R Notebook: CBDC Speeches

Introduction and data import

This R file generates the daily and weekly speech databases.

setwd("C:/Users/fkraus/Desktop/Articles/effects of CB speeches on stablecoins/2025")

# Packages
require(gt)
require(gtsummary)
library(dplyr)
library(tidyr)
library(stringr)
library(lubridate)
library(readr)
library(purrr)
library(httr)
library(jsonlite)
library(data.table)   # pratique pour expansions rapides
library(fixest)
library(ggplot2)
require(scales)

options(dplyr.summarise.inform = FALSE)
theme_set(theme_minimal(base_size = 12))

sent_pred <-   read.csv(file="C:/Users/fkraus/Desktop/Articles/effects of CB speeches on stablecoins/2025/2b_cbdc_sentence_preds_with_author_desc.csv") 
countries <-   read.csv(file="C:/Users/fkraus/Desktop/Articles/effects of CB speeches on stablecoins/2025/countries2.csv", sep=";")
titles <-   read.csv(file="C:/Users/fkraus/Desktop/Articles/effects of CB speeches on stablecoins/2025/cbdc_title_flags_from_matches.csv")

Speech database

Sentence-level database

Data import

We use the database 2b_cbdc_sentence_preds_with_author_desc.csv downloaded from the Kaggle notebook. We also use the countries2.csv file that merge the authors of speeches with their Central Bank and Countries. For some cases, there are multiple authors for a specific speech. Fortunately, these case are rare and they group authors from the same Central Banks and the same Countries. We manually retrieve their informations and add them directly.

sent_pred2 <- sent_pred %>%
  select(-c(country,description) )%>%
  distinct()%>%
  left_join(countries%>%unique(), by=c("author"))%>%
  mutate(Country =case_when(
    author=="Jacqueline Loh; Benoît Cœuré" ~ "ECB Board", 
    author=="Thomas Jordan; Martin Schlegel; Antoine Martin"~"Switzerland", 
    author=="Petra Tschudin; Thomas Moser"~"Switzerland",
    author=="Olaf Sleijpen; Klaas Knot; Steven Maijoor" ~ "Netherlands",
    TRUE ~ Country))%>% 
  mutate(Central_Bank =case_when(
    author=="Jacqueline Loh; Benoît Cœuré" ~ "European Central Bank", 
    author=="Thomas Jordan; Martin Schlegel; Antoine Martin"~"Swiss National Bank", 
    author=="Petra Tschudin; Thomas Moser"~"Swiss National Bank",
    author=="Olaf Sleijpen; Klaas Knot; Steven Maijoor" ~ "European Central Bank",
    TRUE ~ Central_Bank))%>%
  mutate(
    datetime = parse_date_time(
      date,
      orders = c("d/m/Y H:M:S", "d/m/Y H:M", "Y-m-d H:M:S", "Y-m-d H:M"),
      tz = "UTC"  
    ),
    date = as_date(datetime)  # keep a pure Date too (optional)
  )%>%select(-datetime)%>%
  filter(date >= as.Date("2015-01-01"))%>% select(-X)

We save the complete list of speeches in a .csv file (completelist.csv). Note that the database has loose keywords conditions, in order to capture all speeches related to CBDC. However, some sentences are linked to titles and/or references, or to crypto-assets exclusively (see for example the first observations above).

write.table(sent_pred2,
            file = "C:/Users/fkraus/Desktop/Articles/effects of CB speeches on stablecoins/2025/completelist.csv",
            sep = ";", dec = ".",
            row.names = FALSE, col.names = TRUE,
            fileEncoding = "UTF-8")

We manually note each title. The dataset list.csv is the same as completelist.csv, with an additional column cbdc_sentence that is equal to FALSE when sentences are related to keywords in title or in references. We then remove these “wrong” sentences, and add the corresponding currency to each Central Bank manually.

auer_data <- readxl::read_excel("C:/users/fkraus/Desktop/Articles/effects of CB speeches on stablecoins/speechesstances.xlsx")%>%filter(source=="auer")

sentence_base <- read.csv2("C:/Users/fkraus/Desktop/Articles/effects of CB speeches on stablecoins/2025/list.csv",
                        fileEncoding = "UTF-8",
                        stringsAsFactors = FALSE)%>%
  left_join(titles %>% select(url, cbdc_in_title), by='url')%>%
  unique()%>%
  mutate(cbdc_sentence = ifelse(is.na(cbdc_sentence), "TRUE",cbdc_sentence))%>%
  filter(!cbdc_sentence=="FALSE")%>% # we only keep the true CBDC-related sentences
  mutate(
    datetime = parse_date_time(
      date,
      orders = c("d/m/Y H:M:S", "d/m/Y H:M", "Y-m-d H:M:S", "Y-m-d H:M"),
      tz = "UTC"   
    ),
    date = as_date(datetime)  
  )%>%select(-datetime) %>%
select(-c(match_type,  keyword,source,  cbdc_sentence))%>%
  mutate(date = as.Date(date)) %>%
  distinct()%>%
  mutate(Currency = case_when(
    Central_Bank == "Bank of England" ~ "GBP", 
    Central_Bank == "European Central Bank" ~ "EUR", 
    Central_Bank == "Bank of Japan" ~ "JPY", 
    Central_Bank == "Bank of Canada" ~ "CAD", 
    Central_Bank == "Reserve Bank of Australia" ~ "AUD", 
    Central_Bank == "Monetary Authority of Singapore" ~ "SGD", 
    Central_Bank == "Sveriges Riksbank" ~ "SEK", 
    Central_Bank == "Bank of Korea" ~ "KRW", 
    Central_Bank == "Fed" ~ "USD", 
    Central_Bank == "Central Bank of Malaysia" ~ "MYR", 
    Central_Bank == "Central Bank of Chile" ~ "CLP", 
    Central_Bank == "National Bank of the Republic of (North) Macedonia" ~ "MKD", 
    Central_Bank == "Czech National Bank" ~ "CZK", 
    Central_Bank == "South African Reserve Bank" ~ "ZAR", 
    Central_Bank == "Bank of Botswana" ~ "BWP", 
    Central_Bank == "Central Bank of Barbados" ~ "BBD", 
    Central_Bank == "National Bank of Denmark" ~ "DKK", 
    Central_Bank == "Bank of Mauritius" ~ "MUR", 
    Central_Bank == "State Bank of Pakistan" ~ "PKR", 
    Central_Bank == "Central Bank of Sri Lanka"    ~ "LKR", 
    Central_Bank ==  "Swiss National Bank"  ~ "CHF", 
    Central_Bank ==  "Central Bank of the Philippines"  ~ "PHP", 
    Central_Bank == "Central Bank of Iceland"  ~ "ISK", 
    Central_Bank ==  "Bank of Thailand"  ~ "THB", 
    Central_Bank ==  "Reserve Bank of New Zealand" ~ "NZD", 
    Central_Bank == "Eastern Caribbean Central Bank" ~ "XCD", 
    Central_Bank == "Central Bank of The Bahamas"  ~ "BSD", 
    Central_Bank == "Hong Kong Monetary Authority" ~ "HKD", 
    Central_Bank ==  "Norges Bank"   ~ "NOK", 
    Central_Bank ==   "Central Bank of Bosnia and Herzegovina"     ~ "BAM", 
    Central_Bank ==  "Central Bank of Kuwait"  ~ "KWD", 
    Central_Bank == "Central Bank of Morocco"  ~ "MAD", 
    Central_Bank ==   "Bank of Mexico"  ~ "MXN", 
    Central_Bank == "Bank of Albania"   ~ "ALL", 
    Central_Bank ==   "Bank of Russia"  ~ "RUB", 
    Central_Bank == "Reserve Bank of India"  ~ "INR", 
    Central_Bank == "People's Bank of China" ~ "CNY", 
    Central_Bank == "Bank Indonesia"     ~ "IDR", 
    Central_Bank ==   "Central Bank of Kenya"~ "KES", 
    Central_Bank ==  "Central Bank of Nigeria"     ~ "NGN", 
    Central_Bank ==  "National Bank of Serbia"    ~ "RSD", 
    Central_Bank == "Bank of Israel"   ~ "ILS", 
    Central_Bank == "Central Bank of the Republic of Kosovo"  ~ "EUR", 
    Central_Bank == "Bank of Finland"  ~ "EUR", 
    Central_Bank ==  "Central Bank of Nepal"  ~ "NPR", 
    Central_Bank ==  "Central Bank of Cyprus"  ~ "EUR", 
    Central_Bank == "Central Bank of Eswatini"  ~ "SZL", 
    Central_Bank == "Bank of Slovenia" ~ "EUR", 
    TRUE~"Other"
    ))%>%
  select(-c(X, X.1))

sentence_base%>%filter(Central_Bank=="Bank of Finland")

Statistics on sentence-level data

We now have a sentence-level clean database that contains the type_label (retail, wholesale, general/unspecified), the sentiment_label (positive, negative, neutral), stance_label (Pro-CBDC, Anti-CBDC, Wait-and-See) and discourse_label (Risk-Benefit, Feature, Process) associated with each sentence measured by BERT.

Summary statistics of sentence-level database (N = 5397 sentences)
	Number of sentences	Share within dimension
Discourse
Process	2,381	44.1%
Feature	1,543	28.6%
Risk-Benefit	1,473	27.3%
Sentiment
neutral	3,029	56.1%
positive	1,937	35.9%
negative	431	8.0%
Stance
Pro-CBDC	2,813	52.1%
Wait-and-See	2,051	38.0%
Anti-CBDC	533	9.9%
Type de CBDC
Retail CBDC	2,854	52.9%
General/Unspecified	2,114	39.2%
Wholesale CBDC	429	7.9%

In total, there are 5397 uniques sentences, mostly about Retail CBDC (53% of speeches), while unspecified (39%) and wholesale (8%) are relatively marginally mentioned. Overall, the sentiment is essentially neutral yet pro-CBDC. In addition, we can see that there are less speeches about wholesale CBDC, but they tend to be more positive/Pro-CBDC :

In addition, we can observe that some Central Banks - and notably ECB - tend to communicate more than others :

Day/Week-level database

We have sentiment, stance, discourse and type of CBDC discussed at sentence-level. We transform the sentiments (positive, neutral et negative) and stances (Pro-CBDC, Wait-and-See et Anti-CBDC) in numeric values (1, 0, -1) to obtain similar data as in Auer & al (2021) and Dionysopoulos & Makridis (2025).

Then, for a given speech, we measure the average sentiment and stance. We then aggregate the sentiment and stance daily-level as their average across all speeches that day.

We also decompose by type of CBDC discussed within each day, as well as for ECB, Fed and other central banks, and for developed versus emerging markets central banks.

bankday_type <- speech_level %>%
  group_by(date, Central_Bank, type_label) %>%
  summarise(
    sent_btk   = mean(sent_speech,   na.rm = TRUE),
    stance_btk = mean(stance_speech, na.rm = TRUE),
    .groups = "drop"
  )



type_daily <- bankday_type %>%
  group_by(date, type_label) %>%
  summarise(
    index_sent_type   = mean(sent_btk,   na.rm = TRUE),
    index_stance_type = mean(stance_btk, na.rm = TRUE),
    .groups = "drop"
  )


bankday_all <- bankday_type %>%
  group_by(date, Central_Bank) %>%
  summarise(
    sent_bt_all   = mean(sent_btk,   na.rm = TRUE),
    stance_bt_all = mean(stance_btk, na.rm = TRUE),
    .groups = "drop"
  )


sentence_base2 <- sentence_base%>%
  select(url, date)%>%
  distinct(url, date)%>%
  inner_join(auer_data%>%select(URL_text, speech_stance), by=c("url"="URL_text"))%>%
  group_by(date)%>%
  summarize(stance_auer = mean(speech_stance))

global_daily <- bankday_all %>%
  group_by(date) %>%
  summarise(
    index_sent_global   = mean(sent_bt_all,   na.rm = TRUE),
    index_stance_global = mean(stance_bt_all, na.rm = TRUE),
    .groups = "drop"
  )%>%
  left_join(sentence_base2, by="date")%>%
  mutate(speechday = 1)


global_daily_deveme <- bankday_all %>%
  mutate(deveme = case_when(Central_Bank %in% c("Bank of England", 
                                            "European Central Bank", 
                                            "Fed", 
                                            "Bank of Japan", 
                                            "Bank of Canada", 
                                            "Reserve Bank of Australia", 
                                            "Sveriges Riksbank", 
                                            "Swiss National Bank", 
                                            "Central Bank of Iceland", "Norges Bank", 
                                            "National Bank of Denmark",
                                            "Reserve Bank of New Zealand", "Bank of Finland")  ~"Developed", 
                       TRUE ~"Emerging"))%>%
  group_by(deveme, date)%>%
  summarise(index_sent_deveme = mean(sent_bt_all,na.rm = TRUE), 
              index_stance_deveme=mean(stance_bt_all,na.rm = TRUE))%>%
  arrange(date)%>%ungroup()%>% 
  dplyr::rename(
    sent  = index_sent_deveme,
    stance = index_stance_deveme
  ) %>% 
  pivot_wider(
    id_cols   = date,
    names_from  = deveme,              # "Developed", "Emerging"
    values_from = c(sent, stance),
    names_glue  = "{.value}_{deveme}",  # sent_Developed, stance_Developed, etc.
    values_fill = list(sent = 0, stance = 0)
  )


global_daily_decomposed <- bankday_all %>%
  mutate(CB = case_when(Central_Bank %in% c(
    "European Central Bank") ~ "ECB", 
    Central_Bank =="Fed" ~ "Fed",
    TRUE ~ "Other"))%>%
  group_by(CB, date)%>%
  summarise(index_sent_cb = mean(sent_bt_all,na.rm = TRUE), 
              index_stance_cb=mean(stance_bt_all,na.rm = TRUE))%>%
  arrange(date)%>%ungroup()%>% 
  dplyr::rename(
    sent  = index_sent_cb,
    stance = index_stance_cb
  ) %>% 
  pivot_wider(
    id_cols   = date,
    names_from  = CB,              # "Developed", "Emerging"
    values_from = c(sent, stance),
    names_glue  = "{.value}_{CB}",  # sent_Developed, stance_Developed, etc.
    values_fill = list(sent = 0, stance = 0)
  )


global_daily_fedecb <- bankday_all %>%
  mutate(CB = case_when(Central_Bank %in% c(
    "European Central Bank", "Fed") ~ "ECBFED", 
    TRUE ~ "nonECBFED"))%>%
  group_by(CB, date)%>%
  summarise(index_sent_cb = mean(sent_bt_all,na.rm = TRUE), 
              index_stance_cb=mean(stance_bt_all,na.rm = TRUE))%>%
  arrange(date)%>%ungroup()%>% 
  dplyr::rename(
    sent  = index_sent_cb,
    stance = index_stance_cb
  ) %>% 
  pivot_wider(
    id_cols   = date,
    names_from  = CB,              # "Developed", "Emerging"
    values_from = c(sent, stance),
    names_glue  = "{.value}_{CB}",  # sent_Developed, stance_Developed, etc.
    values_fill = list(sent = 0, stance = 0)
  )

deveme_cb <- global_daily_deveme %>%
  left_join(global_daily_decomposed, by="date")%>%
  left_join(global_daily_fedecb, by="date")

Dynamics of speeches sentiments

weekly_sentiment_plot <- cbdc_idx_weekly_labels%>%ggplot(aes(x=date_week, y=index_sent_global))+
  geom_point()+geom_line(lwd=.5, lty=2)+labs(x="", y="Weekly Sentiment")

daily_sentiment_plot <- cbdc_idx_daily_labels%>%ggplot(aes(x=date, y=index_sent_global))+
  geom_point(size=1)+geom_line(lwd=.5, lty=2)+labs(x="", y="Daily Sentiment")

daily_sentiment_plot/weekly_sentiment_plot 

Statistics of weekly speeches

global_daily %>%
  left_join(type_daily_sent_wide,   by = "date") %>%
  left_join(type_daily_stance_wide, by = "date") %>%
  left_join(deveme_cb,              by = "date") %>%
  arrange(date) %>%
  mutate(week = floor_date(date, unit = "week")) %>%
  group_by(week) %>%
  summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)), .groups = "drop") %>%
  select(-speechday)%>%
  tbl_summary(
    type = list(
      all_continuous() ~ "continuous2",  # tous les continus
      week ~ "continuous"               # sauf week
    ),
    statistic = list(
      all_continuous() ~ c("{mean} ({sd})"),
      week ~ "{min}, {max}"             # seulement min/max pour week
    ),
    label = week ~ "Week (min, max)"
  )%>%
  modify_footnote(everything() ~ NA_character_)

Characteristic	N = 295
Week (min, max)	2016-02-28, 2025-06-15
index_sent_global
Mean (SD)	0.33 (0.37)
index_stance_global
Mean (SD)	0.50 (0.38)
stance_auer
Mean (SD)	0.45 (0.63)
Unknown	99
sent_General.Unspecified
Mean (SD)	0.23 (0.38)
Unknown	26
sent_Retail.CBDC
Mean (SD)	0.30 (0.40)
Unknown	73
sent_Wholesale.CBDC
Mean (SD)	0.69 (0.40)
Unknown	153
stance_General.Unspecified
Mean (SD)	0.44 (0.40)
Unknown	26
stance_Retail.CBDC
Mean (SD)	0.44 (0.45)
Unknown	73
stance_Wholesale.CBDC
Mean (SD)	0.78 (0.36)
Unknown	153
sent_Developed
Mean (SD)	0.20 (0.30)
sent_Emerging
Mean (SD)	0.16 (0.30)
stance_Developed
Mean (SD)	0.33 (0.34)
stance_Emerging
Mean (SD)	0.21 (0.34)
sent_Other
Mean (SD)	0.19 (0.31)
sent_ECB
Mean (SD)	0.18 (0.29)
sent_Fed
Mean (SD)	-0.0032 (0.0580)
stance_Other
Mean (SD)	0.28 (0.35)
stance_ECB
Mean (SD)	0.27 (0.33)
stance_Fed
Mean (SD)	0.011 (0.083)
sent_nonECBFED
Mean (SD)	0.19 (0.31)
sent_ECBFED
Mean (SD)	0.17 (0.30)
stance_nonECBFED
Mean (SD)	0.28 (0.35)
stance_ECBFED
Mean (SD)	0.27 (0.33)

Stablecoins supply data

Data import

Statistics

panel_artemis_trimmed %>%
  select(Date, supply, stablecoin)%>%
  tbl_summary(by=stablecoin,
    type = list(
      all_continuous() ~ "continuous2",  # tous les continus
      Date ~ "continuous"               # sauf week
    ),
    statistic = list(
      all_continuous() ~ c("{mean} ({sd})"),
      Date ~ "{min}, {max}"             # seulement min/max pour week
    ),
    label = Date ~ "Date (min, max)"
  )%>%
  modify_footnote(everything() ~ NA_character_)

Characteristic	AYSD N = 391	BUSD N = 340	cEUR N = 1,401	cUSD N = 47	DAI N = 1,944	EURC N = 1,069	FDUSD N = 727	PYUSD N = 694	USD0 N = 372	USDC N = 2,296	USDe N = 588	USDGLO N = 546	USDP N = 2,310	USDS N = 321	USDT N = 2,825	USDY N = 584
Date (min, max)	2024-08-05, 2025-10-21	2024-08-31, 2025-10-21	2021-07-11, 2025-10-21	2025-08-29, 2025-10-21	2019-11-23, 2025-10-21	2022-07-09, 2025-10-21	2023-08-05, 2025-10-21	2023-09-09, 2025-10-21	2024-08-28, 2025-10-21	2018-10-17, 2025-10-21	2024-01-05, 2025-10-19	2024-02-01, 2025-10-21	2018-10-10, 2025-10-21	2024-10-26, 2025-10-21	2015-12-08, 2025-10-21	2024-01-05, 2025-10-21
supply
Mean (SD)	70,719,608 (68,990,092)	337,130,067 (25,003,950)	18,296,966 (13,327,185)	146,370,152 (69,792,967)	4,448,341,212 (2,356,732,077)	84,686,832 (66,646,634)	1,868,248,233 (913,223,249)	626,776,286 (484,825,385)	671,778,959 (341,271,342)	28,755,546,082 (21,595,129,898)	4,822,102,281 (3,431,221,409)	3,093,883 (644,218)	463,487,550 (374,289,377)	7,149,699,496 (1,069,000,697)	57,454,318,058 (52,167,651,561)	374,343,880 (210,577,377)

Final Database

Daily

data_artemis <-panel_artemis_trimmed%>%
  left_join(cbdc_idx_daily_labels, by=c("Date"="date"))%>%
  mutate(quarteryear = paste0(quarter(Date), year(Date) ))%>%
  left_join(GPR, by=c("Date"="date"))%>%
  left_join(vix_data, by=c("Date"="date"))%>%
  arrange(stablecoin, Date)%>%
  mutate(FTX = ifelse(Date > as.Date("2022-11-01"),1,0 ))%>%
  mutate(Luna = ifelse(Date > as.Date("2022-05-01"),1,0 ))%>%
  mutate(pegType = case_when(
    stablecoin == "USDT"~ "USD",
    stablecoin == "USDP"~ "USD",
    stablecoin == "USDC"~ "USD",
    stablecoin == "DAI"~ "USD",
    stablecoin == "cEUR"~ "EUR",
    stablecoin == "EURC"~ "EUR",
    stablecoin == "FDUSD"~ "USD",
    stablecoin == "PYUSD"~ "USD",
    stablecoin == "USDY"~ "USD",
    stablecoin == "USDe"~ "USD",
    stablecoin == "USDGLO"~ "USD",
    stablecoin == "AYSD"~ "USD",
    stablecoin == "BUSD"~ "USD",
    stablecoin == "USD0"~ "USD",
    stablecoin == "USDS"~ "USD",
    stablecoin == "cUSD"~ "USD"
    ))%>%
  mutate(pegMechanism = case_when(
    stablecoin == "USDT"~ "fiat",
    stablecoin == "USDP"~ "fiat",
    stablecoin == "USDC"~ "fiat",
    stablecoin == "DAI"~ "crypto",
    stablecoin == "cEUR"~ "algo",
    stablecoin == "EURC"~ "fiat",
    stablecoin == "FDUSD"~ "fiat",
    stablecoin == "PYUSD"~ "fiat",
    stablecoin == "USDY"~ "fiat",
    stablecoin == "USDe"~ "fiat",
    stablecoin == "USDGLO"~ "fiat",
    stablecoin == "AYSD"~ "fiat",
    stablecoin == "BUSD"~ "fiat",
    stablecoin == "USD0"~ "fiat",
    stablecoin == "USDS"~ "fiat",
    stablecoin == "cUSD"~ "fiat"
    
  ))%>%
  #mutate(across(-Date, ~ tidyr::replace_na(.x, 0)))%>% # remove this to replace 0 with NA when there's no speech
  rename_with(~ gsub("\\.", "_", .x))

Weekly

data_artemis_weekly <-panel_artemis_trimmed_weekly%>%
  left_join(cbdc_idx_weekly_labels, by=c("week"="date_week"))%>%
  mutate(quarteryear = paste0(quarter(week), year(week) ))%>%
  left_join(vix_data_weekly, by="week")%>%
  left_join(GPR_data_weekly, by="week")%>%
  arrange(stablecoin, week)%>%
  dplyr::select(stablecoin, week,everything())%>%
  #na.omit()%>%
  mutate(FTX = ifelse(week > as.Date("2022-11-01"),1,0 ))%>%
  mutate(Luna = ifelse(week > as.Date("2022-05-01"),1,0 ))%>%
  arrange(stablecoin, week)%>%
  mutate(across(-stablecoin, ~ tidyr::replace_na(.x, 0)))%>%# remove this to replace 0 with NA when there's no speech
  rename_with(~ gsub("\\.", "_", .x))

full_weeks <- seq(
  from = min(data_artemis_weekly$week, na.rm = TRUE),
  to   = max(data_artemis_weekly$week, na.rm = TRUE),
  by   = "week"
)

# Panel équilibré stablecoin × week
panel_balanced <- expand_grid(
  stablecoin = unique(data_artemis_weekly$stablecoin),
  week       = full_weeks
)

# On rattache toutes les variables existantes
data_artemis_balanced <- panel_balanced %>%
  left_join(data_artemis_weekly, by = c("stablecoin", "week"))%>%
  mutate(index_sent_global = ifelse(index_sent_global == "NaN", NA, index_sent_global))%>%
  mutate(week1 = floor_date(week, "week", week_start = 1))%>%
  mutate(
    iso_year = isoyear(week1),
    iso_week = isoweek(week1),
    year_week = sprintf("%dw%02d", iso_year, iso_week)  # ex : "2023w01"
  )%>%
  select(stablecoin, year_week, everything())%>% select(-c(week1, iso_year, iso_week))%>%
  mutate(pegType = case_when(
    stablecoin == "USDT"~ "USD",
    stablecoin == "USDP"~ "USD",
    stablecoin == "USDC"~ "USD",
    stablecoin == "DAI"~ "USD",
    stablecoin == "cEUR"~ "EUR",
    stablecoin == "EURC"~ "EUR",
    stablecoin == "FDUSD"~ "USD",
    stablecoin == "PYUSD"~ "USD",
    stablecoin == "USDY"~ "USD",
    stablecoin == "USDe"~ "USD",
    stablecoin == "USDGLO"~ "USD",
    stablecoin == "AYSD"~ "USD",
    stablecoin == "BUSD"~ "USD",
    stablecoin == "USD0"~ "USD",
    stablecoin == "USDS"~ "USD",
    stablecoin == "cUSD"~ "USD"
    ))%>%
  mutate(pegMechanism = case_when(
    stablecoin == "USDT"~ "fiat",
    stablecoin == "USDP"~ "fiat",
    stablecoin == "USDC"~ "fiat",
    stablecoin == "DAI"~ "crypto",
    stablecoin == "cEUR"~ "algo",
    stablecoin == "EURC"~ "fiat",
    stablecoin == "FDUSD"~ "fiat",
    stablecoin == "PYUSD"~ "fiat",
    stablecoin == "USDY"~ "fiat",
    stablecoin == "USDe"~ "fiat",
    stablecoin == "USDGLO"~ "fiat",
    stablecoin == "AYSD"~ "fiat",
    stablecoin == "BUSD"~ "fiat",
    stablecoin == "USD0"~ "fiat",
    stablecoin == "USDS"~ "fiat",
    stablecoin == "cUSD"~ "fiat"
    
  ))

library(haven)
#haven::write_dta(data_artemis, "C:/users/fkraus/Desktop/Articles/effects of CB speeches on stablecoins/2025/data_artemis.dta")
haven::write_dta(data_artemis_balanced, "C:/users/fkraus/Desktop/Articles/effects of CB speeches on stablecoins/2025/data_artemis_weekly.dta")

data_artemis_balanced %>%
  count(stablecoin, year_week) %>%
  filter(n > 1)

Local Projections

\[ Y_{i,t+h}-Y_{i,t-1}= \alpha_i+ \alpha_q + \beta^h \Delta X_t + \gamma Z_t + \mu_{i,t} \] Where \(Y\) is the log-difference of stablecoin supply, \(\alpha_i\) is an individual FE, and \(X_t\) is our index of speech CBDC-related sentiment/stance. \(Z_t\) are control variables representing the VIX and the GPRD and their lags, and dummies representing Terra-Luna and FTX crashes. We include individual (\(\alpha_i\)) and quarter-year (\(\alpha_q\)) fixed-effects.
Note : we take the 90% interval confidences for all estimations. Standard errors are heteroskedasticity robusts (clusterized by stablecoin). Data are in weekly frequencies.

Baseline results

IRFs Effect of sentiment/stance shocks on the log-difference supply (from Artemis.xyz).

Developed Versus Emerging Central Banks

ECB, Fed and Other

Effect on fiat currency

manual local proj with fixest

To verify the robustness of our results obtained with lpirfs R package, we run the local-projections by hand with fixest with the exact same model. This way, we can clusterize our results by stablecoin and week :

h <- 15

lp_man_auer <- feols(
  f(dlog_supply, 0:h)- l(dlog_supply,1) ~ d(stance_auer,1) +  l(GPRD,0:1)+l(VIX_close, 0:1)+Luna+FTX | stablecoin,
  data = data_artemis_weekly, panel.id=c("stablecoin", "week")
)

lp_man_globsent <- feols(
  f(dlog_supply, 0:h)- l(dlog_supply,1) ~ d(index_sent_global,1) +  l(GPRD,0:1)+l(VIX_close, 0:1)+Luna+FTX | stablecoin,
  data = data_artemis_weekly, panel.id=c("stablecoin", "week")
)

lp_man_globstance <- feols(
  f(dlog_supply, 0:h)- l(dlog_supply,1) ~ d(index_stance_global,1) +  l(GPRD,0:1)+l(VIX_close, 0:1)+Luna+FTX | stablecoin,
  data = data_artemis_weekly, panel.id=c("stablecoin", "week")
)

lp_man_gensent <- feols(
  f(dlog_supply, 0:h)- l(dlog_supply,1) ~ d(sent_General_Unspecified,1) +  l(GPRD,0:1)+l(VIX_close, 0:1)+Luna+FTX | stablecoin,
  data = data_artemis_weekly, panel.id=c("stablecoin", "week")
)

lp_man_genstance <- feols(
  f(dlog_supply, 0:h)- l(dlog_supply,1) ~ d(stance_General_Unspecified,1) +  l(GPRD,0:1)+l(VIX_close, 0:1)+Luna+FTX | stablecoin,
  data = data_artemis_weekly, panel.id=c("stablecoin", "week")
)

lp_man_retsent <- feols(
  f(dlog_supply, 0:h)- l(dlog_supply,1) ~ d(sent_Retail_CBDC,1) +  l(GPRD,0:1)+l(VIX_close, 0:1)+Luna+FTX | stablecoin,
  data = data_artemis_weekly, panel.id=c("stablecoin", "week")
)

lp_man_retstance <- feols(
  f(dlog_supply, 0:h)- l(dlog_supply,1) ~ d(stance_Retail_CBDC,1) +  l(GPRD,0:1)+l(VIX_close, 0:1)+Luna+FTX | stablecoin,
  data = data_artemis_weekly, panel.id=c("stablecoin", "week")
)


lp_man_who_sent <- feols(
  f(dlog_supply, 0:h)- l(dlog_supply,1) ~ d(sent_Wholesale_CBDC,1) +  l(GPRD,0:1)+l(VIX_close, 0:1)+Luna+FTX | stablecoin,
  data = data_artemis_weekly, panel.id=c("stablecoin", "week")
)

lp_man_who_stance <- feols(
  f(dlog_supply, 0:h)- l(dlog_supply,1) ~ d(stance_Wholesale_CBDC,1) +  l(GPRD,0:1)+l(VIX_close, 0:1)+Luna+FTX | stablecoin,
  data = data_artemis_weekly, panel.id=c("stablecoin", "week")
)

#etable(lp_man_who_sent, vcov = ~stablecoin+week)
#etable(lp_man_who_stance, vcov = ~stablecoin+week)

require(broom)
shock_var <- c("d(stance_auer, 1)", "d(index_stance_global, 1)", "d(index_sent_global, 1)", "d(sent_Retail_CBDC, 1)", "d(sent_General_Unspecified, 1)" , "d(sent_Wholesale_CBDC, 1)", "d(stance_Wholesale_CBDC, 1)", "d(stance_Retail_CBDC, 1)" , "d(stance_General_Unspecified, 1)"               )

irf_df_auer <- map2_dfr(
  lp_man_auer,                  # chaque sous-modèle
  0:h,                      # horizons correspondants
  ~ {
    # tidy avec IC robustes HC1
    tt <- tidy(.x, conf.int = TRUE, vcov = ~stablecoin+week)
    
    tt %>%
      filter(term %in% shock_var) %>%
      transmute(
        h   = .y,
        irf = estimate,
        low = conf.low,
        up  = conf.high, 
        term=term
      )
  }
)

irf_df_glob_sent<- map2_dfr(
  lp_man_globsent,                  # chaque sous-modèle
  0:h,                      # horizons correspondants
  ~ {
    # tidy avec IC robustes HC1
    tt <- tidy(.x, conf.int = TRUE, vcov = ~stablecoin+week)
    
    tt %>%
      filter(term %in% shock_var) %>%
      transmute(
        h   = .y,
        irf = estimate,
        low = conf.low,
        up  = conf.high, 
        term=term
      )
  }
)
irf_df_glob_stance<- map2_dfr(
  lp_man_globstance,                  # chaque sous-modèle
  0:h,                      # horizons correspondants
  ~ {
    # tidy avec IC robustes HC1
    tt <- tidy(.x, conf.int = TRUE, vcov = ~stablecoin+week)
    
    tt %>%
      filter(term %in% shock_var) %>%
      transmute(
        h   = .y,
        irf = estimate,
        low = conf.low,
        up  = conf.high, 
        term=term
      )
  }
)

irf_df_gen_sent<- map2_dfr(
  lp_man_gensent,                  # chaque sous-modèle
  0:h,                      # horizons correspondants
  ~ {
    # tidy avec IC robustes HC1
    tt <- tidy(.x, conf.int = TRUE, vcov = ~stablecoin+week)
    
    tt %>%
      filter(term %in% shock_var) %>%
      transmute(
        h   = .y,
        irf = estimate,
        low = conf.low,
        up  = conf.high, 
        term=term
      )
  }
)
irf_df_gen_stance<- map2_dfr(
  lp_man_genstance,                  # chaque sous-modèle
  0:h,                      # horizons correspondants
  ~ {
    # tidy avec IC robustes HC1
    tt <- tidy(.x, conf.int = TRUE, vcov = ~stablecoin+week)
    
    tt %>%
      filter(term %in% shock_var) %>%
      transmute(
        h   = .y,
        irf = estimate,
        low = conf.low,
        up  = conf.high, 
        term=term
      )
  }
)

irf_df_ret_sent<- map2_dfr(
  lp_man_retsent,                  # chaque sous-modèle
  0:h,                      # horizons correspondants
  ~ {
    # tidy avec IC robustes HC1
    tt <- tidy(.x, conf.int = TRUE, vcov = ~stablecoin+week)
    
    tt %>%
      filter(term %in% shock_var) %>%
      transmute(
        h   = .y,
        irf = estimate,
        low = conf.low,
        up  = conf.high, 
        term=term
      )
  }
)
irf_df_ret_stance<- map2_dfr(
  lp_man_retstance,                  # chaque sous-modèle
  0:h,                      # horizons correspondants
  ~ {
    # tidy avec IC robustes HC1
    tt <- tidy(.x, conf.int = TRUE, vcov = ~stablecoin+week)
    
    tt %>%
      filter(term %in% shock_var) %>%
      transmute(
        h   = .y,
        irf = estimate,
        low = conf.low,
        up  = conf.high, 
        term=term
      )
  }
)



irf_df_who_sent <- map2_dfr(
  lp_man_who_sent,                  # chaque sous-modèle
  0:h,                      # horizons correspondants
  ~ {
    # tidy avec IC robustes HC1
    tt <- tidy(.x, conf.int = TRUE, vcov = ~stablecoin+week)
    
    tt %>%
      filter(term %in% shock_var) %>%
      transmute(
        h   = .y,
        irf = estimate,
        low = conf.low,
        up  = conf.high, 
        term=term
      )
  }
)

irf_df_who_stance <- map2_dfr(
  lp_man_who_stance,                  # chaque sous-modèle
  0:h,                      # horizons correspondants
  ~ {
    # tidy avec IC robustes HC1
    tt <- tidy(.x, conf.int = TRUE, vcov = ~stablecoin+week)
    
    tt %>%
      filter(term %in% shock_var) %>%
      transmute(
        h   = .y,
        irf = estimate,
        low = conf.low,
        up  = conf.high, 
        term=term
      )
  }
)

auer_2 <- irf_df_auer %>%
  mutate(type="Auer")

global_2<- irf_df_glob_sent %>% rbind(irf_df_glob_stance)%>%
  mutate(type=case_when(
    term == "d(index_sent_global, 1)" ~ "Sentiment", 
    
    term=="d(index_stance_global, 1)" ~"Stance"
  ))

general_2<- irf_df_gen_sent %>% rbind(irf_df_gen_stance)%>%
  mutate(type=case_when(
    term == "d(sent_General_Unspecified, 1)" ~ "Sentiment", 
    
    term=="d(stance_General_Unspecified, 1)" ~"Stance"
  ))

retail_2<- irf_df_ret_sent %>% rbind(irf_df_ret_stance)%>%
  mutate(type=case_when(
    term == "d(sent_Retail_CBDC, 1)" ~ "Sentiment", 
    
    term=="d(stance_Retail_CBDC, 1)" ~"Stance"
  ))

wholesale_2<- irf_df_who_sent %>% rbind(irf_df_who_stance)%>%
  mutate(type=case_when(
    term == "d(sent_Wholesale_CBDC, 1)" ~ "Sentiment", 
    
    term=="d(stance_Wholesale_CBDC, 1)" ~"Stance"
  ))

We then plot the IRFs of the models :

And those IRFs are perfectly consistent with baseline estimations.