes by party on S.1582 across five successful votes (Senate cloture May 19; Senate motion to proceed May 21; Senate cloture June 12; Senate vote June 17; House vote July 17). Votes shown by party with Y/N/A markers; x-axis counts votes cast by chamber.
  - Figure A.2: Attention regarding potential crypto asset legislation
    - Panel (a) Google Trends: Crypto Week timelines for May 1 to Aug. 1, indexed 0–100 for GENIUS Act, CLARITY Act, Anti-CBDC Surveillance State Act.
    - Panel (b) Mentions in firm communication: counts of earnings and conference calls mentioning GENIUS Act or CLARITY Act in May–August.
- Appendix B (Payment firm groups):
  - Definition: payment firms = GICS code 40201060 plus American Express (reclassified as payment firm).
  - Table B.1: Descriptive statistics (percentiles) for payment firms and sub-groups (G_P, G_X, G_N, G_E) with numerous numeric entries:
    - Example entries (p(25), p(50), p(75) for market capitalization ($ million)): 4302, 649, 20,068, 2,268, 22,026, 9,094 (table layout preserved in source).
    - Revenue ($ million, quarterly): 764, 121, 1,104, 336, 1,550, 1,035 (as shown).
    - Total assets ($ million): 382, 2,316, 14,674, 4,575, 6,555, 6,402.
    - Total liabilities ($ million): 152, 949, 13,695, 3,750, 5,462, 5,217.
    - Employees (#): 1,019, 2,800, 11,372, 2,603, 15,686, 9,306.
    - Net income ($ million, quarterly): -0.37, 14.71, 122.10, 15.24, 97.68, 6.3.
    - Return on assets: 0.33, 2.65, 9.10, 5.21, 5.71, 6.83.
    - Return on common equity: -4.64, 14.39, 32.39, 25, 22.94, 27.78.
    - Price/earnings ratio: 14.58, 26.93, 65.75, 9.95, 25.77, 33.1.
  - Classification process:
    - Function-level coding across closed-loop operator, open-loop operator, open-loop participant, processor, inter-party services, intra-party services, remote gateway, physical POS, cross-border — shown in Table B.2 with firm-by-function binary indicators (0/1).
    - Primary business function assigned and firms grouped into Payment Networks (G_N), Cross-Border Payments (G_X), and Other (Processing & Services).
  - Table B.3: Firms by primary business function listing many named firms under Payment Networks, Cross-Border Payments, and Other.
  - Table B.4: Firms engaged with crypto assets prior to House passage (G_E) with examples: Block Inc (Bitcoin via Cashapp); Cantaloupe Inc (crypto acceptance at POS); Fidelity National Information (partnered with Circle for stablecoin money movement); Jack Henry & Associates (partnered with NYDIG); Marqeta (crypto card issuing); Mastercard (crypto solutions); NCR Atleos (LibertyX acquisition); PayPal (USD stablecoin PYUSD); Ryvyl (crypto treasury solutions); Shift4 Payments (crypto payment processing); Visa (pilot stablecoin settlement services).
- Appendix C (Robustness and alternative specifications)
  - Alternative control groups: excluding banks, comparing to S&P 500 ex-financials — coefficients similar to baseline.
  - Additional controls: differential trends by firm characteristics (market capitalization, revenue, net income, return on assets) via equation (C.1); estimated γ_τ coefficients remain similar to baseline.
  - Alternative event window widths: 4 to 48 trading hours. Market-cap-weighted estimate remains significantly negative at 10% level even at 48 trading hours. Implied aggregate impacts from window scaling: roughly $220–470 billion (implied estimates row in Table C.1; summarized in Figure 3).
  - Pooled legislative events: stacked windows over successful roll-call floor votes; specifications (C.2), (C.3), and (C.4) described.
    - Specification (C.3) scales each window by ∆p_w / 0.07 where 0.07 is baseline probability update for decisive House vote.
    - Difference-in-differences variant (C.4) produces estimate of negative 1.6 percent (slightly larger than baseline negative 1.3 percent in Table 1). Applying scaling implies overall impact approximately 22.6% or $340 billion, close to baseline $300 billion.
  - Direct regression of stock prices on prediction-market probabilities over the month (equation (C.5)):
    - A 100 percentage-point rise in market-implied probability corresponds to a 16 percent differential return for payment firms relative to other financial firms (elasticity similar to baseline regressions).
  - Assessment of confounding legislation (H.R.3633): evidence suggests H.R.3633 unlikely to drive baseline results; one-minute-level analysis shows S.1582 vote coincides with the significant negative differential returns while H.R.3633 coincides with a small non-statistically-significant rise.
  - Robustness to excluding banks from control group, alternative window widths, pooled event specifications, and scaling by prediction-market probability updates all reported and discussed.

*Italic: Source — wpiea2026052-source-pdf - References (IMF PDF content unit provided).*

### 0.75 percent differential return in Table 1 Column 1 in response to a 6.3 percentage point change in the

### Stablecoins and the Future of Payments — Working Paper No. WP/2026/052

### Robustness checks and placebo tests
- Baseline equally weighted specification used because market-cap-weighted version is very sensitive to idiosyncratic shocks to the largest payment firms (Visa and Mastercard).
- Excluding cross-border firms: the differential decline among remaining payment firms remains similar to baseline (Figure C.1e).
- Placebo windows: 1000 randomly selected ten-hour windows (with replacement) between 1 May 2025 and 18 July 2025 (excluding studied windows) were used; the average differential return of payment firms is centered on zero in these placebo windows, supporting the control group as a counterfactual (Figure C.1f).
- Option-implied volatility: 30-day and 24-month put-implied volatility show no significant differential change, consistent with baseline results reflecting changes in expected level of future cash flows rather than uncertainty (Figure D.1a).

### Event-window estimates and key statistics (Table C.1 and related)
- Average differential return of payment firms after House passage of S.1582 (percentage points), unweighted coefficients for 1{t≥t*} × 1{i∈G_P} by window width:
  - 4 hours: -0.666 (t-statistic: -3.07) ***
  - 8 hours: -0.727 (t-statistic: -3.14) ***
  - 12 hours: -0.744 (t-statistic: -3.15) ***
  - 24 hours: -0.707 (t-statistic: -1.67) *
  - 48 hours: -1.435 (t-statistic: -1.27)
- Weighted (by pre-event market capitalization) coefficients for 1{t≥t*} × 1{i∈G_P}:
  - 4 hours: -1.083 (t-statistic: -2.64) **
  - 8 hours: -1.268 (t-statistic: -3.56) ***
  - 12 hours: -1.253 (t-statistic: -3.77) ***
  - 24 hours: -1.278 (t-statistic: -3.08) ***
  - 48 hours: -1.024 (t-statistic: -1.73) *
- Implied estimate ($ billion) of full impact on aggregate payment sector market capitalization (weighted estimate and corresponding change in prediction-market-implied probabilities):
  - Window 4 hours: -220
  - Window 8 hours: -292
  - Window 12 hours: -331
  - Window 24 hours: -470
  - Window 48 hours: -389
- Sample sizes:
  - N (unweighted): 11,492; 22,308; 33,124; 65,572; 130,468 (for 4, 8, 12, 24, 48-hour windows respectively)
  - N (weighted): 11,543; 22,407; 33,271; 65,863; 131,047
- Baseline specification uses a 10-hour window (reported coefficients in main text Figure 1c).

### Log-price response to market-implied probability (Table C.3)
- Coefficients for 1{i∈G_P} × p_t (average differential return in percentage terms per one percentage point change in market-implied probability):
  - Frequency 30min: -0.160 (t-statistic: -2.12) **
  - Frequency 1hr:   -0.166 (t-statistic: -2.05) **
  - Frequency 2hr:   -0.157 (t-statistic: -1.92) *
  - Frequency 4hr:   -0.143 (t-statistic: -1.79) *
  - Frequency 1d:    -0.206 (t-statistic: -3.11) ***
- Frequencies correspond to columns (1)–(5) with N = 195,824; 97,911; 48,614; 23,965; 15,064 respectively.
- Standard errors clustered by firm and by period; firm and period fixed effects included; control: 1{G_P} × t included.

### Mechanisms and heterogeneity
- Network operators and crypto-engaged firms each remain insulated from the shock even after removing overlapping firms (Figures D.1c and D.1d).
- Panel (b) of Figure D.1 shows individual specialized cross-border firms exhibit the largest percentage declines relative to other payment firms; payments excluding cross-border firms decline less.
- No significant differential change in put-implied volatility (30-day or 24-month), suggesting effects operate through expected cash flows rather than increased uncertainty (Figure D.1a).

### Benchmarks and comparative events
- Durbin Amendment (Dodd-Frank 2010): Berg et al. (2024) estimate reduced returns of U.S. payment firms by 5.3% in the week of announcement and finalization.
- Digital euro (Berg et al. (2024) estimates): shift from 0% to 100% probability reduced weighted average returns of U.S. payment firm stocks by 11.6% and increased weighted average returns of European payment firm stocks by 30.5%.
- Credit Card Competition Act (reintroduced January 13, 2026): using an analogous ten-trading-hour specification and weighting by ex-ante market capitalization, payment networks’ returns fell by 3.4% more than control group around the endorsement and reintroduction.
- RBI prohibition on new deposits into Paytm Payments Bank (One97 Communications Limited, OCL):
  - Differential returns for OCL vs NIFTY Financial Services Index control group:
    - Narrow window (day before vs day after): 11%
    - Wider window (15 days either side): 48%
- Suspension of major mobile payments platform IPO (2020): Fidelity estimate of a 51% reduction in overall firm value after IPO suspension.

### Descriptive evidence post-GENIUS Act
- Engagement with crypto assets:
  - Share of payment firms that had issued a crypto asset or offered services utilizing an existing crypto asset increased over 2025 and more quickly after the bill passed (Figure F.1).
- Mentions of stablecoins in earnings and conference calls:
  - Corpus of more than 10,000 transcripts covering 97% of payment firms used to count mentions (sentences containing “stablecoin” or names such as “USDT”, “USDC”).
  - Mentions were rare prior to introduction of S.1582, rose during congressional debate, and increased substantially after the GENIUS Act became law, peaking near nearly one mention per call in September 2025 (Figure F.1).
- Excerpts from earnings calls (Table F.1) illustrate shifts in firms’ public positioning on stablecoins across firm types (cross-border correspondence, card network, bank payment services, payments processing), including statements that with passage of the GENIUS Act firms are revising positions and expanding capabilities involving digital assets.

*Source: Stablecoins and the Future of Payments, Working Paper No. WP/2026/052*

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_Source: https://www.imf.org/-/media/files/publications/wp/2026/english/wpiea2026052-source-pdf.pdf_