Prices for US Bank Index including live quotes, historical charts and news. US Bank Index was last updated by Trading Economics this December 2 of 2025.

The Nasdaq Bank Index tracks hundreds of banks whose shares are traded on the Nasdaq stock exchange. The index performance fluctuated considerably since 2000. Throught the years considered in the graph, the Nasdaq Bank index reached its lowest level at the closing of 2011, when it stood at ******* points. After further fluctuations, the index recovered and peaked at ******* at the end of 2021. As of the end of 2024, the index had a value of ******* points.

Prices for Euro Stoxx Banks including live quotes, historical charts and news. Euro Stoxx Banks was last updated by Trading Economics this November 30 of 2025.

From November 2024 to May 2025, the Nasdaq Bank Index, which tracks hundreds of banks whose shares are traded on the Nasdaq stock exchange, showed the continued impact of the Trump administration. In April 2025, the announcement of renewed Trump-era tariffs triggered a sharp drop in the index, with markets reacting swiftly to fears of escalating trade tensions. The impact was immediate across several sectors, but the banking industry showed notable resilience. Despite the initial selloff, banks recovered quickly. This resilience helped stabilize the broader index despite ongoing trade-related uncertainties.

Context

The National Stock Exchange of India Limited (NSE) is the leading stock exchange of India, located in Mumbai. Nifty Bank, or Bank Nifty, is an index comprised of the most liquid and large capitalized Indian banking stocks. It provides investors with a benchmark that captures the capital market performance of Indian bank stocks. The index has 12 stocks from the banking sector.

Apart from NIFTY BANK index, there are also other indices like NIFTY IT and indexes for other sectors. Exploring these indices may help in taking investment decisions.

Content

This dataset has daily information on NIFTY BANK index starting from 01 January 2018.

The file has the following columns

• Date - date of observation
• Open - open value of the index on that day
• High - highest value of the index on that day
• Low - lowest value of the index on that day
• Close - closing value of the index on that day
• Volume - volume of transaction

Acknowledgements

The data is obtained from NSE website with the help of python packages. Image credits: Photo by Hans Eiskonen on Unsplash

The Nasdaq Bank Index, which tracks hundreds of banks whose shares are traded on the Nasdaq stock exchange, fell drastically between the *** and **** of March 2023, following the collapse of Silicon Valley Bank (SVB) and Signature Bank in the United States. Though no other banks collapsed in the observed period, the index remained low until the end of March, as confidence in the banking sector dropped.

Morocco Casablanca Stock Exchange: Index: Bank Index data was reported at 19,291.538 NA in Nov 2025. This records a decrease from the previous number of 20,608.546 NA for Oct 2025. Morocco Casablanca Stock Exchange: Index: Bank Index data is updated monthly, averaging 13,867.180 NA from Jun 2013 (Median) to Nov 2025, with 150 observations. The data reached an all-time high of 20,911.813 NA in Aug 2025 and a record low of 10,279.140 NA in May 2020. Morocco Casablanca Stock Exchange: Index: Bank Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Morocco – Table MA.EDI.SE: Casablanca Stock Exchange: Monthly.

The dataset contains historical data for Nifty Bank stocks, which represent the performance of the banking sector in the Indian stock market. The dataset covers a period of 10 years, from 2012 to 2022.

The dataset includes the following columns:

1. Date: This column represents the date of the data entry, indicating the specific trading day in the market.

2. Open: The "Open" column displays the opening index value of Nifty Bank on each trading day. It represents the initial value at which the index started trading at the beginning of the day.

3. High: The "High" column represents the highest index value reached by Nifty Bank during the trading day. It indicates the peak value that the index achieved within the given day.

4. Low: The "Low" column indicates the lowest index value reached by Nifty Bank during the trading day. It represents the minimum value that the index touched within the given day.

5. Close: The "Close" column displays the closing index value of Nifty Bank for each trading day. It represents the final value at which the index finished trading at the end of the day.

6. Volume: The "Volume" column represents the trading volume of Nifty Bank on each trading day. It indicates the total number of shares or contracts traded during the day.

This dataset provides valuable information about the historical performance of Nifty Bank, allowing analysts, researchers, and investors to analyze and study the trends, patterns, and fluctuations in the banking sector over the 10-year period. It enables users to assess the overall performance of the banking industry in the Indian stock market and make informed decisions based on historical price movements, trading volume, and other relevant factors.

It's important to note that the dataset is based on historical data and does not guarantee future performance. Additionally, any analysis or interpretation of the dataset should consider other external factors, such as economic conditions, regulatory changes, and company-specific news, to gain a comprehensive understanding of the banking sector's performance.

Graph and download economic data for Volatility of Stock Price Index for Oman (DDSM01OMA066NWDB) from 1992 to 2021 about Oman, volatility, stocks, price index, indexes, and price.

Greece ASE: Index: FTSE Athex CSE Banking Index data was reported at 354.550 31Oct2008=2000 in Nov 2018. This records a decrease from the previous number of 411.870 31Oct2008=2000 for Oct 2018. Greece ASE: Index: FTSE Athex CSE Banking Index data is updated monthly, averaging 556.445 31Oct2008=2000 from Nov 2007 (Median) to Nov 2018, with 132 observations. The data reached an all-time high of 5,514.350 31Oct2008=2000 in Nov 2007 and a record low of 25.890 31Oct2008=2000 in Feb 2016. Greece ASE: Index: FTSE Athex CSE Banking Index data remains active status in CEIC and is reported by Athens Stock Exchange. The data is categorized under Global Database’s Greece – Table GR.Z001: Athens Stock Exchange: Index.

Graph and download economic data for Volatility of Stock Price Index for United States (DDSM01USA066NWDB) from 1984 to 2021 about volatility, stocks, price index, indexes, price, and USA.

Sri Lanka CSE: Index: Banks, Finance & Insurance data was reported at 15,669.030 NA in Oct 2018. This records an increase from the previous number of 15,456.490 NA for Sep 2018. Sri Lanka CSE: Index: Banks, Finance & Insurance data is updated monthly, averaging 2,684.465 NA from Jan 1987 (Median) to Oct 2018, with 382 observations. The data reached an all-time high of 19,298.060 NA in Jul 2015 and a record low of 136.070 NA in Jan 1987. Sri Lanka CSE: Index: Banks, Finance & Insurance data remains active status in CEIC and is reported by Colombo Stock Exchange. The data is categorized under Global Database’s Sri Lanka – Table LK.Z001: Colombo Stock Exchange: Index.

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 U.S. Banks Index Forecast: Mixed Outlook

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.

Dow Jones Banks Index: Will Stability Prevail?

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 Volatility of Stock Price Index for Netherlands (DDSM01NLA066NWDB) from 1984 to 2021 about Netherlands, volatility, stocks, price index, indexes, and price.

In 2020, the EURO STOXX Banks Index and the MSCI Europe Bank Index, two capitalization-weighted indexes that include banks in the monetary union and in Europe, registered some of the worst performances in recent years, falling by **** percent and **** percent respectively. In 2021, both indexes bounced back, growing **** percent and **** percent respectively.

United States Index: Philadelphia Stock Exchange: Bank data was reported at 101.570 21Oct1991=250 in Nov 2018. This records an increase from the previous number of 98.890 21Oct1991=250 for Oct 2018. United States Index: Philadelphia Stock Exchange: Bank data is updated monthly, averaging 72.290 21Oct1991=250 from Sep 1992 (Median) to Nov 2018, with 315 observations. The data reached an all-time high of 117.900 21Oct1991=250 in Jan 2007 and a record low of 22.074 21Oct1991=250 in Sep 1992. United States Index: Philadelphia Stock Exchange: Bank data remains active status in CEIC and is reported by Philadelphia Stock Exchange. The data is categorized under Global Database’s United States – Table US.Z014: Philadelphia Stock Exchange: Indexes.

United States New York Stock Exchange: Index: S&P Regional Banks Select Industry Index data was reported at 1,703.380 NA in Apr 2025. This records a decrease from the previous number of 1,789.060 NA for Mar 2025. United States New York Stock Exchange: Index: S&P Regional Banks Select Industry Index data is updated monthly, averaging 1,630.800 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 2,328.790 NA in Feb 2022 and a record low of 1,023.240 NA in Mar 2020. United States New York Stock Exchange: Index: S&P Regional Banks Select Industry Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: S&P: Monthly.

Graph and download economic data for Volatility of Stock Price Index for Lebanon (DDSM01LBA066NWDB) from 1996 to 2021 about Lebanon, volatility, stocks, price index, indexes, and 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.

Regional Banks Index: A Beacon of Economic Health?

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