Between January 1971 and May 2025, gold had average annual returns of **** percent, which was only slightly more than the return of commodities, with an annual average of around eight percent. The annual return of gold was over ** percent in 2024. What is the total global demand for gold? The global demand for gold remains robust owing to its historical importance, financial stability, and cultural appeal. During economic uncertainty, investors look for a safe haven, while emerging markets fuel jewelry demand. A distinct contrast transpired during COVID-19, when the global demand for gold experienced a sharp decline in 2020 owing to a reduction in consumer spending. However, the subsequent years saw an increase in demand for the precious metal. How much gold is produced worldwide? The production of gold depends mainly on geological formations, market demand, and the cost of production. These factors have a significant impact on the discovery, extraction, and economic viability of gold mining operations worldwide. In 2024, the worldwide production of gold was expected to reach *** million ounces, and it is anticipated that the rate of growth will increase as exploration technologies improve, gold prices rise, and mining practices improve.

As of 31 May 2025, MSCI U.S. had an average **-year return rate of ***** percent, whereas gold had a return rate of ***** percent. Gold mining overview In light of recent technological advancements shaping the gold mining market, global gold production has been rather stable in the last few years, hovering around ***** metric tons since 2020. Among nations, Australia holds the highest gold production, surpassing countries with the highest mine gold reserves. Gold as a financial security Known for its ability to provide diversification to investment portfolios, gold has exhibited a positive trend in its Gold’s return rate was particularly high in the early 2000s, and, despite experiencing a decline during the pandemic, it demonstrated a remarkable recovery since. Furthermore, gold serves as a valuable asset for a nation's economic stability, with the United States holding the highest amount of

Gold is the most popular precious metal in the investment industry. The rate of return for gold investments fluctuated significantly during the period from 2002 to 2024 but generated positive returns in most years of the observed period. The return of gold as an investment reached almost ** percent in 2024, one of the highest recorded. Why is gold valuable? Gold is a precious metal with several practical uses, particularly in technology. For example, NASA uses gold to improve its lasers and protect sensitive things in space, including a part of the visor for its astronauts. However, a large share of the demand for gold worldwide is as an investment, particularly by central banks. Gold serves the purpose of an alternative to currency because it is relatively scarce but still has enough mine production to serve the financial sector. Gold as an investment Under the Bretton Woods agreement after World War II, the world’s major currencies were tied to the value of gold. This system, called the Gold Standard, ended in 1971. Still, most countries maintain significant gold reserves. Due to this history and the overall faith in the value of gold, the average gold price tends to increase in times of recession, making it an attractive investment in uncertain times.

As of 31 May 2025, gold had an average **-year return rate of ***** percent, which was slightly above than U.S. stocks with a rate of ***** percent.

Gold rose to 3,327.32 USD/t.oz on July 4, 2025, up 0.03% from the previous day. Over the past month, Gold's price has fallen 0.76%, but it is still 39.17% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on July of 2025.

Interactive chart of historical data for real (inflation-adjusted) gold prices per ounce back to 1915. The series is deflated using the headline Consumer Price Index (CPI) with the most recent month as the base. The current month is updated on an hourly basis with today's latest value.

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

In 2023, the rate of return on gold was 13.1 percent, making gold the leading commodity based on return rate in that year. Natural resources like any other investments exhibit a wide range of fluctuations over time.

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

Dataset of historical annual gold prices from 1970 to 2024, including significant events and acts that impacted gold prices.

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

Worldwide gold demand amounted to ******* metric tons in 2024, an increase from ******* metric tons in the previous year. Furthermore, 2020 was the first time demand for gold was lower than ***** metric tons throughout the period considered, and the driving force behind that drop was the coronavirus pandemic. Gold supply The supply of gold depends largely on the mine production of gold. Production, in turn, depends on two factors. The countries with higher reserves of gold work harder to extract their gold when the price of gold increases, following the standard theory that the quantity supplied increases with price. Similarly, the expectation of higher prices in the future prompts speculators to explore for new reserves. As new lodes are discovered, the supply of gold increases. Investments in gold Gold is subject to cyclical volatility in its rate of return, and many investors speculate on its value. However, for historic reasons, many view it as a symbol of price stability. After World War II, the Bretton Woods system tied the price of all major currencies to the price of gold until the 1970s. This legacy means that most countries still maintain large gold reserves. While this can drive gold demand, it also reduces the supply of gold in circulation by locking huge amounts of gold in central bank vaults. Gold demand was noticeably higher following the Financial Crisis, until the coronavirus pandemic hit. Many investors look to gold in periods of market turmoil because they believe that it holds value through recessions better than other assets.

As of April 2024, WisdomTree Core Physical Gold was the leading gold back exchange-traded commodity (ETC) listed on the London stock exchange, providing a return of 13 percent on euro investments annually. Invesco Physical Gold A followed closely in second place, providing a return of 12.86 percent on investments made in euros. What is an exchange-traded commodity? An exchange-traded commodity (ETC) is a commodity such as silver, wheat, oats, and gold traded on the stock exchange. Unlike exchange-traded funds (ETFs) which allows investment in a basket of securities, ETCs allow investment in a single commodity. Gold-backed ETCs aim to track the spot price of gold. This results in the price of the ETC moving up and down in correlation with the underlying gold price. The annual return rate The return on investment (ROI) is a way to measure the performance of an investment. The ROI is calculated by dividing the amount gained or lost from an investment by the original invested amount. This number is then represented as a percentage. Different gains and losses can be generated on foreign investments due to changes in the value of the security in foreign markets. If the local home currency of an investor is rising in value, this leads to lower returns on foreign investments. Similarly, a decreasing home currency will increase the returns on foreign investments. The difference in currency performance, inflation levels in the home market or abroad, and interest rates are all factors that can lead to differing ROI rates.

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

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

In 2024, one troy ounce of gold had an annual average price of ******** U.S. dollars. Gold pricing determinants Gold is a metal that is considered malleable, ductile, and is known for its bright lustrous yellow color. This transition metal is highly valued as a precious metal for its use in coins, jewelry, and in investments. Gold was also once used as a standard for monetary policies between different countries. The price of gold is determined by daily fixings where participants agree to buy or sell at a set price or to maintain the price through supply and demand control. For gold, companies like Barclays Capital, Scotia-Mocatta, Sociétè Générale, HSBC, and Deutsche Bank are members in gold fixing at the London Bullion Market Association.

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

The fastest growing exchange traded fund (ETF) for precious metals over the preceding three months was the ProShares UltraShort Gold as of Sep 27, 2022. This ETF grew by over 20 percent during this period. However, this fund is a short selling fund, meaning the growth was made by betting against the price of gold. In fact, the only precious metals ETFs to make positive returns during this three-month period were funds intended to shorted the price of their underlying commodity. Precious metals ETFs are exchanged traded funds that generally purchase and hold bullion of one or more precious metals, although they may also invest in other financial assets such as futures or the shares in companies that mine precious metals. This enables consumers to invest in precious metals without the high cost of entry to needed purchase and store physical bullion.

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: Tracking Precious Metals Performance?

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.

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