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 27 percent in 2024, one of the highest recorded. Why is gold valuable? Gold is a precious metal with a number of 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.

Between January 1971 and March 2024, gold had average annual returns of 7.98 percent, which was only slightly behind the return of commodities, with an annual average of eight percent. The annual average return of gold in 2023 was 13.1 percent. 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 124 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 December 2024, U.S. stocks had an average 10-year return rate of 13.1 percent, whereas gold had a return rate of 8.33 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 3,000 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

As of December 2024, gold had an average 20-year return rate of 9.47 percent, which was one percent below U.S. stocks with a rate of 10.27 percent.

Gold increased 393.93 USD/t oz. or 15.01% since the beginning of 2025, 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 March of 2025.

Historical dataset of nominal and 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.

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 dataset provides predicted prices, return on investment (ROI), and sentiment analysis for Tether Gold over a series of dates. The dataset includes predictions for Tether Gold's price, with low, average, and high values, as well as ROI figures for each predicted date. This data helps users forecast market trends for Tether Gold and make informed trading decisions.

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

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.

In 2023, one troy ounce of gold had an annual average price of 1,943.08 U.S. dollars. Gold market and industry From 2012 to 2018, the annual average gold price dropped from 1,668.98 U.S. dollars per troy ounce to 1,268.49 U.S. dollars per troy ounce, with a slight growth to approximately 1,400 U.S. dollars per troy ounce in 2019. In 2020 the figure rebounded to a record 1,769.64 U.S. dollars per ounce. The average price has grown significantly from the early 2000s with an annual average price of 279 U.S. dollars per troy ounce in 2001 up to the record high in 2020. Similarly, the costs of mining gold have risen considerably in recent years. In 2005, mining costs totaled 280 U.S. dollars per troy ounce and increased to 566 U.S. dollars per troy ounce in 2010. As of 2022, for example, Canadian company Barrick Gold reported an average realized gold price of 1,795 dollars per ounce. 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.

Is Gold Still the Safe Haven? S&P GSCI Gold Index

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

Long term dataset showing the annual percentage change of the NASDAQ Composite Index back to 1972. Performance is calculated as the % change from the last trading day of each year from the last trading day of the previous year.

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.

Uranium Energy (YCA): The Sun's Yellow Cake, or Just a Fool's Gold Investment?

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

Credit report of Return Gold Rk contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Credit report of Return Gold Intl Co Ltd contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

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.

Americas Gold Forecasts Positive Trajectory for Silver Shares (USAS)

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 emergence of the covid-19 health crisis, in this advanced technological era where connections between markets, nations, and economies have grown stronger than ever before, the shock of the COVID-19 pandemic quickly had an impact on both physical and digital financial assets. The Chinese financial market experienced the first consequences of the covid-19 pandemic, then spilled over to other financial markets, including those for cryptocurrencies and the precious metals. This study examines the impact of the covid-19 pandemic on the volatilities of the dynamics of bitcoin and gold. Both assets share some characteristics, such as online trading platforms, however, gold is a tangible financial asset unlike bitcoin, which is digitally generated without any physical form. This study argues that the similarities and differences between bitcoin and gold play major roles in how the covid-19 crisis affected their respective dynamics. Using daily data ranging from 9/22/2014 to 1/31/2023 and employing ARMA as the mean equation for GARCH model, the impact of the health crisis (covid-19) is examined on the volatilities of the prices and volumes of bitcoin and gold. Empirical evidence points out that, the pandemic has a symmetric impact on the volatilities of bitcoin and gold price returns, causing them to be more volatile. The impact of the covid-19 observed on the volume returns of the assets, however, is asymmetrical. The empirical results give evidence to the role that the vital differences existing between these assets played during the covid-19 pandemic.

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 Safe Haven in a Turbulent Market

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 Aoril 2024, DB Gold Double Short Exchange Traded Note was the top-performing gold ETF, providing a one-year return rate of around 22 percent. The next highest return came from ProShares UltraShort Gold, generating a one-year return rate of approximately 18.7 percent.