Prices for US Bank Index including live quotes, historical charts and news. US Bank Index was last updated by Trading Economics this August 20 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 August 20 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.

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.

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.

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.

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 NYSE Financial Index tracks the performance of the equity components on the New York Stock Exchange that offer goods and services in the financial industry by market capitalization. Between ************ and *************, the value of the NYSE Financial Index fluctuated significantly and reached ********* index points.

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.

Dataset Description: 34-year Daily Stock Data (1990-2024)

Context and Inspiration

This dataset captures historical financial market data and macroeconomic indicators spanning over three decades, from 1990 onwards. It is designed for financial analysis, time series forecasting, and exploring relationships between market volatility, stock indices, and macroeconomic factors. This dataset is particularly relevant for researchers, data scientists, and enthusiasts interested in studying: - Volatility forecasting (VIX) - Stock market trends (S&P 500, DJIA, HSI) - Macroeconomic influences on markets (joblessness, interest rates, etc.) - The effect of geopolitical and economic uncertainty (EPU, GPRD)

Sources

The data has been aggregated from a mix of historical financial records and publicly available macroeconomic datasets: - VIX (Volatility Index): Chicago Board Options Exchange (CBOE). - Stock Indices (S&P 500, DJIA, HSI): Yahoo Finance and historical financial databases. - Volume Data: Extracted from official exchange reports. - Macroeconomic Indicators: Bureau of Economic Analysis (BEA), Federal Reserve, and other public records. - Uncertainty Metrics (EPU, GPRD): Economic Policy Uncertainty Index and Global Policy Uncertainty Database.

Columns

1. dt: Date of observation in YYYY-MM-DD format.
2. vix: VIX (Volatility Index), a measure of expected market volatility.
3. sp500: S&P 500 index value, a benchmark of the U.S. stock market.
4. sp500_volume: Daily trading volume for the S&P 500.
5. djia: Dow Jones Industrial Average (DJIA), another key U.S. market index.
6. djia_volume: Daily trading volume for the DJIA.
7. hsi: Hang Seng Index, representing the Hong Kong stock market.
8. ads: Aruoba-Diebold-Scotti (ADS) Business Conditions Index, reflecting U.S. economic activity.
9. us3m: U.S. Treasury 3-month bond yield, a short-term interest rate proxy.
10. joblessness: U.S. unemployment rate, reported as quartiles (1 represents lowest quartile and so on).
11. epu: Economic Policy Uncertainty Index, quantifying policy-related economic uncertainty.
12. GPRD: Geopolitical Risk Index (Daily), measuring geopolitical risk levels.
13. prev_day: Previous day’s S&P 500 closing value, added for lag-based time series analysis.

Key Features

• Cross-Market Analysis: Compare U.S. markets (S&P 500, DJIA) with international benchmarks like HSI.
• Macroeconomic Insights: Assess how external factors like joblessness, interest rates, and economic uncertainty affect markets.
• Temporal Scope: Longitudinal data facilitates trend analysis and machine learning model training.

Potential Use Cases

• Forecasting market indices using machine learning or statistical models.
• Building volatility trading strategies with VIX Futures.
• Economic research on relationships between policy uncertainty and market behavior.
• Educational material for financial data visualization and analysis tutorials.

Feel free to use this dataset for academic, research, or personal projects.

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.

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.

The value of the DJIA index amounted to ****** at the end of June 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

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.

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.

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 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

Graph and download economic data for Volatility of Stock Price Index for Brazil (DDSM01BRA066NWDB) from 1991 to 2021 about stocks, volatility, Brazil, price index, indexes, and price.