Bank Rankings by Total Assets

Tracking the Financial Performance of the Top Banks

By Arthur Keen [source]

About this dataset

This dataset contains the top 100 global banks ranked by total assets on December 31, 2017. With a detailed list of key information for each bank's rank, country, balance sheet and US Total Assets (in billions), this data will be invaluable for those looking to research and study the current status of some of the world's leading financial organizations. From billion-dollar mega-banks such as JP Morgan Chase to small, local savings & loans institutions like BancorpSouth; this comprehensive overview allows researchers and analysts to gain a better understanding of who holds power in the world economy today

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How to use the dataset

This dataset contains the rank and total asset information of the top 100 global banks as of December 31, 2017. It is a useful resource for researchers who wish to study how key financial institutions' asset information relate to each other across countries.

Using this dataset is relatively straightforward – it consists of three columns - rank (the order in which each bank appears in the list), country (the country in which the bank is located) and total assets US billions (the total value expressed in US dollars). Additionally, there is a fourth column containing the balance sheet information for each bank as well.

In order to make full use of this dataset, one should analyse it by creating comparison grids based on different factors such as region, size or ownership structures. This can provide an interesting insight into how financial markets are structured within different economies and allow researchers to better understand some banking sector dynamics that are particularly relevant for certain countries or regions. Additionally, one can compare any two banks side-by-side using their respective balance sheets or distribution plot graphs based on size or concentration metrics by leverage or other financial ratios as well.

Overall, this dataset provides useful resources that can be put into practice through data visualization making an interesting reference point for trends analysis and forecasting purposes focusing on certain banking activities worldwide

Research Ideas

• Analyzing the differences in total assets across countries. By comparing and contrasting data, patterns could be found that give insight into the factors driving differences in banks’ assets between different markets.

• Using predictive models to identify which banks are more likely to perform better based on their balance sheet data, such as by predicting future profits or cashflows of said banks.

• Leveraging the information on holdings and investments of “top-ranked” banks as a guide for personal investments decisions or informing investment strategies of large financial institutions or hedge funds

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: top50banks2017-03-31.csv | Column name | Description | |:----------------------|:------------------------------------------------------------------------| | rank | The rank of the bank globally based on total assets. (Integer) | | country | The country where the bank is located. (String) | | total_assets_us_b | The total assets of a bank expressed in billions of US dollars. (Float) | | balance_sheet | A snapshot of banking activities for a specific date. (Date) |

File: top100banks2017-12-31.csv | Column name | Description | |:----------------------|:--------------------------------------------...

Data are collected as of the end of the month for March, June, September and December, and generally are released three months later. There are two reports showing the same structure and asset information for each U.S. office, but in different orders. Offices located in Puerto Rico, American Samoa, Guam, the Virgin Islands and other U.S.-affiliated insular areas are excluded. The first report lists the offices by institution type. The second report is by the home country of the foreign bank. Each report shows asset totals and subtotals for the categories displayed.

The FFIEC 002 is mandated by the International Banking Act (IBA) of 1978. It collects balance sheet and off-balance-sheet information, including detailed supporting schedule items, from all U.S. branches and agencies of foreign banks. The FFIEC 002S collects information on assets and liabilities of any non-U.S. branch that is managed or controlled by a U.S. branch or agency of a foreign bank.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Banks. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Banks. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Banks, householders within the 25 to 44 years age group have the highest median household income at $94,167, followed by those in the 45 to 64 years age group with an income of $84,566. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $81,667. Notably, householders within the under 25 years age group, had the lowest median household income at $70,000.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Banks median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Banks by race. It includes the population of Banks across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Banks across relevant racial categories.

Key observations

The percent distribution of Banks population by race (across all racial categories recognized by the U.S. Census Bureau): 53.02% are white and 46.98% are Black or African American.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Banks
• Population: The population of the racial category (excluding ethnicity) in the Banks is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Banks total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Banks Population by Race & Ethnicity. You can refer the same here

Continued consolidation of the US banking industry and a general increase in the size of banks have prompted some policymakers to consider policies that discourage banks from getting larger, including explicit caps on bank size. However, limits on the size of banks could entail economic costs if they prevent banks from achieving economies of scale. This paper presents new estimates of returns to scale for US banks based on nonparametric, local-linear estimation of bank cost, revenue, and profit functions. We report estimates for both 2006 and 2015 to compare returns to scale some 7 years after the financial crisis and 5 years after enactment of the Dodd-Frank Act with returns to scale before the crisis. We find that a high percentage of banks faced increasing returns to scale in cost in both years, including most of the 10 largest bank holding companies. Also, while returns to scale in revenue and profit vary more across banks, we find evidence that the largest four banks operate under increasing returns to scale.

These reports collect selected financial information for direct or indirect foreign subsidiaries of U.S. state member banks (SMBs), Edge and agreement corporations, and bank holding companies (BHCs). The FR 2314 consists of a balance sheet and income statement; information on changes in equity capital, changes in the allowance for loan and lease losses, off-balance-sheet items, and loans; and a memoranda section. The FR 2314S collects four financial data items for smaller, less complex subsidiaries. (Note: The Report of Condition for Foreign Subsidiaries of U.S. Banking Organizations, FR 2314a and FR 2314c have been replaced by the FR 2314 and FR 2314S. and the FR 2314b has been discontinued.

U.S. commercial banks, bank holding companies, including financial holding companies, and Edge Act and agreement corporations (U.S. banks) are required to file the FR 2502q reporting form for their large branches and banking subsidiaries that are located in the United Kingdom or the Caribbean.

The H.8 release provides an estimated weekly aggregate balance sheet for all commercial banks in the United States. The release also includes separate balance sheet aggregations for several bank groups: domestically chartered commercial banks; large domestically chartered commercial banks; small domestically chartered commercial banks; and foreign-related institutions in the United States. Foreign-related institutions include U.S. branches and agencies of foreign banks as well as Edge Act and agreement corporations. Published weekly, the release is typically available to the public by 4:15 p.m. each Friday. If Friday is a federal holiday, then the data are released on Thursday.The H.8 release is primarily based on data that are reported weekly by a sample of approximately 875 domestically chartered banks and foreign-related institutions. As of December 2009, U.S. branches and agencies of foreign banks accounted for about 60 of the weekly reporters and domestically chartered banks made up the rest of the sample. Data for domestically chartered commercial banks and foreign-related institutions that do not report weekly are estimated at a weekly frequency based on quarterly Call Report data.

These two .csv files contain the US bank dataset for FETILDA, containing sections of 10-K reports submitted by US banks from 2006 to 2016. They are directly used by the Python scripts for training, validation, and testing. There are two files, one for Item 1A of the 10-K reports, and the other for Item 7/7A.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Banks: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 10(11.49%) households where the householder is under 25 years old, 26(29.89%) households with a householder aged between 25 and 44 years, 23(26.44%) households with a householder aged between 45 and 64 years, and 28(32.18%) households where the householder is over 65 years old.
• In Banks, the age group of 65 years and over stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Banks median household income by age. You can refer the same here

About Dataset

  Context

Term deposits are a major source of income for a bank. A term deposit is a cash investment held at a financial institution. Your money is invested for an agreed rate of interest over a fixed amount of time, or term. The bank has various outreach plans to sell term deposits to their customers such as email marketing, advertisements, telephonic marketing, and digital marketing. Telephonic marketing campaigns still remain one of the most effective way… See the full description on the dataset page: https://huggingface.co/datasets/Andyrasika/banking-marketing.

This report collects information, by country, from U.S. branches and agencies of foreign banks on direct, indirect, and total adjusted claims on foreign residents. The report also collects information about the respondents' direct claims on related non-U.S. offices domiciled in countries other than the home country of the parent bank that are ultimately guaranteed in the home country. A breakdown of adjusted claims on unrelated foreign residents provides exposure information.

The Uniform Bank Performance Report (UBPR) serves as an analysis of the impact that management and economic conditions can have on a bank's balance sheet. It examines liquidity, adequacy of capital and earnings and other factors that could damage the stability of the bank. The Federal Financial Institutions Examination Council (FFIEC) is a formal U.S. government interagency body that includes five banking regulators—the Federal Reserve Board of Governors (FRB), the Federal Deposit Insurance Corporation (FDIC), the National Credit Union Administration (NCUA), the Office of the Comptroller of the Currency (OCC), and the Consumer Financial Protection Bureau (CFPB). It is "empowered to prescribe uniform principles, standards, and report forms to promote uniformity in the supervision of financial institutions".[1] It also oversees real estate appraisal in the United States.[2] Its regulations are contained in title 12 of the Code of Federal Regulations.

The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset tabulates the Banks Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Banks, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Banks.

Key observations

Among the Hispanic population in Banks, regardless of the race, the largest group is of Mexican origin, with a population of 191 (75.49% of the total Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Banks
• Population: The population of the specific origin for Hispanic or Latino population in the Banks is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Banks total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Banks Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Banks population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Banks. The dataset can be utilized to understand the population distribution of Banks by age. For example, using this dataset, we can identify the largest age group in Banks.

Key observations

The largest age group in Banks, OR was for the group of age 35 to 39 years years with a population of 218 (10.42%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Banks, OR was the 80 to 84 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Banks is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Banks total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Banks Population by Age. You can refer the same here

Banking77Classification An MTEB dataset Massive Text Embedding Benchmark

Dataset composed of online banking queries annotated with their corresponding intents.

Task category t2c

Domains Written

Reference https://arxiv.org/abs/2003.04807

  How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code: import mteb

task = mteb.get_tasks(["Banking77Classification"]) evaluator = mteb.MTEB(task)

model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/banking77.

This dataset provides values for BANK DEPOSITS TO GDP WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Multibeam backscatter imagery extracted from gridded bathymetry of Ofu, Olosega, and Ta'u Islands of the Manua Island Group, American Samoa, South Pacific. These data provide coverage between 0 and 350 meters. The backscatter dataset includes data collected using a Reson 8101, 240 kHz multibeam sonar. These metadata are for the 1 m grid cell size Reson 8101 multibeam backscatter data only.