Graph and download economic data for Unemployment Rate in Phoenix-Mesa-Scottsdale, AZ (MSA) (PHOE004UR) from Jan 1990 to May 2025 about Phoenix, AZ, unemployment, rate, and USA.

Phoenix-Mesa-Chandler, AZ - Unemployment Rate in Phoenix-Mesa-Scottsdale, AZ (MSA) was 3.40% in December of 2024, according to the United States Federal Reserve. Historically, Phoenix-Mesa-Chandler, AZ - Unemployment Rate in Phoenix-Mesa-Scottsdale, AZ (MSA) reached a record high of 12.90 in April of 2020 and a record low of 2.50 in April of 1998. Trading Economics provides the current actual value, an historical data chart and related indicators for Phoenix-Mesa-Chandler, AZ - Unemployment Rate in Phoenix-Mesa-Scottsdale, AZ (MSA) - last updated from the United States Federal Reserve on July of 2025.

Unemployment data for Phoenix, AZ from "Local Area Unemployment Statistics" provided by the U.S. Bureau of Labor Statistics.

Survey Description: Labor force and unemployment estimates for States and local areas are developed by State workforce agencies to measure local labor market conditions under a Federal-State cooperative program. The Department of Labor develops the concepts, definitions, and technical procedures which are used by State agencies for preparation of labor force and unemployment estimates.

These estimates are derived from a variety of sources, including the Current Population Survey, the Current Employment Statistics survey, the Quarterly Census of Employment and Wages, various programs at the Census Bureau, and unemployment insurance claims data from the State workforce agencies.

To establish uniform labor force concepts and definitions in all States and areas consistent with those used for the U.S. as a whole, monthly national estimates of employment and unemployment from the Current Population Survey are used as controls (benchmarks) for the State labor force statistics.

Phoenix-Mesa-Chandler, AZ - Unemployment Rate in Phoenix-Mesa-Scottsdale, AZ (MSA) was 3.60% in March of 2025, according to the United States Federal Reserve. Historically, Phoenix-Mesa-Chandler, AZ - Unemployment Rate in Phoenix-Mesa-Scottsdale, AZ (MSA) reached a record high of 13.50 in April of 2020 and a record low of 2.30 in May of 1998. Trading Economics provides the current actual value, an historical data chart and related indicators for Phoenix-Mesa-Chandler, AZ - Unemployment Rate in Phoenix-Mesa-Scottsdale, AZ (MSA) - last updated from the United States Federal Reserve on May of 2025.

Graph and download economic data for Unemployment Rate in Maricopa County, AZ (AZMARI3URN) from Jan 1990 to May 2025 about Maricopa County, AZ; Phoenix; AZ; unemployment; rate; and USA.

Phoenix Area Unemployment Rate: 35 years of historical data from 1990 to 2025.

Graph and download economic data for Unemployed Persons in Phoenix-Mesa-Scottsdale, AZ (MSA) (LAUMT043806000000004) from Jan 1990 to May 2025 about Phoenix, AZ, household survey, unemployment, persons, and USA.

This data shows a summary of annual unemployment rates for cities within the metro Phoenix area and supports Tempe's Unemployment Rate performance measure.The performance measure page is available at 5.13 Unemployment Rate. Additional InformationSource: https://www.bls.gov/Contact (author): Madalaine McConvilleContact E-Mail (author): madalaine_mcconville@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Extracted for selected citiesPublish Frequency: AnnualPublish Method: ManualData Dictionary

Unemployment Rate in Arizona was 4.10% in April of 2025, according to the United States Federal Reserve. Historically, Unemployment Rate in Arizona reached a record high of 13.80 in April of 2020 and a record low of 3.30 in March of 2024. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in Arizona - last updated from the United States Federal Reserve on July of 2025.

Phoenix-Mesa-Chandler, AZ - Unemployed in Phoenix-Mesa-Scottsdale, AZ (MSA) was 84212.00000 Persons in April of 2025, according to the United States Federal Reserve. Historically, Phoenix-Mesa-Chandler, AZ - Unemployed in Phoenix-Mesa-Scottsdale, AZ (MSA) reached a record high of 298791.00000 in April of 2020 and a record low of 36624.00000 in May of 1998. Trading Economics provides the current actual value, an historical data chart and related indicators for Phoenix-Mesa-Chandler, AZ - Unemployed in Phoenix-Mesa-Scottsdale, AZ (MSA) - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for Unemployment Rate in Pinal County, AZ (AZPINA5URN) from Jan 1990 to May 2025 about Pinal County, AZ; Phoenix; AZ; unemployment; rate; and USA.

Graph and download economic data for Unemployment Rate in Pinal County, AZ (LAUCN040210000000003A) from 1990 to 2024 about Pinal County, AZ; Phoenix; AZ; unemployment; rate; and USA.

Graph and download economic data for Unemployed Persons in Maricopa County, AZ (LAUCN040130000000004) from Jan 1990 to Apr 2025 about Maricopa County, AZ; Phoenix; AZ; household survey; unemployment; persons; and USA.

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.

LON:PGR PHOENIX GLOBAL RESOURCES PLC

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.

LON:PHNX PHOENIX GROUP HOLDINGS PLC

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.

Phoenix Soars: A (PHNX) 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

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.

Phoenix Biotech Bravo: Ready to Soar? (PBAX)

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 Unemployed Persons in Pinal County, AZ (LAUCN040210000000004) from Jan 1990 to Apr 2025 about Pinal County, AZ; Phoenix; AZ; household survey; unemployment; persons; and USA.

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.

PEV Phoenix Motor Inc. Common Stock

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.

FENG Phoenix New Media Limited American Depositary Shares each representing 48 Class 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