Unemployment Rate in New York was 4.00% in August of 2025, according to the United States Federal Reserve. Historically, Unemployment Rate in New York reached a record high of 16.70 in May of 2020 and a record low of 3.80 in April of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in New York - last updated from the United States Federal Reserve on September of 2025.

Unemployment Rate in New York County, NY was 5.20% in July of 2025, according to the United States Federal Reserve. Historically, Unemployment Rate in New York County, NY reached a record high of 16.80 in May of 2020 and a record low of 3.10 in December of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in New York County, NY - last updated from the United States Federal Reserve on September of 2025.

United States Unemployment Rate: New York data was reported at 4.200 % in Jun 2018. This records an increase from the previous number of 3.700 % for May 2018. United States Unemployment Rate: New York data is updated monthly, averaging 6.400 % from Jan 1976 (Median) to Jun 2018, with 510 observations. The data reached an all-time high of 11.200 % in Feb 1976 and a record low of 3.600 % in Apr 1988. United States Unemployment Rate: New York data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G057: Unemployment Rate: By State.

View monthly updates and historical trends for New York, NY Unemployment Rate. Source: Bureau of Labor Statistics. Track economic data with YCharts analyt…

Graph and download economic data for Unemployment Rate in New York (NYUR) from Jan 1976 to Aug 2025 about NY, unemployment, rate, and USA.

Unemployment Rate in New York County, NY was 4.80% in January of 2024, according to the United States Federal Reserve. Historically, Unemployment Rate in New York County, NY reached a record high of 9.50 in January of 2020 and a record low of 3.40 in January of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in New York County, NY - last updated from the United States Federal Reserve on September of 2025.

In 2023, the unemployment rate in New York was at 4.2 percent. This is a decrease from the previous year, when the unemployment rate stood at 4.3 and significantly lower than the state's high of 9.8 percent in 2020.

United States Unemployment Rate: sa: New York data was reported at 4.500 % in Jun 2018. This stayed constant from the previous number of 4.500 % for May 2018. United States Unemployment Rate: sa: New York data is updated monthly, averaging 6.400 % from Jan 1976 (Median) to Jun 2018, with 510 observations. The data reached an all-time high of 10.300 % in Feb 1976 and a record low of 4.000 % in May 1988. United States Unemployment Rate: sa: New York data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G058: Unemployment Rate: By State: Seasonally Adjusted.

Graph and download economic data for Unemployment Rate in Bronx County, NY (NYBRON5URN) from Jan 1990 to Jul 2025 about Bronx County, NY; New York; NY; unemployment; rate; and USA.

Insured Unemployment Rate in New York was 1.61% in September of 2025, according to the United States Federal Reserve. Historically, Insured Unemployment Rate in New York reached a record high of 23.43 in May of 2020 and a record low of 0.02 in October of 1987. Trading Economics provides the current actual value, an historical data chart and related indicators for Insured Unemployment Rate in New York - last updated from the United States Federal Reserve on September of 2025.

Unemployment Rate in Bronx County, NY was 6.40% in June of 2025, according to the United States Federal Reserve. Historically, Unemployment Rate in Bronx County, NY reached a record high of 26.50 in May of 2020 and a record low of 4.70 in December of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in Bronx County, NY - last updated from the United States Federal Reserve on September of 2025.

View monthly updates and historical trends for Bronx County, NY Unemployment Rate. Source: Bureau of Labor Statistics. Track economic data with YCharts an…

Unemployment Rate in New York-Newark-Jersey City, NY-NJ-PA (MSA) was 4.30% in June of 2025, according to the United States Federal Reserve. Historically, Unemployment Rate in New York-Newark-Jersey City, NY-NJ-PA (MSA) reached a record high of 17.50 in May of 2020 and a record low of 3.50 in May of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in New York-Newark-Jersey City, NY-NJ-PA (MSA) - last updated from the United States Federal Reserve on September of 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.

Can American Strategic Investment (NYC) Continue Its Market Outperformance?

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.

Nuveen New York (NYS: NQY) - Navigating the Empire State's Fiscal Landscape

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.

PNF PIMCO New York Municipal Income Fund 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

In 2023, the GDP of the New York metro area amounted to *** trillion chained 2017 U.S. dollars. This is an increase from 2021, when the GDP of the New York metro area was **** trillion dollars. New York CityThe New York metro area’s GDP has steadily risen in the last two decades from *** trillion U.S. dollars in 2001 to **** trillion U.S. dollars in 2023. In September 2023, the New York- Newark-Jersey City area had an unemployment rate of *** percent. It also had the highest population in the country in 2022 at ***** million people. New York City’s economy is one of the greatest in the country and is home to many Fortune 500 companies, including Big Pharma’s Bristol-Myers Squibb. Industries such as media, real estate, fashion and entertainment are some of the most prominent in the area. The finance industry in New York City, also known as Wall Street, is one of the leading financial centers of the world and houses the New York Stock Exchange and NASDAQ. The region is also home to one of the largest trading industries in the country at the Port of New York and New Jersey. This port includes a large estuary, regional airports, and a plethora of rail and road networks. Silicon Alley is one of the country’s largest technology industry hubs, including internet, telecommunications, and biotechnology. In 2022, there were some ****** business establishments in the region that focused on professional, scientific, and technical services.

Unemployment Level for New York was 421500.00000 Persons, 4-Qtr. Moving Average in July of 2024, according to the United States Federal Reserve. Historically, Unemployment Level for New York reached a record high of 1038800.00000 in January of 2021 and a record low of 363600.00000 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Level for New York - last updated from the United States Federal Reserve on September of 2025.

State Unemployment Benefits in New York was 2464703.00000 Mil. of $ in January of 2025, according to the United States Federal Reserve. Historically, State Unemployment Benefits in New York reached a record high of 89814248.00000 in April of 2020 and a record low of 151340.00000 in April of 1953. Trading Economics provides the current actual value, an historical data chart and related indicators for State Unemployment Benefits in New York - last updated from the United States Federal Reserve on September of 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.

NYT New York Times Company (The) 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