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.

Gold rose to 3,362.51 USD/t.oz on August 1, 2025, up 2.25% from the previous day. Over the past month, Gold's price has risen 0.15%, and is up 37.65% compared to the same time last year, 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 August of 2025.

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.

Monthly gold prices in USD since 1833 (sourced from the World Gold Council). The data is derived from historical records compiled by Timothy Green and supplemented by data provided by the World Bank...

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

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.

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

The price of gold per troy ounce increased considerably between 1990 and 2025, despite some fluctuations. A troy ounce is the international common unit of weight used for precious metals and is approximately **** grams. At the end of 2024, a troy ounce of gold cost ******* U.S. dollars. As of * June 2025, it increased considerably to ******** U.S. dollars. Price of – additional information In 2000, the price of gold was at its lowest since 1990, with a troy ounce of gold costing ***** U.S. dollars in that year. Since then, gold prices have been rising and after the economic crisis of 2008, the price of gold rose at higher rates than ever before as the market began to see gold as an increasingly good investment. History has shown, gold is seen as a good investment in times of uncertainty because it can or is thought to function as a good store of value against a declining currency as well as providing protection against inflation. However, unlike other commodities, once gold is mined it does not get used up like other commodities (for example, such as gasoline). So while gold may be a good investment at times, the supply demand argument does not apply to gold. Nonetheless, the demand for gold has been mostly consistent.

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

Gold Bullion Market Outlook

According to our latest research, the global gold bullion market size reached USD 248.5 billion in 2024, and it is expected to grow at a CAGR of 4.7% during the forecast period, reaching approximately USD 373.4 billion by 2033. This healthy growth trajectory is primarily attributed to the increasing demand for safe-haven assets amid global economic uncertainties, rising geopolitical tensions, and a persistent appetite for portfolio diversification among both institutional and individual investors. The gold bullion market continues to benefit from its reputation as a reliable store of value, particularly during periods of inflation and currency depreciation, as per our comprehensive market analysis for 2025.

One of the most significant growth factors for the gold bullion market is the heightened volatility and uncertainty in global financial markets. Investors, both retail and institutional, are increasingly turning towards gold bullion as a hedge against inflation, currency fluctuations, and geopolitical risks. The persistent low-interest-rate environment, coupled with concerns over sovereign debt and fiscal imbalances in major economies, has further fueled the demand for physical gold. Central banks, especially in emerging markets, have been augmenting their gold reserves to diversify away from the US dollar and other fiat currencies, providing a strong and sustained impetus to the gold bullion market.

Another key driver propelling the gold bullion market is the growing accessibility and innovation in distribution channels. The proliferation of online platforms and digital gold investment products has democratized access to gold bullion, enabling a broader base of individual investors to participate in the market. This trend is further amplified by the introduction of fractional gold ownership, secure storage solutions, and transparent pricing mechanisms, which have collectively enhanced investor confidence and convenience. Additionally, the rise of gold-backed exchange-traded funds (ETFs) and other financial instruments has expanded the avenues for gold investment, reinforcing the market’s growth momentum.

Sustainability and ethical sourcing concerns are also shaping the gold bullion market landscape. Increasing awareness about responsible mining practices and the environmental and social impact of gold extraction has led to the emergence of certified, conflict-free bullion products. Regulatory initiatives and industry-led standards, such as the London Bullion Market Association (LBMA) Responsible Gold Guidance, are driving transparency and traceability across the supply chain. These developments are not only addressing investor concerns but also attracting a new segment of environmentally and socially conscious buyers, further supporting market expansion.

From a regional perspective, the Asia Pacific region remains the dominant force in the gold bullion market, driven by robust demand in countries like China and India, where gold holds deep cultural and economic significance. North America and Europe also represent substantial market shares, supported by strong institutional investment and central bank activity. Meanwhile, the Middle East & Africa and Latin America are emerging as important markets, buoyed by rising wealth levels, favorable regulatory environments, and increasing financial inclusion. The regional diversity in demand drivers underscores the global appeal and resilience of the gold bullion market.

Product Type Analysis

The gold bullion market is segmented by product type into bars, coins, rounds, and others, each catering to distinct investor preferences and use cases. Gold bars, often regarded as the standard investment vehicle for institutional buyers and high-net-worth individuals, account for the largest share of the market. Their appeal lies in their high purity, lower premiums over spot prices, and ease of storage and transport, making them the preferred choice for those seeking to make substantial investments in physical

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.

Gold Price Outlook: S&P GSCI Gold index Faces Volatility Amidst Economic Uncertainties

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

Gold has been used extensively for savings, investments and consumption since ages;however the importance of the gold as an investment instruments has been much talked in the recenttimes. This research paper intends to find various applications of gold portfolios as an alternate assetclass: the benefits of including gold to an investment portfolio have been analyzed. The results indicatethat gold has performed significantly better than other assets like debt and equity in both emerging andUS markets. It was noted that addition of gold to portfolios helped reduce the volatility and increaseoverall returns during the period 2009-12. For example, in 2008, when the U.S. equity market plunged to36.99%, gold in fact showed returns of 5.8%. It is also observed that the inverse correlation existsbetween the dollar index and the gold prices helped reduce the portfolio risk as a result of diversification.

In 2024, the rate of return on gold was 26.62 percent, making gold the leading commodity based on return rate in that year. Natural resources, like any other investment, 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 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.

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

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 ** percent on euro investments annually. Invesco Physical Gold A followed closely in second place, providing a return of ***** 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.

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.