Gold rose to 3,354.76 USD/t.oz on July 11, 2025, up 0.92% from the previous day. Over the past month, Gold's price has fallen 0.92%, but it is still 39.14% 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.

The average monthly prices for gold increased worldwide between January 2014 and May 2025, although with some fluctuations. In January 2014, the average monthly price for gold worldwide stood at ******** nominal U.S. dollars per troy ounce. Significant jumps in the gold prices were observed, especially in the periods of uncertainty, as the investors tend to see gold as a safe investment option. For instance, the Corona pandemic acted as a shock to the economy, resulting in substantial increases in gold prices in 2020. As of May 2025, gold valued at ******** U.S. dollars per ounce, the highest value reported during this period.

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.

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

Dataset historical price data for XAU/USD (gold vs USD) from 2004 to Feb 2025, captured across multiple timeframes including 5-minute, 15-minute, 30-minute, 1-hour, 4-hour, daily, weekly, and monthly intervals. Dataset includes Open, High, Low, Close prices, and Volume data.

As of May 2025, the London (morning fixing) price of an ounce of gold cost an average of ******** U.S. dollars, a slight increase compared to the average monthly morning fixing price of ******** U.S. dollars per ounce in the previous month.

London fixing gold price In January 2020, the average price for an ounce of fine gold was ******** U.S. dollars. It increased to ******** U.S. dollars as of April 2022. Although the monthly price for fine gold fluctuates, the average annual price of fine gold is gradually increasing. In 2001, the price for one ounce of gold was *** U.S. dollars, and by 2012 the price had risen to some ***** U.S. dollars. By 2024, the annual average gold price was nearly ***** dollars per ounce. In that year, global gold demand reached ******* metric tons worldwide. Price determinants of fine gold Fine gold is considered to be almost pure gold, where the value of the metal depends on the percentage of fineness. Twenty-four-carat gold is considered fine gold (from 99.9 percent gold by mass and higher). The London Gold Fix acts as a benchmark for the price of gold. The price of gold is set by the members of the London Gold Market Fixing Ltd undertaken by Barclays and its other members. The price is determined twice per business day at 10:30 am and 3:00 pm based on the London bullion market to settle contracts within the bullion market. The price is based on the equilibrium point between supply and demand agreed upon by participating banks. Gold prices must remain flexible, and gold fixing provides an instantaneous price at specified times.

Gold prices in , June, 2025 For that commodity indicator, we provide data from January 1960 to June 2025. The average value during that period was 600.07 USD per troy ounce with a minimum of 34.94 USD per troy ounce in January 1970 and a maximum of 3352.66 USD per troy ounce in June 2025. | TheGlobalEconomy.com

Hong Kong Local Gold Prices data was reported at 11,422.000 HKD/Tael in Nov 2018. This records an increase from the previous number of 11,403.000 HKD/Tael for Oct 2018. Hong Kong Local Gold Prices data is updated monthly, averaging 3,611.000 HKD/Tael from Jan 1981 (Median) to Nov 2018, with 455 observations. The data reached an all-time high of 17,028.000 HKD/Tael in Aug 2011 and a record low of 2,213.000 HKD/Tael in Jun 1982. Hong Kong Local Gold Prices data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR – Table HK.P003: Gold and Silver Prices.

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.

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.

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

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.

Gold prices have dropped to a two-week low due to easing trade tensions. Despite the decline, gold remains a strong performer with a bullish outlook for the future.

Vietnam Gold Price Index: YoY: Hanoi data was reported at 3.370 % in Jun 2018. This records a decrease from the previous number of 4.000 % for May 2018. Vietnam Gold Price Index: YoY: Hanoi data is updated monthly, averaging 5.635 % from Aug 2008 (Median) to Jun 2018, with 118 observations. The data reached an all-time high of 71.470 % in Dec 2009 and a record low of -25.020 % in Jan 2014. Vietnam Gold Price Index: YoY: Hanoi data remains active status in CEIC and is reported by Hanoi Statistical Office. The data is categorized under Global Database’s Vietnam – Table VN.I030: Gold Price Index: MoM & YoY Growth.

Vietnam Gold Price Index: MoM: Hanoi data was reported at 0.400 % in Sep 2018. This records an increase from the previous number of -1.730 % for Aug 2018. Vietnam Gold Price Index: MoM: Hanoi data is updated monthly, averaging -0.045 % from Aug 2008 (Median) to Sep 2018, with 122 observations. The data reached an all-time high of 14.660 % in Sep 2011 and a record low of -8.090 % in Nov 2008. Vietnam Gold Price Index: MoM: Hanoi data remains active status in CEIC and is reported by Hanoi Statistical Office. The data is categorized under Global Database’s Vietnam – Table VN.T030:Table VN.I030: Gold Price Index: MoM & YoY Growth.

Get the latest insights on price movement and trend analysis of Gold in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).

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

EGPB - An Event-based Gold Price Benchmark Dataset

This benchmark dataset consists of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset.

Key variables & Features include:

• Previous gold prices

• Future gold prices with predictions for one day, one week, and one month

• Oil prices

• Standard & Poor's 500 Index (S&P 500)

• Dow Jones Industrial (DJI)

• US dollar index

• US treasury

• Inflation rate

• Consumer price index (CPI)

• Federal funds rate

• Silver prices

• Copper prices

• Iron prices

• Platinum prices

• Palladium prices

Additionally, the dataset considers global events that may impact gold prices, which were categorized into groups and collected from three distinct sources: the Al-Jazeera website spanning from 2022 to 2019, the Investing website spanning from 2018 to 2016, and the Yahoo Finance website spanning from 2007 to 2001.

These events data were then divided into multiple groups:

• Economic data

• Politics

• logistics

• Oil

• OPEC

• Dollar currency

• Sterling pound currency

• Russian ruble currency

• Yen currency

• Euro currency

• US stocks

• Global stocks

• Inflation

• Job reports

• Unemployment rates

• CPI rate

• Interest rates

• Bonds

These events were encoded using a numeric value, where 0 represented no events, 1 represented low events, 2 represented high events, 3 represented stable events, 4 represented unstable events, and 5 represented events that were observed during the day but had no effect on the dataset.

Cite this dataset: Farah Mansour and Wael Etaiwi, "EGPBD: An Event-based Gold Price Benchmark Dataset," 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Canary Islands, Spain, 2023, pp. 1-7, doi: 10.1109/ICECCME57830.2023.10252987.

@INPROCEEDINGS{10252987, author={Mansour, Farah and Etaiwi, Wael}, booktitle={2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)}, title={EGPBD: An Event-based Gold Price Benchmark Dataset}, year={2023}, volume={}, number={}, pages={1-7}, doi={10.1109/ICECCME57830.2023.10252987}}

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 Sri Lankan gold market was finally on the rise to reach $X in 2021, after three years of decline. Over the period under review, consumption saw a noticeable increase. Gold consumption peaked at $X in 2017; however, from 2018 to 2021, consumption remained at a lower figure.