The benchmark interest rate in Australia was last recorded at 3.60 percent. This dataset provides - Australia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Will the ASX 200 Index Continue its Ascent?

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 S&P/ASX 200 index, the most prominent index of stocks listed on the Australian Securities Exchange (ASX), lost over one fifth of its value between the end of February and the end of March 2020, owing to the economic impact of the global coronavirus (COVID-19) pandemic. It has since recovered, and surpassed its pre-corona level in April 2021. Despite fluctuations, it reached its highest value in June 2025 at 8542.3 during this period.The S&P/ASX 200 index is considered the benchmark index for the Australian share market and contains the 200 largest companies listed on the ASX.

The S&P/ASX Small Ordinaries index saw a price return of ******** Australian dollars in July 2023. Due to the financial effects of the global coronavirus (COVID-19) pandemic, the price return for the index decreased significantly between the end of February and the end of March 2020.

ASX index performance affected by the coronavirus pandemic

The S&P/ASX Small Ordinaries index is a key benchmark for small-cap Australian companies. The index measures companies included in the S&P/ASX 300 but not in the S&P/ASX 100. In comparison, the S&P/ASX 200 index measures the performance of the *** largest companies listed on the ASX. Due to the financial effects of the global coronavirus pandemic, it lost more than one-fifth of its value between the end of February and the end of March 2020. Since then, it has improved and surpassed its pre-corona level with its value peaking around *** thousand index points in August 2021.

Financial markets in Australia

Financial markets in Australia are an integral part of the country's economy. The Australian Securities Exchange (ASX) is the country's primary stock exchange and, as of December 2022, it had a domestic market capitalization of approximately **** trillion Australian dollars. As of April 2023, the largest company listed on the ASX was BHP Group Limited, with a total market capitalization of over *** billion Australian dollars. The financial sector dominated the list of the largest Australian domestic companies, with **** of the top 10 companies being either retail or investment banking groups. Overall, financial markets in Australia are diverse, and robust, attracting both local and international investors.

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.

ASX 200: Poised for a Record-Breaking Ascent?

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 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.

ASX 200 to See Modest Gains Amid Global Uncertainty, Experts Say.

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

Predictions hold that the S&P/ASX 200 index may fluctuate within a wide range. Bulls foresee a surge driven by positive economic data, strong corporate earnings, and central bank easing. However, bears anticipate downward pressure due to geopolitical uncertainties, inflation concerns, and potential earnings revisions. Risks include economic slowdown, interest rate hikes, and a resurgence of COVID-19 cases, which could push the index lower.

The S&P/ASX Emerging Companies index recorded a price return of ******** Australian dollars in April 2023. The price return for the index decreased significantly between the end of February and the end of March 2020, owing to the economic impact of the global coronavirus (COVID-19) pandemic.

In June 2025, the All Ordinaries comprised of the 500 most important companies listed on the Australian Securities Exchange (ASX) reached its second-highest value throughout the period considered, standing at 8,773. The All Ordinaries index is considered a benchmark index for the Australian share market and includes the value of over 95 percent the the shares listed on the ASX. The other main benchmark index for the Australian economy is the S&P ASX 200, which is comprised of the 200 largest companies listed on the ASX.

Index Time Series for S&P/ASX 200 VIX. The frequency of the observation is daily. Moving average series are also typically included.

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.

ASE Tech's Strong Growth: How High Can It Go? (ASX)

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

Australia's main stock market index, the ASX200, fell to 8818 points on September 15, 2025, losing 0.53% from the previous session. Over the past month, the index has declined 1.58%, though it remains 8.57% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Australia. Australia Stock Market Index - values, historical data, forecasts and news - updated on September of 2025.

As of June 2025, the price return value of the S&P/ASX 200 ESG Index was ****** Australian dollars compared to a base of 100 Australian dollars. At the same time, the S&P/ASX 300 Net Zero 2050 Paris-Aligned ESG Index had a price return of ****** Australian dollars.

Index Time Series for SPDR S&P/ASX 200 Esg ETF. The frequency of the observation is daily. Moving average series are also typically included. NA

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.

ASX 200 Index: Navigating the Storm?

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

Dataset Information

This dataset includes daily price data for various stock indices.

  Instruments Included

ADSMI: United Arab Emirates Stock Market (ADX General) - United Arab Emirates AEX: Netherlands Stock Market (AEX) - Netherlands (NL) AS30: Australian All - Australia (AU) AS51: Australia S&P/ASX 200 Stock Market Index - Australia (AU) AS52: ASX 50 - Australia (AU) ASE: Greece Stock Market (ASE) - Greece (GR) ATX: Austria Stock Market (ATX) - Austria (AT) BEL20:… See the full description on the dataset page: https://huggingface.co/datasets/paperswithbacktest/Indices-Daily-Price.

Key information about Australia Market Capitalization

• Australia Market Capitalization accounted for 1,878.429 USD bn in Feb 2025, compared with a percentage of 1,953.275 USD bn in the previous month
• Australia Market Capitalization is updated monthly, available from Jan 1985 to Feb 2025
• The data reached an all-time high of 2,088.660 USD bn in Sep 2024 and a record low of 46.183 USD bn in Feb 1985

CEIC converts monthly Market Capitalization into USD. Australian Securities Exchange provides Market Capitalization in local currency. The Federal Reserve Board period end market exchange rate is used for currency conversions. Market Capitalization prior to January 2024 includes only domestically listed companies.

Bank Bill Swap Rate in Australia increased to 3.59 percent on Friday September 12 from 3.58 in the previous day. This dataset includes a chart with historical data for Australia Bank Bill Swap Rate.

Index Time Series for Global X S&P/ASX 200 Covered Call Complex ETF. The frequency of the observation is daily. Moving average series are also typically included.

Prices for Australia Stock Market Index (AU50) including live quotes, historical charts and news. Australia Stock Market Index (AU50) was last updated by Trading Economics this September 15 of 2025.