South Africa's main stock market index, the SAALL, fell to 96366 points on June 6, 2025, losing 0.05% from the previous session. Over the past month, the index has climbed 5.32% and is up 25.39% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from South Africa. South Africa Stock Market (SAALL) - values, historical data, forecasts and news - updated on June of 2025.

Graph and download economic data for Financial Market: Share Prices for South Africa (SPASTT01ZAQ661N) from Q1 1960 to Q1 2025 about South Africa and stock market.

Prices for South Africa Stock Market Index (Composite) including live quotes, historical charts and news. South Africa Stock Market Index (Composite) was last updated by Trading Economics this May 28 of 2025.

Graph and download economic data for Share Prices: All Shares/Broad: Total for South Africa (ZAFSPASTT01GYM) from Jan 1961 to Apr 2025 about , and South Africa.

South Africa Bond Market: Turnover: Market Price data was reported at 3,065,161.000 ZAR mn in Oct 2018. This records an increase from the previous number of 2,327,888.000 ZAR mn for Sep 2018. South Africa Bond Market: Turnover: Market Price data is updated monthly, averaging 1,169,048.500 ZAR mn from Jul 1994 (Median) to Oct 2018, with 292 observations. The data reached an all-time high of 3,065,161.000 ZAR mn in Oct 2018 and a record low of 89,190.000 ZAR mn in Dec 1994. South Africa Bond Market: Turnover: Market Price data remains active status in CEIC and is reported by Johannesburg Stock Exchange. The data is categorized under Global Database’s South Africa – Table ZA.Z013: Johannesburg Stock Exchange: Bond Market.

Prices for South Africa Stock Market Index (SA40) including live quotes, historical charts and news. South Africa Stock Market Index (SA40) was last updated by Trading Economics this June 9 of 2025.

Graph and download economic data for Financial Market: Share Prices for South Africa (SPASTT01ZAM661N) from Jan 1960 to Apr 2025 about South Africa and stock market.

There are six diferent kinds of widgets we have;

Ticker - This Widget is used for your websites top or bottom for navigation bar. It is horizontal bar with symbols last prices, daily changes and daily percentage changes.

Tape Ticker - This is a stock market classic widget that simply displays symbols (prices, daily changes and daily changes of percentages ) with a sliding cursor that stops when your cursor stops in a position it will stop too. Simple, fancy and useful.

Single Ticker - It's a simple one-symbol sized ticker.

Converter - This widget works best on the right or left sidebar of your website with a fast, useful currency converter with the latest updates and unit prices.

Mini Converter - It’s also simple and beautiful converter best for mobile websites.

Historical Chart - You can view the historical data details for a single symbol with the Historical Chart Widget.

Stock market return (%, year-on-year) in South Africa was reported at 24.02 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Stock market return (%, year-on-year) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Saudi Arabia SA: Stocks Traded: Total Value data was reported at 218.381 USD bn in 2017. This records a decrease from the previous number of 306.380 USD bn for 2016. Saudi Arabia SA: Stocks Traded: Total Value data is updated yearly, averaging 362.402 USD bn from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 1,403.048 USD bn in 2006 and a record low of 22.291 USD bn in 2001. Saudi Arabia SA: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

African Stock Market Dataset (Uganda, Ghana, Nigeria, South Africa, Zimbabwe)

This dataset provides historical stock market data from five major African markets: Uganda, Ghana, Nigeria, South Africa, and Zimbabwe. It contains daily stock prices from various companies listed in these countries, offering valuable insights for financial analysis, stock price forecasting, and market behavior comparison across the African continent.

Dataset Features:

Date: The trading date for each entry. Country: The country where the stock is listed (Uganda, Ghana, Nigeria, South Africa, Zimbabwe). Stock Ticker: Unique symbol for each company. Open Price: The price of the stock at market open. Close Price: The price of the stock at market close. High: The highest price reached during the trading session. Low: The lowest price reached during the trading session. Volume: The number of shares traded. This dataset can be used for a wide range of financial studies, including market trend analysis, stock performance comparison, and risk assessment. It is suitable for data scientists, economists, and traders interested in African financial markets.

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.

Should You Buy Now or Wait? SA 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

Key information about South Africa Market Capitalization

• South Africa Market Capitalization accounted for 1,230.120 USD bn in Jul 2023, compared with a percentage of 1,163.163 USD bn in the previous month
• South Africa Market Capitalization is updated monthly, available from Jun 1992 to Jul 2023
• The data reached an all-time high of 1,504.517 USD bn in Jan 2022 and a record low of 144.146 USD bn in Aug 1998

CEIC converts monthly Market Capitalization into USD. Johannesburg Stock Exchange used to provide Market Capitalization in local currency. The Federal Reserve Board period end market exchange rate is used for currency conversions.

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.

SPOT Spotify Technology S.A. Ordinary Shares

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 main stock market index of United States, the US500, rose to 6000 points on June 6, 2025, gaining 1.03% from the previous session. Over the past month, the index has climbed 6.55% and is up 12.22% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

South Africa PE Ratio: FTSE/JSE: Industrials data was reported at 13.002 NA in Nov 2018. This records a decrease from the previous number of 13.975 NA for Oct 2018. South Africa PE Ratio: FTSE/JSE: Industrials data is updated monthly, averaging 15.267 NA from Apr 2013 (Median) to Nov 2018, with 68 observations. The data reached an all-time high of 19.820 NA in Jan 2018 and a record low of 3.131 NA in Aug 2016. South Africa PE Ratio: FTSE/JSE: Industrials data remains active status in CEIC and is reported by Johannesburg Stock Exchange. The data is categorized under Global Database’s South Africa – Table ZA.Z006: Johannesburg Stock Exchange: Price Earnings Ratio.

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.

TBSAW TB SA Acquisition Corp Warrant

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.

PROC Procaps Group S.A. Ordinary Shares

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.

PRM Perimeter Solutions SA Ordinary Shares

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.

AKO/B Embotelladora Andina S.A.

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