Australia's main stock market index, the ASX200, fell to 8703 points on July 24, 2025, losing 0.39% from the previous session. Over the past month, the index has climbed 1.68% and is up 10.71% compared to the same time last year, 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 July of 2025.

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 January 2025 at 8532.3 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.

Australian Securities Exchange reported 193.88M in Outstanding Shares in January of 2025. Data for Australian Securities Exchange | ASX - Outstanding Shares including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Australian Securities Exchange reported AUD13.82B in Market Capitalization this July of 2025, considering the latest stock price and the number of outstanding shares.Data for Australian Securities Exchange | ASX - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 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.

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.

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

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.

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.

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)

Advanced Semiconductor Engineering stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Australia's main stock market index, the ASX200, fell to 8682 points on July 24, 2025, losing 0.64% from the previous session. Over the past month, the index has climbed 1.43% and is up 10.43% compared to the same time last year, 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 July of 2025.

Between January 2010 and June 2025, the total market capitalization of domestic companies listed on the Australian Securities Exchange (ASX) grew from **** trillion Australian dollars to **** trillion Australian dollars. While the overall trend was upward, the growth curve was far from linear. The two most notable periods of decline were from March to September 2011, and the crash of March 2020 caused by the global coronavirus (COVID-19) pandemic.

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.

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

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: Will the Momentum Continue?

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

Australian Securities Exchange reported 26.58 in PE Price to Earnings for its fiscal semester ending in June of 2024. Data for Australian Securities Exchange | ASX - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last July in 2025.

The average value of daily trades on Australian equity markets jumped sharply in the first quarter of 2020, increasing from around 6.5 billion Australian dollars in the previous quarter to over 9.4 billion Australian dollars. While this spike was likely due to the economic impact of the coronavirus (COVID-19) pandemic, values did not return back to their trend value for the previous two years. While the quarterly average between Q1 2017 and Q4 2019 was around 6.4 billion U.S. dollars, the average between the first quarter of 2020 and the first quarter of 2024 was over eight billion Australian dollars. In general, between 80 and 85 percent of these the total values traded was on the Australian Securities Exchange (ASX), with the remainder being on the Chi-X Australia platform, which is operated by the Chicago Board Options Exchange (CBOE).

Graph and download economic data for Financial Market: Share Prices for Australia (SPASTT01AUM661N) from Jan 1958 to Jun 2025 about Australia and stock market.

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 ASX LIMITED

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