Hereby I am sharing the data used in the paper: "The words have power: the impact of news on exchange rates". The dataset includes: Taylor Rule Fundamentals: - inflation, - industrial production index (as a high-frequency proxy of GDP), - money market rate from 2000 until 2018. Textual information: - Entropies of news items about the U.S. Dollar from Nexis-Uni database. This is how we get the textual data from Nexis-Uni database: We enter “U.S. Dollar” as a keyword in searching for the news, which gives over 15 Million non-duplicate news. Next, we clean data news and select the relevant news items as follows. We select news about U.S. Dollar with the following criteria: (i) the U.S. Dollar appears in the title of news items, (ii) U.S. Dollar is repeated several times in the news, (iii) the first paragraph of news contains the word “U.S. Dollar”, (iv) U.S. Dollar is the subject of news items which are automatically selected by Nexis-Uni database. - economic policy uncertainty index from https://www.policyuncertainty.com/index.html

I am hereby sharing the dataset used in the paper titled 'Beyond Tradition: A Hybrid Model Unveiling News Impact on Exchange Rates'. The dataset comprises the following components: Taylor Rule Fundamentals: - Inflation - Industrial production index (as a high-frequency proxy of GDP) - Money market rate spanning from 2000 to 2018. Textual Information: - Economic Policy Uncertainty Index from https://www.policyuncertainty.com/index.html (as of November 9, 2023). - Time series of entropies calculated for U.S. Dollar-related news topics extracted from the Nexis-Uni database. Note: To acquire the textual data from the Nexis-Uni database, we conducted the following steps: We entered "U.S. Dollar" as a keyword in the search for news, resulting in over 15 million non-duplicate news items. Subsequently, we cleaned the news data and selected relevant news items using the following criteria: (i) The U.S. Dollar appears in the title of news items, (ii) The term "U.S. Dollar" is repeated several times in the news, (iii) The first paragraph of the news contains the word "U.S. Dollar", (iv) The news items are automatically selected by the Nexis-Uni database with the U.S. Dollar as the subject. Subsequently, we identified the topics related to the US Dollar from the news using LDA and calculated the Shannon entropies over time for each topic.

South African monthly The FTSE/JSE All Share Index data was procured from Bloomberg and the nominal effective exchange rate (NEER) from South African Reserve Bank (SARB) database, where the data has been seasonally adjusted specifying 2015 as the base year. Volatility measures in these markets are generated through a multivaraite EGARCH model in the WinRATS software. South African monthly consumer price index (CPI) data was procured from the International Monetary Fund’s International Financial Statistics (IFS) database, where the data has been seasonally adjusted, specifying 2010 as the base year. The inflation rate is constructed by taking the year-on-year changes in the monthly CPI figures. Inflation uncertainty was generated through the GARCH model in Eviews software. The following South African macroeconomic variables were procured from the SARB: real industrial production (IP), which is used as a proxy for real GDP, real investment (I), real consumption (C), inflation (CPI), broad money (M3), the 3-month treasury bill rate (TB3) and the policy rate (R), a measure of U.S. EPU developed by Baker et al. (2016) to account for global developments available at http://www.policyuncertainty.com/us_monthly.html.

The EUR/USD exchange rate fell to 1.1735 on July 24, 2025, down 0.33% from the previous session. Over the past month, the Euro US Dollar Exchange Rate - EUR/USD has strengthened 0.44%, and is up by 8.14% over the last 12 months. Euro US Dollar Exchange Rate - EUR/USD - values, historical data, forecasts and news - updated on July of 2025.

This paper investigates both the effects of domestic monetary policy and external shocks on fundamental macroeconomic variables in six fast growing emerging economies: Brazil, Russia, India, China, South Africa and Turkey—denoted hereafter as BRICS_T. The authors adopt a structural VAR model with a block exogeneity procedure to identify domestic monetary policy shocks and external shocks. Their research reveals that a contractionary monetary policy in most countries appreciates the domestic currency, increases interest rates, effectively controls inflation rates and reduces output. They do not find any evidence of the price, output, exchange rates and trade puzzles that are usually found in VAR studies. Their findings imply that the exchange rate is the main transmission mechanism in BRICS_T economies. The authors also find that that there are inverse J-curves in five of the six fast growing emerging economies and there are deviations from UIP (Uncovered Interest Parity) in response to a contractionary monetary policy in those countries. Moreover, world output shocks are not a dominant source of fluctuations in those economies.

This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines


    Forex Pair
    Headline
    Sentiment
    Explanation




    GBPUSD 
    Diminishing bets for a move to 12400 
    Neutral
    Lack of strong sentiment in either direction


    GBPUSD 
    No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
    Positive
    Positive sentiment towards GBPUSD (Cable) in the near term


    GBPUSD 
    When are the UK jobs and how could they affect GBPUSD 
    Neutral
    Poses a question and does not express a clear sentiment


    JPYUSD
    Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
    Positive
    Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply


    USDJPY
    Dollar rebounds despite US data. Yen gains amid lower yields 
    Neutral
    Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other


    USDJPY
    USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
    Negative
    USDJPY is expected to reach a lower value, with the USD losing value against the JPY


    AUDUSD

RBA Governor Lowe’s Testimony High inflation is damaging and corrosive

    Positive
    Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.

Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

This panel dataset contains quarterly series on inflation targets, bands, and track records for 41 inflation targeting countries from 1990 to 2024. Data on inflation targets and bands are collected through each central bank’s historical documents and rules-based track record measures are calculated by the author to assess actual inflation outcomes with respect to the central banks’ stated policy objectives. The dataset supports research work in Zhang (2025), Zhang and Wang (2022), and Zhang (2021). Please cite the papers when using the data.

Z. Zhang, Does inflation targeting track record matter for asset prices? Evidence from stock, bond, and foreign exchange markets, Journal of International Financial Markets, Institutions and Money, Volume 101, 2025, 102141. Z. Zhang, S. Wang, Do actions speak louder than words? Assessing the effects of inflation targeting track records on macroeconomic performance, 2022, IMF Working Papers 2022/227.
Z. Zhang, Stock returns and inflation redux: An explanation from monetary policy in advanced and emerging markets, 2021, IMF Working Papers 2021/219.

Inflation Rate in Japan decreased to 3.30 percent in June from 3.50 percent in May of 2025. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Inflation Rate in Turkey decreased to 35.05 percent in June from 35.41 percent in May of 2025. This dataset provides the latest reported value for - Turkey Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The DXY exchange rate rose to 97.3823 on July 24, 2025, up 0.17% from the previous session. Over the past month, the United States Dollar has weakened 0.30%, and is down by 6.67% over the last 12 months. United States Dollar - values, historical data, forecasts and news - updated on July of 2025.

The dataset contains several macroeconomic time-series regarding the Russian economy. The time-series were collected from the Russian Federal State Statistics Service, the Bank of Russia and Federal Reserve Economic Data. The time-series included in the dataset are: 1. Time: 1-Jan-2005 = 1, every successive step in time represents one quarter 2. Date: Quarterly dates from 1-Jan-2005 to 1-Oct-2021 5. GDP: Quarterly nominal GDP in 2016 prices, excluding seasonal factor (bln RUB) 6. GDPgr: Nominal GDP growth rate (Quarterly, %) 7. M0: Base or high-powered money (bln RUB) 8. M0gr: M0 growth rate (Quarterly, %) 9. BM: M2 measure of money supply (bln RUB) 10. BMgr: M2 growth rate (Quarterly, %) 11. Interest: 90-day interbank rate (APR, %) 12. USDRUB: USD/RUB exchange rate (RUB) 12. EURRUB: EUR/RUB exchange rate (RUB) 13. Unemployment: Unemployment rate (%) 14. PPI: Domestic producer price index (index: 2015=100) 15. PPIgr: Growth rate of producer price index (Quarterly, %) 16. OIL: Spot prices of Brent per barrel (USD) 17. OILgr: Growth rate of Brent prices (Quarterly, %) 18. WAGE: Average monthly nominal wage rate (RUB) 19. WAGEgr: Changes in nominal wage rate (Quarterly, %) 3. CPI: Change in CPI as a ratio (End of quarter to end of previous quarter, %) 4. Inflation: Percentage change in CPI, calculated as Relative CPI - 100 (Quarterly, %)

The data was used to in time-series regression modelling to explain the factors affecting inflation in Russia. Some other modelling ideas for the dataset are: 1. Shift the focus from factor analysis to predicting future inflation 2. Perform factor analyses of other key macroeconomic variables, such as the GDP growth rate, the unemployment rate or the interest rate

Due to the low number of available observations because of quarterly sampling, this dataset is probably better suited to time-series econometric analysis rather than more modern machine learning methods.

The International Macroeconomic Data Set provides data from 1969 through 2030 for real (adjusted for inflation) gross domestic product (GDP), population, real exchange rates, and other variables for the 190 countries and 34 regions that are most important for U.S. agricultural trade. The data presented here are a key component of the USDA Baseline projections process, and can be used as a benchmark for analyzing the impacts of U.S. and global macroeconomic shocks.

Table 1: May 20 to Mar 21. Table 5: Dec 18 to Dec19. Month on Month inflation rates for SADC Member States as measured by National HCPIs

The monthly rate measures the price change between the two latest months. Although up to date, it can be affected by seasonal and other short-term effects

EGPB - An Event-based Gold Price Benchmark Dataset

This benchmark dataset consists of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset.

Key variables & Features include:

• Previous gold prices

• Future gold prices with predictions for one day, one week, and one month

• Oil prices

• Standard & Poor's 500 Index (S&P 500)

• Dow Jones Industrial (DJI)

• US dollar index

• US treasury

• Inflation rate

• Consumer price index (CPI)

• Federal funds rate

• Silver prices

• Copper prices

• Iron prices

• Platinum prices

• Palladium prices

Additionally, the dataset considers global events that may impact gold prices, which were categorized into groups and collected from three distinct sources: the Al-Jazeera website spanning from 2022 to 2019, the Investing website spanning from 2018 to 2016, and the Yahoo Finance website spanning from 2007 to 2001.

These events data were then divided into multiple groups:

• Economic data

• Politics

• logistics

• Oil

• OPEC

• Dollar currency

• Sterling pound currency

• Russian ruble currency

• Yen currency

• Euro currency

• US stocks

• Global stocks

• Inflation

• Job reports

• Unemployment rates

• CPI rate

• Interest rates

• Bonds

These events were encoded using a numeric value, where 0 represented no events, 1 represented low events, 2 represented high events, 3 represented stable events, 4 represented unstable events, and 5 represented events that were observed during the day but had no effect on the dataset.

Cite this dataset: Farah Mansour and Wael Etaiwi, "EGPBD: An Event-based Gold Price Benchmark Dataset," 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Canary Islands, Spain, 2023, pp. 1-7, doi: 10.1109/ICECCME57830.2023.10252987.

@INPROCEEDINGS{10252987, author={Mansour, Farah and Etaiwi, Wael}, booktitle={2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)}, title={EGPBD: An Event-based Gold Price Benchmark Dataset}, year={2023}, volume={}, number={}, pages={1-7}, doi={10.1109/ICECCME57830.2023.10252987}}

This dataset facilitates an analysis of the impact of the recent Israel-Hamas conflict on the stock market performance of U.S. defense companies, as measured by the returns of defense-sector Exchange-Traded Funds (ETFs). The conflict is quantified using variables such as a binary "attack" indicator, casualty counts, and the intensity of Google search activity related to the war. Additionally, the dataset incorporates a comprehensive set of control variables, including interest rates, exchange rates, oil prices, inflation rates, and factors related to the Ukraine conflict, ensuring a robust framework for evaluating the effects of this geopolitical event.

Inflation Rate in Russia decreased to 9.40 percent in June from 9.90 percent in May of 2025. This dataset provides - Russia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This is the latest version of the Global VAR (GVAR) dataset - a global modelling framework for analyzing the international macroeconomic transmission of shocks while accounting for drivers of economic activity, interlinkages and spillovers between different countries, and the effects of unobserved or observed common factors. This dataset includes quarterly macroeconomic variables for 33 economies (log real GDP, y, the rate of inflation, dp, short-term interest rate, r, long-term interest rate, lr, the log deflated exchange rate, ep, and log real equity prices, eq, as well as quarterly data on commodity prices (oil prices, poil, agricultural raw material, pmat, and metals prices, pmetal), from 1979Q2 to 2023Q3. These 33 countries cover more than 90% of world GDP.

It would be appreciated if use of the updated dataset could be acknowledged as: “Mohaddes, K. and M. Raissi (2024). Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2023Q3. University of Cambridge: Judge Business School (mimeo)”.

For more details on Global VAR (GVAR) modelling, see also www.mohaddes.org/gvar

Introduction of the euro in Lithuania. Topics: contact with and use of euro banknotes or coins; use of euro banknotes or coins in the own country or abroad; knowledge test on the euro: equal design of euro banknotes and coins in every country, number of countries that already introduced the euro, possibility of the own country to choose whether to introduce the euro or not, year of introduction of the euro in the own country; self-rated knowledge on the euro; preferred time of information about the introduction of the euro in the own country; trust in information about the introduction provided by: national or regional government or authorities, tax administration, national central bank, European institutions, commercial banks, journalists, trade unions or professional organizations, consumer associations; preferred places of information about the euro and the changeover; most important issues to be covered by information campaigns; significance of selected information campaign actions; assessment of the impact of the introduction of the euro in the countries already using the euro as positive; assessment of the impact of the introduction on the own country and on personal life; approval of introducing the euro in the own country; preferred time for introducing the euro; expected impact of the introduction on the prices in the own country; expected impact of the introduction: easier price comparisons with other countries, easier shopping in other countries, save money by eliminating fees of currency exchange in other countries, more convenient travel in other countries, protection of the own country from the effects of international crises; benefits from the adoption of the euro on the own country: lower interest rates, sounder public finances, improvement of growth and employment, ensuring low inflation rates, reinforcement of the place of Europe in the world, strengthening of European identity; approval of the following statements on the impact of the introduction of the euro: confident to adapt to the replacement of the national currency, afraid of abusive price setting, loss of control over national economic policy, loss of national identity. Demography: age; sex; nationality; age at end of education; occupation; professional position; region; type of community; own a mobile phone and fixed (landline) phone; household composition and household size. Additionally coded was: type of phone line; weighting factor. Einführung des Euro in Litauen. Themen: Kontakt mit und Nutzung von Euro-Banknoten und -Münzen; Nutzung im eigenen Land, im Ausland oder beides; Wissenstest über den Euro: identisches Aussehen von Euro-Banknoten und -Münzen in jedem Land, Anzahl der bereits den Euro nutzenden Länder, Wahlmöglichkeit des eigenen Landes zur Einführung des Euro, Jahr der Einführung im eigenen Land; Selbsteinschätzung der Informiertheit über den Euro; bevorzugter Zeitpunkt für Informationen zur Euro-Einführung im eigenen Land; Vertrauen in Informationen zur Euro-Einführung von: nationaler bzw. regionaler Regierung oder Behörden, Steuerbehörden, nationaler Zentralbank, europäischen Institutionen, Geschäftsbanken, Journalisten, Gewerkschaften oder Berufsorganisationen, Verbraucherschutzorganisationen; bevorzugte Orte für Informationen über den Euro und die Umstellung; wichtigste Inhalte einer Informationskampagne zum Euro; Bedeutung einzelner Aktionen einer Informationskampagne; Einschätzung der Folgen der Einführung in den bereits den Euro nutzenden Ländern als positiv; Einschätzung der Folgen der Einführung für das eigene Land und für den Befragten persönlich; Zustimmung zur Einführung des Euro im eigenen Land; bevorzugter Zeitpunkt für die Einführung des Euro; erwartete Auswirkungen der Einführung auf die Preise im eigenen Land; erwartete Folgen der Einführung: Erleichterung von Preisvergleichen mit anderen Ländern, Erleichterung von Einkäufen in anderen Ländern, Kostensenkung beim Geldumtausch durch Aufheben von Gebühren, bequemeres Reisen in anderen Ländern, Schutz des eigenen Landes vor den Folgen internationaler Krisen; Vorzüge durch die Einführung des Euro für das eigene Land: niedrigere Zinssätze, solidere öffentliche Finanzen, Verbesserung von Wachstum und Beschäftigung, niedrige Inflationsraten, Stärkung der europäischen Identifikation; Einstellung zu folgenden Aussagen zur Euro-Einführung: Überzeugung der persönlichen Gewöhnung an die neue Währung, Besorgnis über missbräuchliche Preisbildung, Verlust der Kontrolle über die nationale Wirtschaftspolitik, Verlust der nationalen Identität. Demographie: Alter; Geschlecht; Staatsangehörigkeit; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Region; Urbanisierungsgrad; Besitz eines Mobiltelefons; Festnetztelefon im Haushalt; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Interviewmodus (Mobiltelefon oder Festnetz); Gewichtungsfaktor.

Inflation Rate in Brazil increased to 5.35 percent in June from 5.32 percent in May of 2025. This dataset provides - Brazil Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The USD/TRY exchange rate rose to 40.4736 on July 24, 2025, up 0.07% from the previous session. Over the past month, the Turkish Lira has weakened 1.90%, and is down by 22.20% over the last 12 months. Turkish Lira - values, historical data, forecasts and news - updated on July of 2025.