Core consumer prices in Australia increased 2.90 percent in March of 2025 over the same month in the previous year. This dataset provides - Australia Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Australia RBA Forecast: Underlying Inflation: High data was reported at 2.700 % in Jun 2027. This stayed constant from the previous number of 2.700 % for Dec 2026. Australia RBA Forecast: Underlying Inflation: High data is updated semiannually, averaging 2.500 % from Jun 2007 (Median) to Jun 2027, with 41 observations. The data reached an all-time high of 6.500 % in Dec 2022 and a record low of 1.000 % in Dec 2020. Australia RBA Forecast: Underlying Inflation: High 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.I004: Consumer Price Index: Forecast.

Inflation Rate in Australia remained unchanged at 2.40 percent in the first quarter of 2025 from 2.40 percent in the fourth quarter of 2024. This dataset provides the latest reported value for - Australia Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The statistic shows the inflation rate in Australia from 1987 to 2023, with projections up until 2030. The inflation rate is calculated using the price increase of a defined product basket. This product basket contains products and services, on which the average consumer spends money throughout the year. They include expenses for groceries, clothes, rent, power, telecommunications, recreational activities and raw materials (e.g. gas, oil), as well as federal fees and taxes. In 2023, the average inflation rate in Australia was at about 5.62 percent compared to the previous year. Australia's economy Australia has one of the world’s largest economies and is a significant global importer and exporter. It is also labeled as one of the G20 countries, also known as the Group of Twenty, which consists of 20 major economies around the globe. The Australian economy is highly dependent on its mining sector as well as its agricultural sector in order to grow, and it exports the majority of these goods to eastern Asian countries, most prominently China. Large quantities of exports have helped Australia maintain a stable economy and furthered economic expansion, despite being affected by several economic obstacles. Australia’s GDP has seen a significant increase over the past decade, more than doubling its value, and experienced a rather quick recovery from the 2008 financial crisis, which indicates that the country experienced economic growth as well as higher productivity. One of the primary reasons is the further development of the nation’s mining industry coupled with the expansion and success of many Australian mining companies.

Australia RBA Forecast:(CPI) Consumer Price IndexInflation: High data was reported at 2.700 % in Jun 2027. This records a decrease from the previous number of 2.800 % for Dec 2026. Australia RBA Forecast:(CPI) Consumer Price IndexInflation: High data is updated semiannually, averaging 2.500 % from Jun 2007 (Median) to Jun 2027, with 41 observations. The data reached an all-time high of 8.000 % in Dec 2022 and a record low of -1.000 % in Jun 2020. Australia RBA Forecast:(CPI) Consumer Price IndexInflation: High 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.I004: Consumer Price Index: Forecast.

Trimmed Mean CPI QoQ in Australia increased to 0.70 percent in the first quarter of 2025 from 0.50 percent in the fourth quarter of 2024. This dataset includes a chart with historical data for Australia RBA Trimmed Mean CPI QoQ.

The benchmark interest rate in Australia was last recorded at 3.85 percent. This dataset provides - Australia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

RBA Forecast: Underlying Inflation: Low data was reported at 2.000 % in Jun 2019. This records an increase from the previous number of 1.500 % for Dec 2018. RBA Forecast: Underlying Inflation: Low data is updated semiannually, averaging 2.000 % from Jun 2008 (Median) to Jun 2019, with 17 observations. The data reached an all-time high of 2.750 % in Dec 2009 and a record low of 1.000 % in Dec 2016. RBA Forecast: Underlying Inflation: Low 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.I004: Consumer Price Index: Forecast.

Break Even Inflation Rate: 10-Year data was reported at 2.160 % in Mar 2025. This records a decrease from the previous number of 2.320 % for Dec 2024. Break Even Inflation Rate: 10-Year data is updated quarterly, averaging 2.620 % from Dec 1985 (Median) to Mar 2025, with 157 observations. The data reached an all-time high of 9.370 % in Mar 1989 and a record low of 0.660 % in Mar 2020. Break Even Inflation Rate: 10-Year 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.I067: Breakeven Inflation Rate. The Breakeven 10 Year Inflation Rate is measured by the Reserve Bank of Australia using Commonwealth government securities. For data since 1996, the RBA uses interpolation and projection of the yield curve to estimate both nominal and inflation-indexed bond yields with an exact 10-year maturity. Prior to 1996, these adjustments are not made and the nominal and inflation-indexed bond yields used to derive the series may not reflect ideal 10-year maturities. [COVID-19-IMPACT]

On November 8, 2023, the Reserve Bank of Australia (RBA) increased the interest rate by **** percentage points, bringing the interest rate to **** percent. This was the thirteenth interest rate increase by the RBA since November 4, 2020, which saw the interest rate drop to a record *** percent.

Inflation Expectations in Australia increased to 5 percent in June from 4.10 percent in May of 2025. This dataset provides - Australia Inflation Expectations- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Reserve Bank of Australia's (RBA) cash rate target in-part determines interest rates on financial products.

This report analyses the Australian cash rate target. The cash rate is the interest rate that authorised deposit-taking institutions pay or charge for overnight funds. The cash rate target is controlled by the Reserve Bank of Australia (RBA) and is the main monetary policy tool of the RBA in signalling their stance and decision of easing or tightening policy. The RBA board meets on the first Tuesday of every month and decides whether to change the cash rate and by how much. Changes to the cash rate tend to be made in 25 basis point increments. The main objectives for the bank when adjusting the rate are to keep inflation within the target of 2-3%, maintain full employment and ensure the economic prosperity and welfare of Australians. The data for this report is sourced from the RBA and is presented as the average cash rate over each financial year.

Weighted Median CPI YoY in Australia decreased to 3 percent in the first quarter of 2025 from 3.50 percent in the fourth quarter of 2024. This dataset includes a chart with historical data for Australia RBA Weighted Mean CPI YoY.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is RBA Stock Expected to Go Up?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The real interest rate in Australia decreased by *** percentage points (-***** percent) in 2019 in comparison to the previous year. This was a significant decrease in the real interest rate. Real interest rate is the adjusted lending interest rate to remove the effects of inflation, as measured by the GDP deflator (implicit price deflator).Find more statistics on other topics about Australia with key insights such as deposit interest rate, domestic credit to the private sector as a share of GDP, and market capitalization of listed domestic companies as a share of GDP.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

RBA:TSX Ritchie Bros. Auctioneers Incorporated

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This report analyses the exchange rate of the Australian dollar (AU$) in terms of the US dollar (US$). The exchange rate is determined by the supply and demand for each currency in the pair. The major drivers of the supply and demand for currencies are: interest rates; GDP growth; inflation; current account positions; equity flows; and the demand and price of commodities. The data for this report is sourced from the Reserve Bank of Australia (RBA) and is measured in US dollars. The RBA records the average monthly exchange rate on the last trading day of each month, and annual rates are calculated as the average of monthly rates over the financial year.

The Credit Card Issuance industry has contracted as the number of cards issued and balances accruing interest have fallen. Issuers have faced significant competition from other forms of payment like debit cards and BNPL services. The monthly value of debit card transactions has continued to surpass the monthly value of credit card transactions thanks to initiatives like the Reserve Bank of Australia's (RBA) least-cost routing initiative. BNPL services have also gained popularity with younger consumers who constitute a significant market for online sellers. That's why revenue is set to weaken by an annualised 5.3% over the five years through 2024-25, to $7.6 billion. To compete with sophisticated competition, credit card issuers have beefed up their reward and referral programs and integrated online payment, service and customer acquisition platforms into their operations. The Big Four banks dominate the industry and NAB's acquisition of Citigroup's Australian consumer banking business has expanded its collective market share. Economic conditions tied to inflationary pressures have ravaged consumer sentiment and appetites for spending through credit. Some customers have opted to pay down debt instead and have avoided taking on more. A sharp climb in interest rates over the past few years has compounded this dynamic, which is set to constrain industry performance in 2024-25, with revenue declining by an anticipated 0.9%. Credit card issuers' performance will improve over the coming years as economic conditions recover. Credit card issuance revenue is projected to expand at an annualised 2.0% through the end of 2029-30, to total $8.4 billion. The RBA is forecast to slash the cash rate once inflation falls within the central banks' target band, lifting credit card issuer profit margins as funding costs drop. Alternative payment methods, like BNPL services, debit transactions and other fintech solutions, are on track to sap away demand for credit cards. However, easing inflationary pressures and lower interest rates over the medium term are set to spur household consumption expenditure and credit card use. In response to the fierce competition, issuers will emphasise innovation and enhance their rewards and points systems to entice consumers.

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.