In 2024, U.S. banks maintained a consistent return on equity (ROE), consistently exceeding ** percent quarterly. The fourth quarter saw ROE reach ***** percent, marking a continued recovery from previous economic disruptions. This performance reflects the banking sector's resilience through challenging periods, including the 2007-2008 financial crisis and the COVID-19 pandemic, highlighting the industry's capacity to adapt to economic volatility and regulatory shifts.

This statistic presents the return on average equity of banks in the United States from 1996 to 2019. The value of ROAE of the American banks was 11.39 percent in 2019.

The Dominican Republic's banking sector had the highest return on equity (ROE) in Latin America in 2021, with 23 percent. The banking sectors of Haiti, Guyana, and Uruguay also had ROEs above 20 percent. Among the observed countries, Venezuela and Colombia had the lowest ROE, with 6.5 and 4.8 percent, respectively.

Bank of America's return on equity (ROE) - calculated by dividing net income by shareholders' equity - fluctuated significantly between 2007 and 2024. The ROE was 8.65 percent in 2024, down from 8.71 percent in 2023. In the observed period, the ROE of the bank was the highest in 2021, and the lowest in 2010, at negative 1.56 percent.

India Bank of America: Return on Equity data was reported at 9.650 % in 2018. This records a decrease from the previous number of 10.400 % for 2017. India Bank of America: Return on Equity data is updated yearly, averaging 11.815 % from Mar 1999 (Median) to 2018, with 20 observations. The data reached an all-time high of 23.220 % in 1999 and a record low of 7.530 % in 2005. India Bank of America: Return on Equity data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Banking Sector – Table IN.KBR008: Foreign Banks: Selected Financial Ratios: Bank of America.

Graph and download economic data for Return on Average Equity for all U.S. Banks (DISCONTINUED) (USROE) from Q1 1984 to Q3 2020 about ROE, banks, depository institutions, and USA.

All five largest U.S. banks saw their returns on equity increase in the last quarter of 2024 compared to the same period in the previous year. JPMorgan Chase led the way with a ROE of 16.25 percent. It was followed by U.S. Bancorp and Wells Fargo, both with ROEs above 11 percent.

The return on equity (ROE) of European banking sectors showed significant disparities in the last quarter of 2024, with Romania leading at **** percent and Liechtenstein trailing at *** percent. This wide range reflects the diverse financial landscapes across the continent, influenced by factors such as market conditions, regulatory environments, and economic stability. While ROE is a crucial indicator of banking efficiency, it's important to consider it alongside other metrics for a comprehensive view of the industry's health. Digital transformation reshaping European banking The banking sector in Europe is undergoing a digital revolution, with online banking penetration reaching impressive levels. In 2024, Denmark lead with a ***** percent penetration rate, closely followed by Norway at **** percent. This shift towards digital banking is not only changing how traditional banks operate but also paving the way for the rise of digital-only banks. Neobanks like Revolut have seen rapid growth, with the UK-based fintech reaching ** million users by November 2024, highlighting the increasing consumer preference for digital financial services. Consolidation and asset growth in European banking Despite the high number of banks operating in Europe, with ***** institutions in the EU as of December 2024, the industry is dominated by a few large players. In 2023, HSBC Holdings lead European banks with total assets exceeding *** trillion U.S. dollars in 2023, followed closely by BNP Paribas SA with over *** trillion U.S. dollars. This concentration of assets among top banks, coupled with the ongoing digital transformation, suggests a trend towards consolidation in the European banking sector, potentially impacting future ROE figures across the continent.

India Bank of America: Return on Assets data was reported at 2.120 % in 2018. This records a decrease from the previous number of 2.320 % for 2017. India Bank of America: Return on Assets data is updated yearly, averaging 2.465 % from Mar 1999 (Median) to 2018, with 20 observations. The data reached an all-time high of 3.770 % in 2011 and a record low of 1.170 % in 2000. India Bank of America: Return on Assets data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Banking Sector – Table IN.KBR008: Foreign Banks: Selected Financial Ratios: Bank of America.

Bank of America's return on equity (ROE) has shown resilience in recent years, reaching 8.98 percent in the fourth quarter of 2024. This marks a significant improvement from the 4.32 percent recorded in the same quarter of 2023, reflecting the bank's ability to generate profits from shareholders' investments. The fluctuations in Bank of America's ROE mirror broader trends in the U.S. banking sector, which has demonstrated remarkable recovery from past financial crises and economic challenges.

India American Express Banking Corporation: Return on Equity data was reported at 6.170 % in 2018. This records an increase from the previous number of 4.510 % for 2017. India American Express Banking Corporation: Return on Equity data is updated yearly, averaging 0.640 % from Mar 1999 (Median) to 2018, with 19 observations. The data reached an all-time high of 15.130 % in 2006 and a record low of -21.800 % in 2009. India American Express Banking Corporation: Return on Equity data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Banking Sector – Table IN.KBR004: Foreign Banks: Selected Financial Ratios: American Express Banking Corporation.

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. Telecommunications: Industry in Transition?

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

Indonesia Bank of America N.A: Consolidated: Performance Ratio: Return on Equity data was reported at 7.040 % in Mar 2019. This records an increase from the previous number of 5.190 % for Dec 2018. Indonesia Bank of America N.A: Consolidated: Performance Ratio: Return on Equity data is updated quarterly, averaging 3.000 % from Dec 2000 (Median) to Mar 2019, with 72 observations. The data reached an all-time high of 182.000 % in Dec 2000 and a record low of -13.000 % in Mar 2007. Indonesia Bank of America N.A: Consolidated: Performance Ratio: Return on Equity data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Indonesia Premium Database’s Banking Sector – Table ID.KBF022: Foreign Bank: Financial Ratio: Bank of America N.A.

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 U.S. Financial Sector Index Conquer the Market?

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

Indexes included in the Russell U.S. Index Series Russell 3000®: The Russell 3000 Index measures the performance of the largest 3,000 U.S. companies representing approximately 98% of the investable U.S. equity market. Russell 1000®: The Russell 1000 Index measures the performance of the large-cap segment of the U.S. equity universe. It is a subset of the Russell 3000 Index and includes approximately 1,000 of the largest securities based on a combination of their market cap and current index membership. The Russell 1000 represents approximately 91% of the U.S. market. Russell 2000®: The Russell 2000 Index measures the performance of the small-cap segment of the U.S. equity universe. The Russell 2000 Index is a subset of the Russell 3000 Index representing approximately 9% of the total market capitalization of that index. It includes approximately 2,000 of the smallest securities based on a combination of their market cap and current index membership. Index Inception Dates Russell 1000® Index (1/1979) Russell 1000® Growth Index (1/1979) Russell 1000® Value Index (1/1979) Russell 2000® Index (1/1979) Russell 2000® Growth Index (1/1979) Russell 2000® Value Index (1/1979) Russell 2500™ Index (4/2003) Russell 2500™ Growth Index (4/2003) Russell 2500™ Value Index (4/2003) Russell 3000® Index (1/1979) Russell 3000® Growth Index (1/1979) Russell 3000® Value Index (1/1979) Russell Midcap® Index (1/1986) Russell Midcap® Growth Index (1/1987) Russell Midcap® Value Index (1/1987) Russell Small Cap Completeness Index (4/2003) Russell Small Cap Completeness Growth Index (4/2003) Russell Small Cap Completeness Value Index (4/2003) Russell Top 200® Index (7/1996) Russell Top 200® Growth Index (7/2001) Russell Top 200® Value Index (7/2001) Monthly Files included in the Russell U.S. Index Series Monthly Closing Files – RGS These holdings files reflect the official closing positions for all constituents of the 21 U.S. Russell Indexes at month-end back to December 1986 and at quarter-end from September 1986 back to December 1978. Security level information such as returns, market values, sector and industry classifications, and security weights are included in the file. Files are fixed-width text files and have a naming convention of H_yyyymmdd_RGS.txt. Monthly Closing Files – ICB These holdings files reflect the official closing positions for all constituents of the 21 U.S. Russell Indexes at month-end back to January 2010. Security level information such as returns, market values, sector and industry classifications, and security weights are included in the file. Files are comma delimited text files and have a naming convention of H_yyyymmdd.csv. Monthly Contribution to Return by RGS Files These files provide contribution to return using RGS as of the end of the month for each of the 21 U.S. Russell Indexes back to August 2008. Files are tab delimited text files and have a naming convention of CTR_MONTHLY_RGS_yyyymmdd.txt.. Monthly Contribution to Return by ICB Files These files provide contribution to return using ICB as of the end of the month for each of the 21 U.S. Russell Indexes back to August 2020. Files are comma delimited text files and have a naming convention of CTR_MONTHLY_yyyymmdd.csv. Monthly RGS Sector Weights Files These files provide monthly Russell Global Sector (RGS) weights for all 21 US Indexes at month-end back to November 2009. Files are comma delimited text files and have a naming convention of SWH_RGS_ALL_yyyymmdd.txt. Monthly ICB Sector Weights Files These files provide monthly Industrial Classification Benchmark (ICB) weights for all 21 US Indexes at month-end back to March 2020. Files are comma delimited text files and have a naming convention of SWH_ALL_yyyymmdd.csv. Note: In August 2020 FTSE Russell transitioned to ICB classification from the RGS classification. All data from September, 2020 is only available using ICB Classification. Data is current to 2024.

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.

Financial Services Sector Anticipates Moderate Growth, Dow Jones U.S. Financial Services index Shows.

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.

Investment Services Sector Poised for Growth, Dow Jones U.S. Select Investment Services Index Forecasts.

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. Select Medical Equipment: A Vital Sector in Healthcare?

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

The Real Estate Investment Trust (REIT) industry has witnessed significant transformation with the surge of data center REITs as a crucial asset class. Demand for hyperscale and edge computing facilities has been propelled by advancements in technologies such as artificial intelligence (AI) and 5G, supported by industry giants like Digital Realty and Equinix. Office REITs are recovering, facilitated by up-cycling in 2024 because of more significant leasing activity and return-to-office mandates. Strategically placed office spaces in urban cores are seeing increased demand, boosting property valuations and lease renewals, instilling renewed investor confidence in REITs. Through the end of 2025, industry revenue climbed at a CAGR of 0.9% to $243.7 billion, including a 4.4% gain in 2025 alone, when profit will reach 23.5%. The REIT industry has also seen marked consolidation activity. Despite elevated interest rates, publicly traded REITs raised $84.7 billion in 2024, signaling a strong appetite for acquisitions and displaying the benefits of having scope, scale and a robust operating platform. A strong PropTech adoption trend is evident, with AI, IoT and blockchain integrated into property operations to improve efficiency, reduce costs and enhance tenant experiences. This drive toward innovation helps the industry to better navigate economic challenges like elevated interest rates and inflation. Through the end of 2030, the REIT industry is expected to see favorable developments. Interest rates are expected to moderate over the next five years, easing borrowing costs for REITs and positively affecting their acquisitions and development strategies. Demand for healthcare-related properties will strengthen because of an aging US population and healthcare REIT's position as a resilient sector. The importance of data centers as a REIT asset class will gain, driven by the continuous advancements in AI and increased data operation transfers to the cloud. With an environment conducive to mergers and acquisitions, consolidation will continue, creating fewer but more substantial REITs that are better armed to navigate economic uncertainties and capitalize on sector-specific tailwinds. Industry revenue will climb at a CAGR of 1.6% to $264.0 billion through the end of 2030.

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.

Consumer Services Sector Anticipates Continued Growth, Dow Jones U.S. Consumer Services Index Shows.

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