Graph and download economic data for Financial Market: Share Prices for New Zealand (SPASTT01NZM661N) from Jan 1967 to Oct 2025 about New Zealand and stock market.

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

Graph and download economic data for Financial Market: Share Prices for New Zealand (SPASTT01NZQ661N) from Q1 1967 to Q3 2025 about New Zealand and stock market.

New Zealand's main stock market index, the NZX 50, rose to 13503 points on December 2, 2025, gaining 0.40% from the previous session. Over the past month, the index has declined 0.39%, though it remains 3.13% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from New Zealand. New Zealand Stock Market (NZX 50) - values, historical data, forecasts and news - updated on December of 2025.

Air New Zealand stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Graph and download economic data for Share Prices: All Shares/Broad: Total for New Zealand (SPASTT01NZA657N) from 1968 to 2024 about New Zealand and stock market.

Prices for NZX 50 INDEX GROSS including live quotes, historical charts and news. NZX 50 INDEX GROSS was last updated by Trading Economics this November 30 of 2025.

New Zealand NZ: Stocks Traded: Total Value data was reported at 11.868 USD bn in 2017. This records an increase from the previous number of 10.936 USD bn for 2016. New Zealand NZ: Stocks Traded: Total Value data is updated yearly, averaging 5.407 USD bn from Dec 1985 (Median) to 2017, with 33 observations. The data reached an all-time high of 12.046 USD bn in 1999 and a record low of 937.670 USD mn in 1985. New Zealand NZ: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s New Zealand – Table NZ.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.

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.

Dow Jones New Zealand Index: A Bullish Journey or Bearish Plunge?

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

Graph and download economic data for Share Prices: All Shares/Broad: Total for New Zealand (SPASTT01NZM657N) from Feb 1967 to Oct 2025 about New Zealand and stock market.

New Zealand NZ: Stocks Traded: Total Value: % of GDP data was reported at 5.765 % in 2017. This records a decrease from the previous number of 5.777 % for 2016. New Zealand NZ: Stocks Traded: Total Value: % of GDP data is updated yearly, averaging 4.899 % from Dec 1985 (Median) to 2017, with 33 observations. The data reached an all-time high of 20.927 % in 1998 and a record low of 1.307 % in 2010. New Zealand NZ: Stocks Traded: Total Value: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s New Zealand – Table NZ.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.; ; World Federation of Exchanges database.; Weighted average; 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.

The Dow Jones New Zealand index is expected to experience moderate growth in the upcoming period. The index may face risks associated with global economic uncertainties, fluctuations in commodity prices, and changes in monetary policy. However, the index is supported by positive economic fundamentals, such as a strong labor market and increasing consumer spending. Additionally, potential mergers and acquisitions within the top constituents of the index could provide further upside potential. Overall, the risks associated with the predictions are within the realm of normal market volatility.

NZX reported NZD22.07 in PE Price to Earnings for its fiscal semester ending in June of 2024. Data for NZX - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Graph and download economic data for Volatility of Stock Price Index for New Zealand (DDSM01NZA066NWDB) from 1992 to 2021 about New Zealand, volatility, stocks, price index, indexes, and price.

This dataset is about stocks per day. It has 957 rows and is filtered where the stock is NWF.NZ. It features 6 columns including stock, opening price, highest price, and lowest price.

NZX reported NZD495.27M in Market Capitalization this December of 2025, considering the latest stock price and the number of outstanding shares.Data for NZX - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last December in 2025.

This dataset is about stocks per day. It has 843 rows and is filtered where the stock is MMH.NZ. It features 6 columns including stock, opening price, highest price, and lowest price.

This dataset is about stocks per day. It has 957 rows and is filtered where the stock is TRU.NZ. It features 3 columns: stock, and highest price.

This dataset is about stocks per day. It has 467 rows and is filtered where the stock is TCL.NZ. It features 6 columns including stock, opening price, highest price, and lowest 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.

NZ Dow Jones Forecast: Gains Projected Amidst Global Uncertainty for Local Market Index

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