Gold Resource debt/equity ratio from 2010 to 2025. Debt/equity ratio can be defined as a measure of a company's financial leverage calculated by dividing its long-term debt by stockholders' equity.

Sandstorm Gold debt/equity ratio from 2013 to 2025. Debt/equity ratio can be defined as a measure of a company's financial leverage calculated by dividing its long-term debt by stockholders' equity.

Osisko Gold Royalties debt/equity ratio from 2015 to 2025. Debt/equity ratio can be defined as a measure of a company's financial leverage calculated by dividing its long-term debt by stockholders' equity.

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.

Machine Learning Models for Gold Price Prediction

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

Colombia CO: External Debt: Debt Service: % of Exports data was reported at 18.047 % in 2023. This records an increase from the previous number of 11.155 % for 2022. Colombia CO: External Debt: Debt Service: % of Exports data is updated yearly, averaging 18.191 % from Dec 1970 (Median) to 2023, with 54 observations. The data reached an all-time high of 39.950 % in 1988 and a record low of 5.932 % in 2013. Colombia CO: External Debt: Debt Service: % of Exports data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: External Debt: Debt Outstanding, Debt Ratio and Debt Service. Debt service, the sum of principal repayments and interest actually paid in currency, goods, or services, is expressed as a percentage of exports of goods and services--all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. This series differs from the standard debt to exports series in that it covers only long-term public and publicly guaranteed debt and repayments (repurchases and charges) to the IMF.;World Bank, International Debt Statistics.;;

Comoros KM: External Debt: Debt Service: % of Exports data was reported at 11.554 % in 2023. This records an increase from the previous number of 2.726 % for 2022. Comoros KM: External Debt: Debt Service: % of Exports data is updated yearly, averaging 5.403 % from Dec 1980 (Median) to 2023, with 37 observations. The data reached an all-time high of 33.795 % in 2007 and a record low of 0.540 % in 2013. Comoros KM: External Debt: Debt Service: % of Exports data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Comoros – Table KM.World Bank.WDI: External Debt: Debt Outstanding, Debt Ratio and Debt Service. Debt service, the sum of principal repayments and interest actually paid in currency, goods, or services, is expressed as a percentage of exports of goods and services--all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. This series differs from the standard debt to exports series in that it covers only long-term public and publicly guaranteed debt and repayments (repurchases and charges) to the IMF.;World Bank, International Debt Statistics.;;

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.

How does stagflation affect gold prices?

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.

SolGold (SOLG): Digging for Gold or a Digger's Folly?

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

Brazil BR: External Debt: Debt Service: % of Exports data was reported at 7.652 % in 2023. This records an increase from the previous number of 7.404 % for 2022. Brazil BR: External Debt: Debt Service: % of Exports data is updated yearly, averaging 19.577 % from Dec 1975 (Median) to 2023, with 49 observations. The data reached an all-time high of 44.244 % in 1982 and a record low of 4.067 % in 2012. Brazil BR: External Debt: Debt Service: % of Exports data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: External Debt: Debt Outstanding, Debt Ratio and Debt Service. Debt service, the sum of principal repayments and interest actually paid in currency, goods, or services, is expressed as a percentage of exports of goods and services--all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. This series differs from the standard debt to exports series in that it covers only long-term public and publicly guaranteed debt and repayments (repurchases and charges) to the IMF.;World Bank, International Debt Statistics.;;

Costa Rica CR: External Debt: Debt Service: % of Exports data was reported at 7.772 % in 2023. This records an increase from the previous number of 3.980 % for 2022. Costa Rica CR: External Debt: Debt Service: % of Exports data is updated yearly, averaging 9.283 % from Dec 1977 (Median) to 2023, with 47 observations. The data reached an all-time high of 55.469 % in 1983 and a record low of 3.319 % in 2007. Costa Rica CR: External Debt: Debt Service: % of Exports data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: External Debt: Debt Outstanding, Debt Ratio and Debt Service. Debt service, the sum of principal repayments and interest actually paid in currency, goods, or services, is expressed as a percentage of exports of goods and services--all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. This series differs from the standard debt to exports series in that it covers only long-term public and publicly guaranteed debt and repayments (repurchases and charges) to the IMF.;World Bank, International Debt Statistics.;;

Centerra Gold debt/equity ratio from 2018 to 2025. Debt/equity ratio can be defined as a measure of a company's financial leverage calculated by dividing its long-term debt by stockholders' equity.

Iraq External Debt: Debt Service: % of Exports data was reported at 3.936 % in 2023. This records an increase from the previous number of 3.564 % for 2022. Iraq External Debt: Debt Service: % of Exports data is updated yearly, averaging 3.325 % from Dec 2015 (Median) to 2023, with 9 observations. The data reached an all-time high of 7.798 % in 2020 and a record low of 2.137 % in 2018. Iraq External Debt: Debt Service: % of Exports data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iraq – Table IQ.World Bank.WDI: External Debt: Debt Outstanding, Debt Ratio and Debt Service. Debt service, the sum of principal repayments and interest actually paid in currency, goods, or services, is expressed as a percentage of exports of goods and services--all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. This series differs from the standard debt to exports series in that it covers only long-term public and publicly guaranteed debt and repayments (repurchases and charges) to the IMF.;World Bank, International Debt Statistics.;;

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.

IAU:TSX i-80 Gold Corp.

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

Vista Gold debt/equity ratio from 2010 to 2025. Debt/equity ratio can be defined as a measure of a company's financial leverage calculated by dividing its long-term debt by stockholders' equity.

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.

Gold: A Brighter Future Ahead?

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.

Gold Rush: Price of Gold Set to Hit $2,100 by Year-End

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.

Philadelphia Gold and Silver Index: The Future of Precious Metals?

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.

What happens to gold if CPI increases?

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

Burkina Faso BF: External Debt: Debt Service: % of Exports data was reported at 4.134 % in 2023. This records an increase from the previous number of 3.435 % for 2022. Burkina Faso BF: External Debt: Debt Service: % of Exports data is updated yearly, averaging 5.501 % from Dec 1974 (Median) to 2023, with 45 observations. The data reached an all-time high of 18.448 % in 2000 and a record low of 2.135 % in 2012. Burkina Faso BF: External Debt: Debt Service: % of Exports data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burkina Faso – Table BF.World Bank.WDI: External Debt: Debt Outstanding, Debt Ratio and Debt Service. Debt service, the sum of principal repayments and interest actually paid in currency, goods, or services, is expressed as a percentage of exports of goods and services--all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. This series differs from the standard debt to exports series in that it covers only long-term public and publicly guaranteed debt and repayments (repurchases and charges) to the IMF.;World Bank, International Debt Statistics.;;

Bolivia BO: External Debt: Debt Service: % of Exports data was reported at 12.596 % in 2023. This records an increase from the previous number of 12.474 % for 2022. Bolivia BO: External Debt: Debt Service: % of Exports data is updated yearly, averaging 18.381 % from Dec 1976 (Median) to 2023, with 48 observations. The data reached an all-time high of 52.579 % in 1988 and a record low of 2.206 % in 2013. Bolivia BO: External Debt: Debt Service: % of Exports data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bolivia – Table BO.World Bank.WDI: External Debt: Debt Outstanding, Debt Ratio and Debt Service. Debt service, the sum of principal repayments and interest actually paid in currency, goods, or services, is expressed as a percentage of exports of goods and services--all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. This series differs from the standard debt to exports series in that it covers only long-term public and publicly guaranteed debt and repayments (repurchases and charges) to the IMF.;World Bank, International Debt Statistics.;;