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.

Key information about Australia S&P/ASX 200

• Australia S&P/ASX 200 closed at 8,172.4 points in Feb 2025, compared with 8,532.3 points at the previous month end
• Australia Equity Market Index: Month End: ASX: S&P/ASX 200 data is updated monthly, available from May 1992 to Feb 2025, with an average number of 4,604.3 points
• The data reached an all-time high of 8,532.3 points in Jan 2025 and a record low of 1,428.8 points in Oct 1992

The S&P/ASX 200 Index (XJO) is recognised as the investable benchmark for the Australian equity market, it addresses the needs of investment managers to benchmark against a portfolio characterised by sufficient size and liquidity. The S&P/ASX 200 is comprised of the S&P/ASX 100 plus an additional 100 stocks. It forms the basis for the S&P/ASX 200 Index Future and Options and the SPDR S&P/ASX 200 Exchange Traded Fund (ETF)

Australia's main stock market index, the ASX200, rose to 8788 points on September 26, 2025, gaining 0.17% from the previous session. Over the past month, the index has declined 1.93%, though it remains 7.01% 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.

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

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

Australia ASX Index: S&P/ASX 300 data was reported at 8,060.900 Point in Apr 2025. This records an increase from the previous number of 7,782.100 Point for Mar 2025. Australia ASX Index: S&P/ASX 300 data is updated monthly, averaging 4,617.150 Point from May 1992 (Median) to Apr 2025, with 396 observations. The data reached an all-time high of 8,460.400 Point in Jan 2025 and a record low of 1,419.300 Point in Oct 1992. Australia ASX Index: S&P/ASX 300 data remains active status in CEIC and is reported by Australian Securities Exchange. The data is categorized under Global Database’s Australia – Table AU.Z001: Australian Stock Exchange: Indices. The S&P/ASX 300 Index (XKO) provides additional depth and coverage to the S&P/ASX 200 whilst maintaining strict liquidity guidelines. It provides up to an additional 100 small-cap stocks to the S&P/ASX 200. The S&P/ASX 300 Index forms the basis for the Vanuard Australian Shares Index 300 Exchange Traded Fund (ETF).

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.

S&P/ASX 200 Index Target Price Prediction

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

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.

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.

S&P/ASX 200 index forecast: cautious optimism

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

Prices for Australia Stock Market Index (AU200) including live quotes, historical charts and news. Australia Stock Market Index (AU200) was last updated by Trading Economics this September 28 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.

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

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

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.

A dataset of key technical indicators for S&P/ASX 200, including RSI and MACD, used for technical analysis.

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.

What are the most successful trading algorithms? (S&P/ASX 200 Index Stock Forecast)

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

Prices for Australia Stock Market Index (All Ordinaries Composite) including live quotes, historical charts and news. Australia Stock Market Index (All Ordinaries Composite) was last updated by Trading Economics this September 27 of 2025.

A dataset of key technical indicators for S&P/ASX 200 Industrials, including RSI and MACD, used for technical analysis.