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2. The Dollar Channel of Monetary Policy Transmission. (2025). Schmidt-Eisenlohr, Tim ; Niepmann, Friederike ; Meisenzahl, Ralf R.
   In: Finance and Economics Discussion Series.
   RePEc:fip:fedgfe:2025-46.

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3. Yield drifts when issuance comes before macro news. (2025). Üslü, Semih ; Pinter, Gabor ; Lou, Dong ; Walker, Danny.
   In: Journal of Financial Economics.
   RePEc:eee:jfinec:v:165:y:2025:i:c:s0304405x25000017.

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4. Monetary policy expectations and financial Markets: A Quantile-on-Quantile connectedness approach. (2025). Naifar, Nader.
   In: The North American Journal of Economics and Finance.
   RePEc:eee:ecofin:v:77:y:2025:i:c:s1062940825000294.

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5. The Dollar Channel of Monetary Policy Transmission. (2025). Meisenzahl, Ralf R ; Schmidt-Eisenlohr, Tim ; Niepmann, Friederike.
   In: CESifo Working Paper Series.
   RePEc:ces:ceswps:_11777.

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6. Monetary policy through the risk-taking channel: Evidence from an emerging market. (2024). Carrasco-Gutierrez, Carlos Enrique ; Passos, Felipe Vieira ; Amorim, Paulo Roberto.
   In: The Quarterly Review of Economics and Finance.
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7. What moves markets?. (2024). Kerssenfischer, Mark ; Schmeling, Maik.
   In: Journal of Monetary Economics.
   RePEc:eee:moneco:v:145:y:2024:i:c:s0304393224000138.

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8. Oil price shocks in real time. (2024). Gazzani, Andrea Giovanni ; Veronese, Giovanni ; Venditti, Fabrizio.
   In: Journal of Monetary Economics.
   RePEc:eee:moneco:v:144:y:2024:i:c:s0304393223001630.

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9. The economic impact of yield curve compression: Evidence from euro area forward guidance and unconventional monetary policy. (2024). Goodhead, Robert.
   In: European Economic Review.
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10. Monetary policy and Bitcoin. (2023). Karau, Soren.
    In: Journal of International Money and Finance.
    RePEc:eee:jimfin:v:137:y:2023:i:c:s0261560623000815.

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11. Stock market evidence on the international transmission channels of US monetary policy surprises. (2023). Nitschka, Thomas ; Maurer, Tim.
    In: Journal of International Money and Finance.
    RePEc:eee:jimfin:v:136:y:2023:i:c:s0261560623000670.

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12. Stock market reactions to monetary policy surprises under uncertainty. (2023). Segev, Nimrod ; Saadon, Yossi ; Benchimol, Jonathan.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:89:y:2023:i:c:s1057521923002995.

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13. Central bank information effects and transatlantic spillovers. (2022). Jarociński, Marek ; Jarociski, Marek.
    In: Journal of International Economics.
    RePEc:eee:inecon:v:139:y:2022:i:c:s0022199622001155.

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14. Spillover effects of sovereign debt-based quantitative easing in the euro area. (2022). Gnewuch, Matthias.
    In: European Economic Review.
    RePEc:eee:eecrev:v:145:y:2022:i:c:s0014292122000654.

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15. Market-stabilization QE. (2022). Motto, Roberto ; Ozen, Kadir.
    In: Working Paper Series.
    RePEc:ecb:ecbwps:20222640.

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16. Does the Federal Open Market Committee cycle affect credit risk?. (2022). Zhong, Zhaodong ; Li, Yubin ; Wang, Xinjie ; Huang, Difang.
    In: Financial Management.
    RePEc:bla:finmgt:v:51:y:2022:i:1:p:143-167.

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17. Monetary policy and Bitcoin. (2021). Karau, Soren.
    In: Discussion Papers.
    RePEc:zbw:bubdps:412021.

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18. Asset pricing and FOMC press conferences. (2021). Eriksen, Jonas Nygaard ; Bodilsen, Simon ; Gronborg, Niels S.
    In: Journal of Banking & Finance.
    RePEc:eee:jbfina:v:128:y:2021:i:c:s0378426621001229.

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19. Supplementary Paper Series for the Assessment (1): The Effects of the Bank of Japans ETF Purchases on Risk Premia in the Stock Markets. (2021). Kitamura, Tomiyuki ; Adachi, KO ; Hiraki, Kazuhiro.
    In: Bank of Japan Working Paper Series.
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1. , Full Sample (1994-2019, #213) Intraday Return t = a + b MP surprise t + e t
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7. All articles entering the average sentiment score are weighted by their relevance score (ranging from 0, ..., 100) so that articles with higher relevance receive a higher weight. We then compute the change in sentiment as the difference between the average, relevance-weighted sentiment scores in the post-FOMC window and then average, relevance-weighted sentiment scores in the pre-FOMC window. We do this for each of our 96 scheduled FOMC meetings to obtain a timeseries of sentiment changes. To avoid timing issues we do not include the day before the FOMC decision and the day after the FOMC meeting in the computation of sentiment scores. For example, if the FOMC meetings ends on July 27th, 2016, the x = 3 day pre-FOMC window runs from July 22nd to 25th whereas the 3-day post-FOMC window runs from July 29th to 31st. t-statistics in squared brackets are based on White standard errors. The sample period is from 2006 to 2017; 96 scheduled FOMC announcements.
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8. All Events (#213) Excluding ELB (12/2008-12/2015, #156) Short Rate Long Rate Risk Shift 0 0.2 0.4 0.6 0.8 1 R 2
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9. As discussed in Section 3 of the main paper, we observe fund flows almost every day when we start the sample in 2006. Figures IA.2 and IA.3 visualize the short rate and long rate monetary policy surprises.
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23. Bollerslev, T., J. Li, and Y. Xue (2018): “Volume, Volatility and Public News Announcements, ” Review of Economic Studies, 85, 2005–2041. 31 Electronic copy available at: https://ssrn.com/abstract=3774275 Bollerslev, T., G. Tauchen, and H. Zhou (2009): “Expected Stock Returns and Variance Risk Premia,” Review of Financial Studies, 22(11), 4463–4492.

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36. Engsted, T., T. Q. Pedersen, and C. Tanggaard (2012): “Pitfalls in VAR Based Return Decompositions: A Clarification,” Journal of Banking and Finance, 36, 1255–1265.

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38. Extended sample period results (-15m:+75m/close if followed by press conference) Full sample Before ELB Exclude ELB (1994-2019, #213) (1994-11/2008, #124) (ex.12/2008-12/2015, #156) SR LR RS SR LR RS SR LR RS b-0.31-0.04 0.58-0.31-0.05 0.53-0.30 0.10 0.53 t(b) -3.22-0.40 10.83-2.73-0.34 7.60-2.79 0.72 8.44 R2 13.19 0.21 44.25 14.45 0.25 34.30 13.57 1.00 37.43 muMP Shock>0-0.06 0.13 0.37-0.01 0.12 0.33-0.14 0.15 0.29 s.e. 0.09 0.07 0.06 0.11 0.11 0.09 0.10 0.09 0.07 muMP Shock<0 0.17-0.01-0.47 0.15 0.00-0.43 0.16-0.15-0.47 s.e. 0.08 0.10 0.10 0.13 0.14 0.15 0.10 0.11 0.14 Table IA.9: Monetary Policy Factor Comparisons This table compares different rate surprises extracted from fed fund futures and Eurodollar futures to our three monetary policy surprises. The data based on fed fund futures are taken from Gorodnichenko and Weber (2016).
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39. Extended Sample Period: Table IA.7 shows the results from the factor analysis (as in Table 1) using the extended (but less detailed) sample from 1994 - 2005. We then splice these monetary policy surprises from 1994 - 2005 with those from the more detailed dataset reported in Table 1 from 2006 - 2019.
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40. Fernandez-Perez, A., B. Frijns, and A. Tourani-Rad (2017): “When No News is Good News – The Decrease in Investor Fear After the FOMC Announcement,”Journal of Empirical Finance, 41, 187–199. 32 Electronic copy available at: https://ssrn.com/abstract=3774275 Frazzini, A., and O. Lamont (2008): “Dumb Money: Mutual Fund Flows and the CrossSection of Stock Returns,” Journal of Financial Economics, 88, 299–322.

41. Figure IA.11 provides the long horizon response of equity prices and flows for the extended sample period from 1994-2019 as well as the sub sample 1994-2005. The downside of the longer sample is that we need to rely on less detailed and less reliable data for this exercise.41 Yet, the reaction of stock returns is overall very similar to the results documented above for the more recent sample period. Prices revert back to a large extent within four weeks. The point estimates of the response of fund flows is more muted in the early sample period (1994-2005).
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42. FOMC,t + e t+h 1994-2019: #190 2006-2019: #95 1994-2005: #95 0 2 4 6 8 10 12 14 16 18 20 Days from FOMC announcement, h -1 -0.5 0 0.5 1 1.5 SPY ETF: Flow(Equity)t+h = ah + bh Risk Shiftt DFOMC,t + et+h 1994-2019: #190 2006-2019: #95 1994-2005: #95 IA – 39 Electronic copy available at: https://ssrn.com/abstract=3774275 Figure IA.12: NYSE Order Flow 0 2 4 6 8 10 12 14 16 18 20 Days from FOMC announcement, h -1 -0.5 0 0.5 1 1.5 2 Return t+h = a h + b h Order Flow FOMC,t D High/Low Absolute FOMC Risk Shift, t + e t+h 0 2 4 6 8 10 12 14 16 18 20 Days from FOMC announcement, h -30 -20 -10 0 10 20 30 40 50 Volumet+h = ah + bh DHigh/Low Absolute FOMC Risk Shift, t + et+h NYSE Volume: High Absolute Risk Shift NYSE Volume: Low Absolute Risk Shift IA – 40 Electronic copy available at: https://ssrn.com/abstract=3774275 Figure IA.13: Mutual Funds 0 2 4 6 8 10 12 14 16 18 20 Days from FOMC announcement, h -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
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43. Gabaix, X., and R. S. Koijen (2020): “Granular Instrumental Variables,” Working Paper.

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54. IA – 2 Electronic copy available at: https://ssrn.com/abstract=3774275 to be less responsive to news (e.g., Barber, Odean, and Zheng, 2005; Frazzini and Lamont, 2008).
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55. IA.E. Details on the return decomposition Campbell and Shiller (1988) and Campbell (1991) show that unexpected realized returns are equal to revisions in future expected dividends minus revisions in future expected returns: rt+1 − Et [rt+1] = ηd,t+1 − ηr,t+1, where ηd,t+1 = Et+1 hP∞ j=0 ρj dt+1+j i −Et hP∞ j=0 ρj dt+1+j i summarizes “cash-flow news”, and ηr,t+1 = Et+1 hP∞ j=1 ρj rt+1+j i − Et hP∞ j=1 ρj rt+1+j i summarizes “discount rate news”.
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69. Mutual Funds, 2006-2017: Return t+h = a h + b h Risk Shift t D FOMC,t + e t+h 0 2 4 6 8 10 12 14 16 18 20 Days from FOMC announcement, h -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
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72. Our event list is provided in Table IA.4. This event list is obtained from the comprehensive database collected by Cieslak and Schrimpf (2019). Figure IA.1 gives an overview on the ETF flow data that are available. The figure shows total assets under management for the equity and bond category that we employ in this paper.
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73. Ozdagli, A., and M. Weber (2016): “Monetary Policy Through Production Networks: Evidence from the Stock Market,” Working Paper, Chicago Booth.

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75. R2 , rolling window, past #40 Excluding effective lower bound (#156) Fed Funds Rate, (#133) All Events (#213) Long Rate Surprises effective lower bound 2000 2002 2005 2007 2010 2012 2015 2017 0 0.2 0.4 0.6 0.8 1 R 2
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76. Returns, Excluding the Financial Crisis (08/2007-12/2009) Exclude the financial crisis, but ignore confounding events (#92) Baseline 0 2 4 6 8 10 12 14 16 18 20 Days from FOMC announcement, h -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
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77. Risk Shifts effective lower bound 2000 2002 2005 2007 2010 2012 2015 2017 0 0.2 0.4 0.6 0.8 1
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78. S&P 500 Return (SPY, 1 min) FOMC Days, 2006 - 2019 (#112) All Announcements Positive Risk Shift #72 Negative Risk Shift #40 -3-2 -1 0 1 2 Hours Around FOMC Announcement 0 1 2 3 4 5 6 7 8
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83. Short Rate Surprises effective lower bound 2000 2002 2005 2007 2010 2012 2015 2017 0 0.2 0.4 0.6 0.8 1 R 2
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84. Staer, A. (2017): “Fund Flows and Underlying Returns: The case of ETFs,” International Journal of Business, 22(4), 275–304.
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85. Stoll, H. R. (1978): “The Supply of Dealer Services in Securities Markets,”Journal of Finance, 33(4), 1133–1151.

86. Swanson, E. T. (2020): “Measuring the Effects of Federal Reserve Forward Guidance and Asset Purchases on Financial Markets,” Journal of Monetary Economics, forthcoming.
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87. Swanson, E. T., and J. C. Williams (2014): “Measuring the Effect of the Zero Lower Bound on Medium- and Longer-Term InterestRates,” American Economic Review, 104(10), 3154–3185.

88. Table IA.8 compares the effect of the three monetary policy surprises on event window S&P 500 returns across several sub-samples. These results are (in part) shown in Figure 7 of the main paper. 40 See, e.g., Scholes (1972), Glosten and Milgrom (1985), Grossman and Miller (1988).
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89. The sample includes 114 FOMC announcements from 01/2006 to 12/2019.
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90. The sample period is from 2006 to 2017; 96 scheduled FOMC announcements.
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91. VAR parameter estimates: In Section 4 of the main paper, we utilize the discount rates news estimated by a Campbell and Shiller (1988)/Campbell (1991)-VAR. The procedure is explained in Appendix IA.E. Table IA.12 provides the parameter estimates. Textual analysis of market commentary: In Section 4 of the main paper, we discuss the results of an analysis of market commentary on FOMC days.
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92. Volume/average volume first 2 hours (SPY, 1 min) FOMC Days, 2006 - 2019 (#112) All Announcements Low Absolute Risk Shift #34 High Absolute Risk Shift #34 42 Electronic copy available at: https://ssrn.com/abstract=3774275 Figure 6: Fund Flow-Induced Price Pressures 0 2 4 6 8 10 12 14 16 18 20 Days from FOMC announcement, h -1 -0.5 0 0.5 1 1.5 2006-2019: Return t+h = a h + b h Risk Shift t D FOMC,t + e t+h S&P 500 Return - Rf Flow Pressure Component Unexplained by Flows (Permanent Component) 43 Electronic copy available at: https://ssrn.com/abstract=3774275 Figure 7: Intraday Returns: Extended Sample & Subsamples Intraday R 2
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93. We use the Bloomberg data as reported. We notice that other data providers (e.g., Datastream) often report shares outstanding with a lag of one day compared to Bloomberg.35 Figure IA.1 provides an overview of total net assets of ETFs over time. The cut-off year 2006 for our baseline results (from 01/02/2006 to 12/13/2019, 3,512 daily observations; 114 FOMC announcements) ensures that we almost always observe non-zero flows for “Blend” equity and “Broad” bond funds, see Table IA.2. Before the year 2006, equity flows come with a large fraction of days with a zero fund flow, which would make our analysis of fund flows unreliable. In our empirical analysis, we mainly focus on ETF fund flows as these flows are more likely to represent “fast money”. Transaction costs are low for ETFs (there are no front-end loads).
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94. where TNAi,t are total net assets of asset class i (equity or bond) at time t, and Ri,t is the fund return of asset class i. Because fund flows do not have an economically meaningful long-run mean or standard deviation, we follow the recent literature (e.g., Berk and van Binsbergen (2016), Menkhoff, Sarno, Schmeling, and Schrimpf (2016)) and use a normalization. In particular, we normalize flows by their moving average and standard deviation: F̃i,t = Fi,t − i,t (Fi,t−250;t−1) σi,t (Fi,t−250;t−1) , where i,t (Fi,t−250;t−1) and σi,t (Fi,t−250;t−1) are computed over lagged 250-day rolling days. If there is a zero flow, we ignore this observation when computing the rolling mean or standard deviation and we also do not adjust any zero flow observation in the sample. This means that we remove any local trends in the data, i.e. comparable to a year/fund category fixed effect.
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