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.

US Debt Default Expectations Rise as Congress Stalls on Raising the Limit

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

A table that summarizes the amounts outstanding for the securities issued by the Bureau of the Fiscal Service adjusted for Unamortized Discount on Treasury Bills and Zero Coupon Treasury Bonds, Other Debt (old debt issued before 1917 and old currency called United States Notes), Debt held by the Federal Financing Bank and Guaranteed Debt of Government Agencies that makes up the Total Public Debt Subject to Limit amount.

Graph and download economic data for Federal Debt: Total Public Debt (GFDEBTN) from Q1 1966 to Q2 2025 about public, debt, federal, government, and USA.

Government Debt in the United States increased to 38040094 USD Million in October from 37637553 USD Million in September of 2025. This dataset provides - United States Government Debt- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This poll, fielded June 3-7, 2011, is part of a continuing series of monthly surveys that solicits public opinion on the presidency and on a range of other political and social issues. Respondents were asked whether they approved of the way Barack Obama was handling his job as president, foreign policy, the economy, the situation with Afghanistan, the threat of terrorism, and the federal budget deficit. Respondents were also asked whether they approved of Congress, about the condition of the economy, and whether things in the country were on the right track. Opinions were sought on the severity of the federal budget deficit, overall approval of the Republican and Democratic parties, whether Barack Obama and the Republicans in Congress have spent enough time on important issues, the handling of the federal budget deficit by the Republicans and Democrats in Congress, and the United States' presence in Libya and Afghanistan. Multiple questions addressed the 2012 Republican presidential candidates including respondents' overall opinions of several of the candidates. Further questions asked for respondents' opinions on the debt ceiling debate, including the potential effects of reducing the deficit on the number of jobs, making changes to Medicare, Social Security, and increasing taxes, the probability of a stock market downturn if the debt ceiling was not raised, whether spending cuts should be included in talks of raising the debt ceiling, and whether the debate in Washington about the debt ceiling is mostly about honest disagreements about economic policy or political gain. Additional topics include health care law, Medicare, the regional job and housing markets, the respondents' selection of the most important issues, voter participation, as well as knowledge of and relationship to an individual killed in the September 11, 2001 terrorist attack. Demographic variables include sex, age, race, education level, household income, religious preference, type of residential area (e.g., urban or rural), whether respondents thought of themselves as born-again Christians, marital status, employment status, number of children, number of people in the household between the ages of 18 and 29 years old, political party affiliation, political philosophy, and voter registration status.

Description:

Explore the dynamic landscape of the Indian stock market with this extensive dataset featuring 4456 companies listed on both the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). Gain insights into each company's financial performance, quarterly and yearly profit and loss statements, balance sheets, cash flow data, and essential financial ratios. Dive deep into the intricacies of shareholding patterns, tracking the movements of promoters, foreign and domestic institutional investors, and the public.

This dataset is a rich resource for financial analysts, investors, and data enthusiasts. Perform thorough company evaluations, sector-wise comparisons, and predictive modeling. With figures presented in crore rupees, leverage the dataset for in-depth exploratory data analysis, time series forecasting, and machine learning applications. Stay tuned for updates as we enrich this dataset for a deeper understanding of the Indian stock market landscape. Unlock the potential of data-driven decision-making with this comprehensive repository of financial information.

Folder Structure:

• 4492 NSE & BSE Companies
  • Main directory containing data for 4456 NSE and BSE registered companies.
  • Company_name folder
    • Individual folders for each company allowing for easy organization and retrieval.
    • Company_name.csv
      • General company information.
    • Quarterly_Profit_Loss.csv
      • Quarterly financial data.
    • Yearly_Profit_Loss.csv
      • Annual financial data.
    • Yearly_Balance_Sheet.csv
      • Annual balance sheet information.
    • Yearly_Cash_flow.csv
      • Annual cash flow data.
    • Ratios.csv.csv
      • Financial ratios over time.
    • Quarterly_Shareholding_Pattern.csv
      • Quarterly shareholding pattern.
    • Yearly_Shareholding_Pattern.csv
      • Annual shareholding pattern.

File Explanation:

Company_name.csv

- `Company_name`: Name of the company.
- `Sector`: Industry sector of the company.
- `BSE`: Bombay Stock Exchange code.
- `NSE`: National Stock Exchange code.
- `Market Cap`: Market capitalization of the company.
- `Current Price`: Current stock price.
- `High/Low`: Highest and lowest stock prices.
- `Stock P/E`: Price to earnings ratio.
- `Book Value`: Book value per share.
- `Dividend Yield`: Dividend yield percentage.
- `ROCE`: Return on capital employed percentage.
- `ROE`: Return on equity percentage.
- `Face Value`: Face value of the stock.
- `Price to Sales`: Price to sales ratio.
- `Sales growth (1, 3, 5, 7, 10 years)`: Sales growth percentage over different time periods.
- `Profit growth (1, 3, 5, 7, 10 years)`: Profit growth percentage over different time periods.
- `EPS`: Earnings per share.
- `EPS last year`: Earnings per share in the last year.
- `Debt (1, 3, 5, 7, 10 years)`: Debt of the company over different time periods.

Quarterly_Profit_Loss.csv

 - `Sales`: Revenue generated by the company.
 - `Expenses`: Total expenses incurred.
 - `Operating Profit`: Profit from core operations.
 - `OPM %`: Operating Profit Margin percentage.
 - `Other Income`: Additional income sources.
 - `Interest`: Interest paid.
 - `Depreciation`: Depreciation of assets.
 - `Profit before tax`: Profit before tax.
 - `Tax %`: Tax percentage.
 - `Net Profit`: Net profit after tax.
 - `EPS in Rs`: Earnings per share.

Yearly_Profit_Loss.csv

- Same as Quarterly_Profit_Loss.csv, but on a yearly basis.

Yearly_Balance_Sheet.csv

- `Equity Capital`: Capital raised through equity.
- `Reserves`: Company's retained earnings.
- `Borrowings`: Company's borrowings.
- `Other Liabilities`: Other financial obligations.
- `Total Liabilities`: Sum of all liabilities.
- `Fixed Assets`: Company's long-term assets.
- `CWIP`: Capital Work in Progress.
- `Investments`: Company's investments.
- `Other Assets`: Other non-current assets.
- `Total Assets`: Sum of all assets.

Yearly_Cash_flow.csv

- `Cash from Operating Activity`: Cash generated from core business operations.
- `Cash from Investing Activity`: Cash from investments.
- `Cash from Financing Activity`: Cash from financing (borrowing, stock issuance, etc.).
- `Net Cash Flow`: Overall net cash flow.

Ratios.csv.csv

- `Debtor Days`: Number of days it takes to collect receivables.
- `Inventory Days`: Number of days inventory is held.
- `Days Payable`: Number of days a company takes to pay its bills.
- `Cash Conversion Cycle`: Time taken to convert sales into cash.
- `Wor...

High-level information on the federal government's outstanding debts, holdings, and the statutory debt limit. Data is reported monthly.

China Market Cap of Depository Securities: Shenzhen SE: Securities Company Short-Term Debt data was reported at 0.000 RMB mn in 01 Dec 2025. This stayed constant from the previous number of 0.000 RMB mn for 28 Nov 2025. China Market Cap of Depository Securities: Shenzhen SE: Securities Company Short-Term Debt data is updated daily, averaging 0.000 RMB mn from Apr 2019 (Median) to 01 Dec 2025, with 1619 observations. The data reached an all-time high of 36,835.000 RMB mn in 09 Apr 2019 and a record low of 0.000 RMB mn in 01 Dec 2025. China Market Cap of Depository Securities: Shenzhen SE: Securities Company Short-Term Debt data remains active status in CEIC and is reported by Shenzhen Stock Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZA: Shenzhen Stock Exchange: Depository Securities.

Disclaimer

This dataset was compiled from publicly available financial data sourced from Yahoo Finance for research and educational purposes only. Redistribution of raw data may be subject to Yahoo Finance’s terms of service. Users are responsible for complying with all applicable data usage policies and regulations.

Description

This dataset contains two CSV files for companies listed on the London Stock Exchange (next, LSE) * raw_financial_metrics_2025_march.csv - latest available financial data (as of March 2025) including metrics such as revenue, net income, P/E ratio, total debt, market capitalisation and industry classification. * historical_stock_prices_2015_2025.csv - 10 years of daily closing stock prices (2015 April 1st - 2025 March 28) for the same set of companies

The dataset is designed to support: * Financial valuation research * Time-series forecasting (e.g., LSTM ARIMA) * Multi-modal learning (e.g., combining static metrics and price trends) * Exploratory analysis by sector, market capitalisation, etc.

Columns Overview

Financial Metrics:

General Company Information

• Ticker: Unique stock symbol used to identify the company on the stock exchange.
• Name: Full name of the company.
• Website: Official company website.
• Address, City, Postcode, Country: Headquarters location details.
• Exchange: Stock exchange where the company is listed (e.g. LSE for London Stock Exchange).

Market & Share Information

• Market Cap: Total market value of a company’s outstanding shares.
• Shares Outstanding: Total number of shares currently issued and held by shareholders.
• Float Shares: Shares available for public trading (excludes insider holdings).

Stock Performance

• Regular Market Price: Latest trading price during regular market hours.
• Regular Market Change / (%): Absolute and percentage change in price from the previous trading day.
• 50 Day Average / 200 Day Average: Average stock prices over the past 50 and 200 days, respectively.
• 52 Week Change: Percentage change in stock price over the past year.
• 52 Week High / Low: Highest and lowest stock prices over the past year.

Valuation Ratios

• Enterprise to Revenue / EBITDA: Valuation multiples showing how the company is priced relative to its revenue or earnings before interest, taxes, depreciation, and amortization.
• P/E Ratio (Price-to-Earnings): Valuation based on earnings per share.
• P/B Ratio (Price-to-Book): Compares market value to book value.
• Price to Sales (TTM): Market cap divided by total sales over the trailing twelve months.
• PEG Ratio: Price/Earnings ratio adjusted for expected earnings growth.

Financial Strength

• Book Value: Total company equity per share.
• Beta: A Measure of stock volatility relative to the market.
• Debt-to-Equity Ratio: Leverage ratio comparing total debt to shareholder equity.
• Quick Ratio / Current Ratio: Liquidity indicators for meeting short-term obligations.

Cash Flows & Liquidity

• Operating Cash Flow: Cash generated from core business operations.
• Free Cash Flow: Cash available after capital expenditures.
• Total Cash: The Company’s total cash reserves.
• Total Debt: Total liabilities in the form of debt.
• Current Total Assets: Total assets expected to be converted into cash within a year.
• Total Cash Per Share: Cash reserves divided by the number of outstanding shares.

Earnings & Profitability

• Gross Profits: Revenue minus cost of goods sold.
• EBITDA: Earnings before interest, taxes, depreciation, and amortization.
• Operating Margins / Profit Margins: Efficiency and profitability ratios.
• Forward EPS / Trailing EPS: Projected vs actual earnings per share.
• Earnings Quarterly Growth: Year-over-year change in earnings.
• Revenue Growth: Year-over-year change in revenue.

Dividends

• Dividend Rate: Annual dividend per share.
• Dividend Yield: Dividend as a percentage of the share price.
• Payout Ratio: Proportion of earnings paid out as dividends.

Analyst Sentiment

• Recommendation: Consensus recommendation from financial analysts (e.g., Buy, Hold, Sell).
• Recommendation Mean: Average numerical value of analyst ratings (lower means more favorable).
• Number of Analyst Opinions: Number of analyst reports used to generate recommendation scores.

Stock Prices

• Company Name - the name of the company
• Date (daily) - the dates in the "yyyy-mm-dd" format

Potential Use Cases

• Stock price prediction
• Deep learning models combining tabular and time-series data
• Graham-style intrinsic value estimation
• Sector-wise performance benchmarking
• Feature engineering and data preprocessing experiments

Inspiration

This dataset was created as part of a Data Science research project exploring the use of financial fundamentals and historical price movements to evaluate company value and predict future performance.

Debt-To-Capital-Ratio Time Series for BlackRock Inc. BlackRock, Inc. is a publicly owned investment manager. The firm primarily provides its services to institutional, intermediary, and individual investors including corporate, public, union, and industry pension plans, insurance companies, third-party mutual funds, endowments, public institutions, governments, foundations, charities, sovereign wealth funds, corporations, official institutions, and banks. It also provides global risk management and advisory services. The firm manages separate client-focused equity, fixed income, and balanced portfolios. It also launches and manages open-end and closed-end mutual funds, offshore funds, unit trusts, and alternative investment vehicles including structured funds. The firm launches equity, fixed income, balanced, and real estate mutual funds. It also launches equity, fixed income, balanced, currency, commodity, and multi-asset exchange traded funds. The firm also launches and manages hedge funds. It invests in the public equity, fixed income, real estate, currency, commodity, and alternative markets across the globe. The firm primarily invests in growth and value stocks of small-cap, mid-cap, SMID-cap, large-cap, and multi-cap companies. It also invests in dividend-paying equity securities. The firm invests in investment grade municipal securities, government securities including securities issued or guaranteed by a government or a government agency or instrumentality, corporate bonds, and asset-backed and mortgage-backed securities. It employs fundamental and quantitative analysis with a focus on bottom-up and top-down approach to make its investments. The firm employs liquidity, asset allocation, balanced, real estate, and alternative strategies to make its investments. In real estate sector, it seeks to invest in Poland and Germany. The firm benchmarks the performance of its portfolios against various S&P, Russell, Barclays, MSCI, Citigroup, and Merrill Lynch indices. BlackRock, Inc. was founded in 1988 and is based in New York, New York with additional offices in 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.

RMT Royce Micro-Cap Trust Inc. Common Stock

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

With a market capitalization of 3.12 trillion U.S. dollars as of May 2024, Microsoft was the world’s largest company that year. Rounding out the top five were some of the world’s most recognizable brands: Apple, NVIDIA, Google’s parent company Alphabet, and Amazon. Saudi Aramco led the ranking of the world's most profitable companies in 2023, with a pre-tax income of nearly 250 billion U.S. dollars. How are market value and market capitalization determined? Market value and market capitalization are two terms frequently used – and confused - when discussing the profitability and viability of companies. Strictly speaking, market capitalization (or market cap) is the worth of a company based on the total value of all their shares; an important metric when determining the comparative value of companies for trading opportunities. Accordingly, many stock exchanges such as the New York or London Stock Exchange release market capitalization data on their listed companies. On the other hand, market value technically refers to what a company is worth in a much broader context. It is determined by multiple factors, including profitability, corporate debt, and the market environment as a whole. In this sense it aims to estimate the overall value of a company, with share price only being one element. Market value is therefore useful for determining whether a company’s shares are over- or undervalued, and in arriving at a price if the company is to be sold. Such valuations are generally made on a case-by-case basis though, and not regularly reported. For this reason, market capitalization is often reported as market value. What are the top companies in the world? The answer to this question depends on the metric used. Although the largest company by market capitalization, Microsoft's global revenue did not manage to crack the top 20 companies. Rather, American multinational retailer Walmart was ranked as the largest company in the world by revenue. Walmart also had the highest number of employees in the world.

A table that shows the historical breakdown of the Debt Held by the Public, Intragovernmental Holdings and the Total Public Debt Outstanding.

Apollo Global Management, Inc. is a private equity firm specializing in investments in credit, private equity and real estate markets. The firm's private equity investments include traditional buyouts, recapitalization, distressed buyouts and debt investments in real estate, corporate partner buyouts, distressed asset, corporate carve-outs, middle market, growth capital, turnaround, bridge, corporate restructuring, special situation, acquisition, and industry consolidation transactions. The firm provides its services to endowment and sovereign wealth funds, as well as other institutional and individual investors. It manages client focused portfolios. The firm launches and manages hedge funds for its clients. It also manages real estate funds and private equity funds for its clients. The firm invests in the fixed income and alternative investment markets across the globe. Its fixed income investments include income-oriented senior loans, bonds, collateralized loan obligations, structured credit, opportunistic credit, non-performing loans, distressed debt, mezzanine debt, and value oriented fixed income securities. The firm seeks to invest in chemicals, commodities, consumer and retail, oil and gas, metals, mining, agriculture, commodities, distribution and transportation, financial and business services, manufacturing and industrial, media distribution, cable, entertainment and leisure, telecom, technology, natural resources, energy, packaging and materials, and satellite and wireless industries. It seeks to invest in companies based in across Africa, North America with a focus on United States, and Europe. The firm also makes investments outside North America, primarily in Western Europe and Asia. It employs a combination of contrarian, value, and distressed strategies to make its investments. The firm seeks to make investments in the range of $10 million and $1500 million. The firm seeks to invest in companies with Enterprise value between $750 million to $2500 million. The firm conducts an in-house research to create its investment portfolio. It seeks to acquire minority and majority positions in its portfolio companies. Apollo Global Management, Inc. was founded in 1990 and is headquartered in New York, New York with additional offices in North America, Asia and Europe.

A table that shows in detail by CUSIP, interest rate, the issue date, maturity date, interest payment dates and amounts outstanding for unmatured Bills, Notes, Bonds, Treasury Inflation-Protected Securities and Floating Rate Notes as of the last business day of the month.

A table that summarizes the amounts outstanding for all the securities issued by the Bureau of the Fiscal Service that makes up the Total Public Debt Outstanding amount.

Funds-From-Operation-To-Total-Debt Time Series for Bridgepoint Group Plc. Bridgepoint Group plc is a private equity and private credit firm specializing in middle market, lower mid-market, small mid cap, small cap, growth capital, buyouts investments, syndicate debt, infrastructure, direct lending and credit opportunities in private credit investments. It prefers to invest in advanced industrials, automation, agricultural sciences, energy transition enablers, business services, financial services, professional services, testing inspection and certification, information services, consumer, digital brands, video games, wellbeing products, health care, pharma and Med-Tech outsourced services, pharma products, and Med-Tech Products sectors. The firm prefers to invest in companies based in United Kingdom, New York and Nordic region. It prefers to make equity investment between 4.77 million pounds ($5.91 million) and 300 million euro ($348.8 million), for small mid cap between 40 million pounds ($49.54 million) and 125 pounds million ($154.82 million) and for small cap between 10 million pounds ($12.39 million) and 25 million pounds ($30.96 million), with enterprise value between 10 million euro ($10.74 million) and 1000 million euro ($1073.62 million). Bridgepoint Group plc was founded in 1985 and is based in London, United Kingdom with additional offices in North America, Asia and Europe. Bridgepoint Group plc formerly known as Bridgepoint Group Limited.

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.

River Mercantile UK Micro Cap (RMMC) Stock Forecast: A Tiny Seed, A Mighty Oak

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

JPMorgan Chase & Co. operates as a financial services company worldwide. It operates through four segments: Consumer & Community Banking (CCB), Corporate & Investment Bank (CIB), Commercial Banking (CB), and Asset & Wealth Management (AWM). The CCB segment offers s deposit, investment and lending products, payments, and services to consumers; lending, deposit, and cash management and payment solutions to small businesses; mortgage origination and servicing activities; residential mortgages and home equity loans; and credit card, auto loan, and leasing services. The CIB segment provides investment banking products and services, including corporate strategy and structure advisory, and equity and debt markets capital-raising services, as well as loan origination and syndication; payments and cross-border financing; and cash and derivative instruments, risk management solutions, prime brokerage, and research. This segment also offers securities services, including custody, fund accounting and administration, and securities lending products for asset managers, insurance companies, and public and private investment funds. The CB segment provides financial solutions, including lending, payments, investment banking, and asset management to small business, large and midsized companies, local governments, and nonprofit clients; and commercial real estate banking services to investors, developers, and owners of multifamily, office, retail, industrial, and affordable housing properties. The AWM segment offers multi-asset investment management solutions in equities, fixed income, alternatives, and money market funds to institutional clients and retail investors; and retirement products and services, brokerage, custody, trusts and estates, loans, mortgages, deposits, and investment management products. The company also provides ATM, online and mobile, and telephone banking services. JPMorgan Chase & Co. was founded in 1799 and is headquartered in New York, New York.