Government Debt in the United States increased to 36215818 USD Million in May from 36213557 USD Million in April 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

The United States recorded a Government Debt to GDP of 124.30 percent of the country's Gross Domestic Product in 2024. This dataset provides - United States Government Debt To GDP - 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.

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

This poll, fielded July of 2011 and the first of two, is a part of a continuing series of monthly surveys that solicits public opinion on a range of political and social issues. Respondents were asked for their opinions of the debt ceiling debate, including how the debate was being handled by President Obama, the Democrats in Congress, and the Republicans in Congress, as well as who should compromise on some of their positions in order to come to an agreement. Further questions included whether raising the debt ceiling would impact Social Security, Medicare, or payments made to veterans. Demographic variables include sex, age, race, education level, household income, religious preference, type of residential area (e.g., urban or rural), 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.

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.

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 poll, the first of two fielded December 2012, is part of a continuing series of monthly surveys that solicits public opinion on a range of political and social issues. Respondents were asked how well Barack Obama was handling the presidency on issues such as foreign policy, the economy, terrorism, taxes, and the budget deficit. Opinions were collected on the progress of the economy, the job market, the performance of Congress, feelings toward Washington, and whether the country was heading in the right direction. The respondents were also asked for their opinions of the Republican and Democratic parties, Barack Obama, John Boehner, Hillary Clinton, and the difficulty in reaching agreements and passing legislation in Congress. Data were collected on tax increases and spending cuts, expectations of the negotiations between the two parties, the preferred plan for reducing the budget deficit, and whether the respondents approved of Congress raising the federal debt ceiling. Respondents were also asked their views about illegal immigrants, ongoing violence in Syria, gun control laws, the recent shooting at Sandy Hook Elementary School and safety of schools, holiday shopping, and New Year's Eve plans. Additional topics included the worst date movie, the least interesting movie, the most difficult job in Hollywood, expected changes to the Oscars broadcast, quintessential actor and actress, and preference of a great movie over a powerful documentary. Demographic information includes sex, age, race, marital status, education level, household income, religious preference, type of residential area (e.g., urban or rural), political party affiliation, political philosophy, voting behavior, whether respondents were registered to vote, and whether respondents thought of themselves as born-again Christians.

Hannon Armstrong Sustnbl Infrstr Cap reported $4.72B in Debt for its fiscal quarter ending in March of 2025. Data for Hannon Armstrong Sustnbl Infrstr Cap | HASI - Debt including historical, tables and charts were last updated by Trading Economics this last June in 2025.

Cap Gemini reported EUR6.6B in Debt for its fiscal semester ending in June of 2024. Data for Cap Gemini | CAP - Debt including historical, tables and charts were last updated by Trading Economics this last June in 2025.

Hannon Armstrong Sustnbl Infrstr Cap reported 180.49M in Interest Expense on Debt for its fiscal quarter ending in December of 2024. Data for Hannon Armstrong Sustnbl Infrstr Cap | HASI - Interest Expense On Debt including historical, tables and charts were last updated by Trading Economics this last June in 2025.

Cap Gemini reported EUR46M in Interest Expense on Debt for its fiscal semester ending in June of 2024. Data for Cap Gemini | CAP - Interest Expense On Debt including historical, tables and charts were last updated by Trading Economics this last June in 2025.