ANZ Roy Morgan Consumer Confidence Index in New Zealand increased to 98.40 points in November from 92.40 points in October of 2025. This dataset includes a chart with historical data for New Zealand ANZ Roy Morgan Consumer Confidence Index.

Consumer Confidence in New Zealand decreased to 90.90 points in the third quarter of 2025 from 91.20 points in the second quarter of 2025. This dataset provides the latest reported value for - New Zealand Consumer Confidence - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Composite Leading Indicators: Composite Consumer Confidence Amplitude Adjusted for New Zealand (CSCICP03NZM665S) from Jun 1988 to Sep 2023 about consumer sentiment, New Zealand, composite, and consumer.

Consumer confidence survey in New Zealand, September, 2025 The most recent value is 98 points as of Q3 2025, a decline compared to the previous value of 98.01 points. Historically, the average for New Zealand from Q2 1988 to Q3 2025 is 100 points. The minimum of 96.32 points was recorded in 2023, while the maximum of 102.54 points was reached in Q2 1994. | TheGlobalEconomy.com

The ANZ Roy Morgan Consumer Confidence Index is a key economic indicator in New Zealand that measures consumer sentiment regarding current and future economic conditions.-2025-10-30

Graph and download economic data for Leading Indicators OECD: Component Series: Consumer Sentiment: Confidence Indicator: Original Series for New Zealand (LOCOCIORNZQ665S) from Q2 1988 to Q3 2022 about consumer sentiment, New Zealand, leading indicator, and consumer.

Overview with Chart & Report: The Westpac McDermott Miller Consumer Confidence Index reflects individuals' expectations concerning New Zealand's economic activity. The indicator is calculated based on a survey, which covers the

This report analyses trends in the consumer sentiment index. The consumer sentiment index is quantified by averaging five sub-indexes that are indicators of consumer confidence. These five indicators are: the household financial situation over the last year, the household financial situation over the coming year; the anticipated economic conditions over the coming year; the anticipated economic conditions over the next five years; and the buying conditions for major household items. A reading of 100 in each sub-index means that the number of positive responses is equal to the number of negative responses. The data for this report is sourced from the monthly ANZ-Roy Morgan New Zealand Survey of Consumer Confidence and is measured in index points. This report uses the average of monthly index values over each financial year.

New Zealand Consumer Confidence Indicator: sa: Normalised data was reported at 97.531 Normal=100 in Sep 2023. This records a decrease from the previous number of 97.613 Normal=100 for Aug 2023. New Zealand Consumer Confidence Indicator: sa: Normalised data is updated monthly, averaging 100.088 Normal=100 from Jun 1988 (Median) to Sep 2023, with 424 observations. The data reached an all-time high of 101.838 Normal=100 in Jun 1994 and a record low of 97.140 Normal=100 in Dec 2022. New Zealand Consumer Confidence Indicator: sa: Normalised data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s New Zealand – Table NZ.OECD.MEI: Consumer Opinion Surveys: Seasonally Adjusted: OECD Member. The indicator measures consumers' opinions by combining their replies to five internationally standardised questions. The five questions asked cover consumers' personal financial position and expectations, national economic expectations (over the next 12 month and over the 5 next years) and attitudes to major purchases.

New Zealand Consumer Confidence Indicator: sa: PoP: Normalised data was reported at 0.186 % in Dec 2024. This records a decrease from the previous number of 0.186 % for Nov 2024. New Zealand Consumer Confidence Indicator: sa: PoP: Normalised data is updated monthly, averaging -0.010 % from Jul 1988 (Median) to Dec 2024, with 438 observations. The data reached an all-time high of 0.648 % in Jul 2008 and a record low of -0.523 % in Jun 2000. New Zealand Consumer Confidence Indicator: sa: PoP: Normalised data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s New Zealand – Table NZ.OECD.MEI: Consumer Opinion Surveys: Seasonally Adjusted: OECD Member.

Graph and download economic data for Consumer Opinion Surveys: Composite Consumer Confidence for New Zealand (NZLCSCICP02STSAQ) from Q2 1988 to Q3 2025 about New Zealand and composite.

Between July and December of 2024, Australia and New Zealand have reported upward trajectories of the Consumer Confidence Index (CCI). In contrast, the Consumer Confidence Index in South Korea dropped from August to December 2024.

Consumer confidence survey in Nouvelle-Zélande, septembre, 2025 Pour cet indicateur, Westpac New Zealand fournit des données pour la Nouvelle-Zélande de Q2 1988 à Q3 2025. La valeur moyenne pour Nouvelle-Zélande pendant cette période était de 100 points avec un minimum de 96.32 points en 2023 et un maximum de 102.54 points en Q2 1994. | TheGlobalEconomy.com

Neuseelands Verbrauchervertrauen Veränderung im Jahresvergleich belief sich im 2025-09 auf 0.1 % Punkt. Dies stellt einen Rückgang im Vergleich zu den vorherigen Zahlen von 9.0 % Punkt für 2025-06 dar. Neuseelands Verbrauchervertrauen Veränderung im Jahresvergleich werden vierteljährlich aktualisiert, mit einem Durchschnitt von -7.0 % Punkt von 2007-03 bis 2025-09, mit 75 Beobachtungen. Die Daten erreichten ein Allzeithoch in Höhe von 24.3 % Punkt im 2009-06 und ein Rekordtief in Höhe von -29.7 % Punkt im 2008-06. Neuseelands Verbrauchervertrauen Veränderung im Jahresvergleich Daten behalten den Aktiv-Status in CEIC und werden von CEIC Data gemeldet. Die Daten werden unter World Trend Pluss Global Economic Monitor – Table: Consumer Confidence: Y-o-Y Change: Quarterly kategorisiert.

Neuseelands Verbrauchervertrauen: Nettobilanz belief sich im 2025-09 auf -9.1 % Punkt. Dies stellt einen Rückgang im Vergleich zu den vorherigen Zahlen von -8.8 % Punkt für 2025-06 dar. Neuseelands Verbrauchervertrauen: Nettobilanz werden vierteljährlich aktualisiert, mit einem Durchschnitt von 10.7 % Punkt von 2006-03 bis 2025-09, mit 79 Beobachtungen. Die Daten erreichten ein Allzeithoch in Höhe von 21.7 % Punkt im 2014-03 und ein Rekordtief in Höhe von -24.4 % Punkt im 2022-12. Neuseelands Verbrauchervertrauen: Nettobilanz Daten behalten den Aktiv-Status in CEIC und werden von CEIC Data gemeldet. Die Daten werden unter World Trend Pluss Global Economic Monitor – Table: Consumer Confidence: Net Balance: Quarterly kategorisiert.

Kepercayaan Konsumen Perubahan y-o-y Selandia Baru dilaporkan sebesar 0.1 % Poin pada 2025-09. Rekor ini turun dibanding sebelumnya yaitu 9.0 % Poin untuk 2025-06. Data Kepercayaan Konsumen Perubahan y-o-y Selandia Baru diperbarui triwulanan, dengan rata-rata -7.0 % Poin dari 2007-03 sampai 2025-09, dengan 75 observasi. Data ini mencapai angka tertinggi sebesar 24.3 % Poin pada 2009-06 dan rekor terendah sebesar -29.7 % Poin pada 2008-06. Data Kepercayaan Konsumen Perubahan y-o-y Selandia Baru tetap berstatus aktif di CEIC dan dilaporkan oleh CEIC Data. Data dikategorikan dalam Global Economic Monitor World Trend Plus – Table: Consumer Confidence: Y-o-Y Change: Quarterly.

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.

ANZ AUSTRALIA AND NEW ZEALAND BANKING GROUP 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

https://static01.nyt.com/images/2020/11/18/nyregion/00nyblind1/merlin_179220645_b77f46ff-a503-40b6-bf2b-4922a676e61b-superJumbo.jpg" alt=""> This dataset offers a comprehensive insight into the economic trajectories of nine major economies from the onset of the COVID-19 pandemic through the beginning of 2024. It encompasses crucial economic indicators and financial market data, covering aspects such as manufacturing and services performance, consumer sentiment, monetary policies, inflation rates, unemployment rates, and overall economic output. Additionally, it includes price data for each economy, with values compared against the dollar for clarity. With data spanning this period, the dataset provides valuable insights for analysts, researchers, and stakeholders into the impact of the pandemic and other significant events on these economies, facilitating an assessment of their resilience, challenges, and opportunities.

Countries included : Australia / Canada / China / Europe / Japan / New Zealand / Switzerland / United Kingdom / United States

Column Descriptions:

• Country : The name of the country.
• Date : The date format (e.g., YYYY-MM-DD).
• Manufacturing PMI : Purchasing Managers' Index (PMI) for the manufacturing sector, indicating the economic health and activity level of the manufacturing industry.
• Services PMI : Purchasing Managers' Index (PMI) for the services sector, indicating the economic health and activity level of the services industry.
• Consumer Confidence : A measure of consumer sentiment or confidence in the economy, indicating consumers' optimism or pessimism about their financial situation and the overall state of the economy.
• Interest Rates : The prevailing interest rates set by the central bank or monetary authority, which influence borrowing costs and investment decisions.
• CPI YoY : Consumer Price Index (CPI) Year-over-Year change, indicating the percentage change in the average price level of a basket of consumer goods and services over the previous year.
• Core CPI : Core Consumer Price Index (CPI), which excludes volatile items such as food and energy prices, providing a measure of underlying inflation trends.
• Unemployment Rate : The percentage of the labor force that is unemployed and actively seeking employment, indicating the health of the labor market.
• GDP YoY : Gross Domestic Product (GDP) Year-over-Year change, indicating the percentage change in the total value of goods and services produced by a country's economy.
• Ticker: Ticker symbol for the corresponding financial asset or index.
• Open: The opening price of the financial asset or index on the specified date.
• High: The highest price of the financial asset or index during the specified date.
• Low: The lowest price of the financial asset or index during the specified date.
• Close: The closing price of the financial asset or index on the specified date.

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

Unpredictable trading conditions have challenged the Department Stores industry over the past few years. Retailers’ revenue hiked in 2020-21 thanks to solid consumer spending on discretionary items, including expensive items like furniture. Government stimulus expanded real household discretionary income, encouraging consumer spending. However, the end of pandemic-related government support has weakened real household discretionary income and consumer sentiment, which has tightened consumers’ budgets for non-essential items sold at department stores in recent years. High inflation has also pressured department stores. A few enterprises entered the industry when business confidence heightened in 2021-22 and 2023-24. However, existing companies, including well-established players, have downsized floor space, reduced store numbers or opted to permanently leave the industry in recent years amid high inflation. These cost-saving strategies have fundamentally shrunk the industry’s size, with declines in profit and wages echoing this trend. Department stores have suffered from reduced consumer spending, which has harmed revenue, contributing to an expected 3.0% fall through the end of 2025-26, to an estimated $6.1 billion. Department stores have faced intense competition from online-only and specialty retailers offering affordable prices or unique products. An emerging consumer preference for personalised experiences has challenged department stores as many have shown strength in providing various products to broad consumer groups. Department stores’ income is projected to drop 2.9% in 2025-26. In response, department stores have been improving operational efficiencies, which include closing unprofitable stores and consolidating operations. Department stores have adopted data-driven technologies and omnichannel strategies to strengthen digital selling amid consumers’ growing online shopping habits. These measures have elevated profitability in recent years. Revenue is forecast to rise by an annualised0.4%, totalling $6.3 billion, through the end of 2030-31. A projected climb in real household discretionary income and consumer sentiment are forecast to stimulate consumer spending in department stores. Still, department stores will face fierce internal and external competition from specialty retailers, including apparel, furniture, sporting goods and electronics stores. Department stores that attract consumer interest through an expanded social media presence and strong multi-channel strategies can strengthen customer engagement and sales.