Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
The online banking penetration rate in Australia was forecast to continuously increase between 2024 and 2029 by in total 4.1 percentage points. After the fifteenth consecutive increasing year, the online banking penetration is estimated to reach 71.28 percent and therefore a new peak in 2029. Notably, the online banking penetration rate of was continuously increasing over the past years.Shown is the estimated percentage of the total population in a given region or country, which makes use of online banking.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Bank Bill Swap Rate in Australia remained unchanged at 4.11 percent on Wednesday March 26. This dataset includes a chart with historical data for Australia Bank Bill Swap Rate.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Inflation Expectations in Australia decreased to 3.60 percent in March from 4.60 percent in February of 2025. This dataset provides - Australia Inflation Expectations- actual values, historical data, forecast, chart, statistics, economic calendar and news.
Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
License information was derived automatically
The protected area estate increased by half a million hectares between 2015–2017 and now covers approximately 8.2% of Queensland The protected area estate increased by half a million hectares between 2015–2017 and now covers approximately 8.2% of Queensland
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Australia Lending Rate: Personal Loans: Unsecured Term Loans: Fixed data was reported at 12.464 % pa in Feb 2020. This records an increase from the previous number of 12.104 % pa for Jan 2020. Australia Lending Rate: Personal Loans: Unsecured Term Loans: Fixed data is updated monthly, averaging 13.500 % pa from Dec 1988 (Median) to Feb 2020, with 375 observations. The data reached an all-time high of 20.700 % pa in Feb 1990 and a record low of 10.900 % pa in May 1999. Australia Lending Rate: Personal Loans: Unsecured Term Loans: Fixed data remains active status in CEIC and is reported by Reserve Bank of Australia. The data is categorized under Global Database’s Australia – Table AU.M004: Lending Rate.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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The protected area estate increased by more than 40,971 hectares between 1 January 2018 and 30 June 2020, and now covers about 8.24% of Queensland.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The protected area estate increased by 3 million hectares between 2011-2015 and now covers 7.9% of Queensland.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Annual Growth Rate of Real GDP per capita
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset provides values for INFLATION RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Australia Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data was reported at 7.115 % in 2022. This records an increase from the previous number of 2.801 % for 2021. Australia Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data is updated yearly, averaging 2.717 % from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 7.115 % in 2022 and a record low of -0.605 % in 2016. Australia Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Inflation. Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years.;World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.;;
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.