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.

The Debt Ceiling: A ticking time bomb

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.

Debt Ceiling Crisis Averted, but Looms Large in Future

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.

The challenges surrounding the US debt ceiling could potentially damage the perception of the US dollar as a safe currency and have repercussions on emerging economies.

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

Government Debt in the United States decreased to 36213557 USD Million in April from 36214310 USD Million in March of 2025. This dataset provides - United States Government Debt- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

U.S. Government Closes in on Debt Ceiling Deadline

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 Federal Debt: Total Public Debt (GFDEBTN) from Q1 1966 to Q1 2025 about public, debt, federal, government, and USA.

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.

Evaluating the United States Credit Rating Amid the Debt Ceiling Debate

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 dataset is about companies. It has 1 row and is filtered where the company is Shell. It features 3 columns: debt, and market cap.

This scatter chart displays debt ($) against market cap ($). The data is filtered where the company is Shinko Shoji. The data is about companies.

China Market Cap of Depository Securities: Shenzhen SE: Securities Company Short-Term Debt data was reported at 0.000 RMB mn in 13 May 2025. This stayed constant from the previous number of 0.000 RMB mn for 12 May 2025. China Market Cap of Depository Securities: Shenzhen SE: Securities Company Short-Term Debt data is updated daily, averaging 2,019.000 RMB mn from Apr 2019 (Median) to 13 May 2025, with 1482 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 13 May 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.

This dataset is about companies. It has 1 row and is filtered where the company is Shinko Shoji. It features 3 columns: debt, and market cap.

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.

The infrastructure real estate investment trust (REIT) Prologis was the largest U.S. REIT as of October 31, 2024, with a market cap of almost 105 billion U.S. dollars. During this period, the debt to ratio of Prologis was 23.6 percent. The debt ratio measures the financial leverage of a company and is calculated as the total debt divided by the sum of implied market capitalization and total debt.

This scatter chart displays debt ($) against market cap ($). The data is filtered where the company is Nomura Research Institute. The data is about companies.

Blackstone Inc. is an alternative asset management firm specializing in real estate, private equity, hedge fund solutions, credit, secondary funds of funds, public debt and equity and multi-asset class strategies. The firm typically invests in early-stage companies. It also provide capital markets services. The real estate segment specializes in opportunistic, core+ investments as well as debt investment opportunities collateralized by commercial real estate, and stabilized income-oriented commercial real estate across North America, Europe and Asia. The firm's corporate private equity business pursues transactions throughout the world across a variety of transaction types, including large buyouts,special situations, distressed mortgage loans, mid-cap buyouts, buy and build platforms, which involves multiple acquisitions behind a single management team and platform, and growth equity/development projects involving significant majority stakes in portfolio companies and minority investments in operating companies, shipping, real estate, corporate or consumer loans, and alternative energy greenfield development projects in energy and power, property, dislocated markets, shipping opportunities, financial institution breakups, re-insurance, and improving freight mobility, financial services, healthcare, life sciences, enterprise tech and consumer, as well as consumer technologies. The firm considers investment in Asia and Latin America. It has a three year investment period. Its hedge fund business manages a broad range of commingled and customized fund solutions and its credit business focuses on loans, and securities of non-investment grade companies spread across the capital structure including senior debt, subordinated debt, preferred stock and common equity. Blackstone Inc. was founded in 1985 and is headquartered in New York, New York with additional offices across Asia, Europe and North America.

This scatter chart displays market cap ($) against debt ($). The data is filtered where the company is Shell. The data is about companies.

The Goldman Sachs Group, Inc., a financial institution, provides a range of financial services for corporations, financial institutions, governments, and individuals worldwide. It operates through four segments: Investment Banking, Global Markets, Asset Management, and Consumer & Wealth Management. The company's Investment Banking segment provides financial advisory services, including strategic advisory assignments related to mergers and acquisitions, divestitures, corporate defense activities, restructurings, and spin-offs; and middle-market lending, relationship lending, and acquisition financing, as well as transaction banking services. This segment also offers underwriting services, such as equity underwriting for common and preferred stock and convertible and exchangeable securities; and debt underwriting for various types of debt instruments, including investment-grade and high-yield debt, bank and bridge loans, and emerging-and growth-market debt, as well as originates structured securities. Its Global Markets segment is involved in client execution activities for cash and derivative instruments; credit and interest rate products; and provision of equity intermediation and equity financing, clearing, settlement, and custody services, as well as mortgages, currencies, commodities, and equities related products. The company's Asset Management segment manages assets across various classes, including equity, fixed income, hedge funds, credit funds, private equity, real estate, currencies, and commodities; and provides customized investment advisory solutions, as well as invests in corporate, real estate, and infrastructure entities. Its Consumer & Wealth Management segment offers wealth advisory and banking services, including financial planning, investment management, deposit taking, and lending; private banking; and unsecured loans, as well as accepts saving and time deposits. The company was founded in 1869 and is headquartered in New York, New York.

The post-COVID-19 era presents a looming threat of global debt, elevating concerns regarding sovereign credit ratings worldwide. This study develops a new index system, divides the rating variables into long- and short-term factors, performs rating fitting and prediction, and investigates the fairness of China and relevant countries. Our findings reveal that sovereign credit ratings have a deterrent effect on the global financial market due to the ceiling effect and quasi-public goods characteristics. A high and stable credit rating demands long-term enhancements in economic fundamentals, budget balances, external surpluses, and overall solvency. Concurrently, effective short-term debt management strategies, including reduction, repayment, and swaps, are essential. Moreover, we introduce the concept of a "rating gap" to assess rating fairness, revealing both undervaluation and overvaluation among countries. Notably, China’s sovereign rating was underestimated between 2009 and 2011 and overestimated between 2013 and 2016. These findings underscore the criticality of government vigilance in monitoring sovereign debt and credit ratings to navigate potential post-COVID-19 sovereign debt crises.

Historical ltm Net Debt to FCF metric data for Applied Materials, Inc. (AMAT).

This scatter chart displays market cap ($) against debt ($). The data is filtered where the company is HERA. The data is about companies.