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

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.

Prices for Euro Stoxx Banks including live quotes, historical charts and news. Euro Stoxx Banks was last updated by Trading Economics this August 31 of 2025.

Morocco Casablanca Stock Exchange: Index: Bank Index data was reported at 18,012.658 NA in Apr 2025. This records a decrease from the previous number of 18,507.430 NA for Mar 2025. Morocco Casablanca Stock Exchange: Index: Bank Index data is updated monthly, averaging 12,895.660 NA from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 18,507.430 NA in Mar 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.

United States New York Stock Exchange: Index: Dow Jones US Bank Index data was reported at 620.500 NA in Apr 2025. This records a decrease from the previous number of 638.670 NA for Mar 2025. United States New York Stock Exchange: Index: Dow Jones US Bank Index data is updated monthly, averaging 411.215 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 710.140 NA in Jan 2025 and a record low of 186.370 NA in Jan 2012. United States New York Stock Exchange: Index: Dow Jones US Bank 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: Dow Jones: Monthly.

The Dow Jones U.S. Banks index is expected to continue its upward trend in the short term, with a moderate level of risk. Positive economic fundamentals and strong bank earnings are expected to drive the index higher. However, rising interest rates and geopolitical uncertainty could pose potential headwinds, leading to volatility and potential downside risks.

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.

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

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

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.

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.

Is the Regional Bank Index Signaling a Shift?

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 U.S. Banks Index Forecast: Steady Growth Anticipated

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.

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

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.

Predictions for Dow Jones U.S. Select Regional Banks index: Gradual upward trend with potential for moderate growth in the near term. Risks include economic headwinds, rising interest rates, and geopolitical uncertainties, which could impact financial performance and market sentiment.

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.

Will the Regional Bank Index Weather the Storm?

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

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.

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

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.