Gold fell to 4,023.41 USD/t.oz on October 27, 2025, down 2.15% from the previous day. Over the past month, Gold's price has risen 4.96%, and is up 46.60% 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 October of 2025.

Gold Fields stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Explore insights from Market Research Intellect's Gold Invest Trading Platform Market Report, valued at USD 2.5 billion in 2024, expected to reach USD 5.8 billion by 2033 with a CAGR of 10.2% during 2026-2033.Uncover opportunities across demand patterns, technological innovations, and market leaders.

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

Gold prices have fallen sharply as global stock markets rally and the US-China trade war shows signs of easing, reducing the demand for gold as a safe haven.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

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

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Forecast: U.S. Gold Futures Trading Stocks in the US 2024 - 2028 Discover more data with ReportLinker!

Description for Kaggle Project

Title: Gold Price Prediction

Subtitle: Analysis and Forecasting Using Gold Price Data from Kaggle's goldstock.csv

Description This project aims to analyze and forecast gold prices using a comprehensive dataset spanning from January 19, 2014, to January 22, 2024. The dataset, sourced from Kaggle, includes daily gold prices with key financial metrics such as opening and closing prices, trading volume, and the highest and lowest prices recorded each trading day. Through this project, we perform time series analysis, develop predictive models, formulate and backtest trading strategies, and conduct market sentiment and statistical analyses.

Upload an Image - Choose a relevant image such as a graph of gold price trends, a gold bar, or an illustrative image related to financial data analysis.

Datasets - Source: Kaggle - File: goldstock.csv

Context, Sources, and Inspiration -Context: Understanding the dynamics of gold prices is crucial for investors and financial analysts. This project provides insights into historical price trends and equips users with tools to predict future prices. - Sources: The dataset is sourced from Kaggle and contains historical gold price data obtained from Nasdaq. Inspiration: The inspiration behind this project is to enable researchers, analysts, and data enthusiasts to make informed decisions, develop trading strategies, and contribute to a broader understanding of market behavior.

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: A Beacon of Precious Metal Value?

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

China Gold Intl Res stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

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

Graph and download economic data for CBOE Gold ETF Volatility Index (GVZCLS) from 2008-06-03 to 2025-10-24 about ETF, VIX, volatility, gold, stock market, and USA.

Historical AI model predictions and analysis for Gold (spot) stock across multiple timeframes and confidence levels

Centerra Gold stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

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.

New Found Gold (NFGC) - A Golden Opportunity or a Fool's Gold Rush?

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

Barrick Gold stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

According to Cognitive Market Research, the Global Gold Bullion Market size was USD 53154.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 12.60% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 21261.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.4%from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 15946.26 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 12225.47 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.6% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 2657.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.6%from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 1063.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.3% from 2024 to 2031.
The gold bars category is the fastest growing segment of the Gold Bullion industry

Market Dynamics of Gold Bullion Market

Key Drivers for Gold Bullion Market

Growing Interest In Safe-Haven Investments To Boost Market Growth

Concerns about inflation, geopolitical unrest, and economic instability are the main causes of the increased interest in safe-haven investments in the gold bullion market. Gold is seen as a trustworthy store of value by investors who are looking for stability during market turbulence. This tendency is further supported by central banks' growing gold reserves, which demonstrate their faith in gold as a hedge against exchange rate swings. Furthermore, it has become more accessible and appealing to a wider spectrum of investors due to the growth of digital gold and gold-backed investment products. This change emphasizes gold's continued allure as a hedge against volatile financial markets. For Instance, Agnico Eagle Mines Limited ("Agnico Eagle" or the "Company") and Kirkland Lake Gold Ltd. ("Kirkland Lake Gold") announced that they have entered into an agreement (the "Merger Agreement") to merge in a merger of equals (the "Merger"), with the combined company to continue under the name "Agnico Eagle Mines Limited" (the "Merger"). The merger will establish the new Agnico Eagle as the gold industry's highest-quality senior producer, with the lowest unit costs, largest profits, most favorable risk profile, and industry-leading best practices in key environmental, social, and governance ("ESG") categories.

Growing Demand In Emerging Markets For Gold To Drive Market Growth

An expanding middle class, rising wealth, and rising disposable incomes are driving the increased demand for gold in emerging nations. The consumption of jewellery and investments in gold bullion is rising significantly in nations with strong cultural ties to gold, such as China and India. Furthermore, these markets see gold as a safe-haven asset due to inflation worries and economic uncertainty. Participation in the gold market is further improved by the growth of financial literacy and the availability of gold investment products like ETFs and internet platforms. This pattern emphasizes how significant gold is in emerging economies as a representation of security and riches.

Restraint Factor for the Gold Bullion Market

Expenses for security and storage

Investors are quite concerned about the rising costs of storage and security in the gold bullion market. The price of securely storing and safeguarding actual gold rises in tandem with the demand for it. To protect their funds from loss or theft, investors need to account for costs associated with safe deposit boxes, insurance, and monitoring services. Regulations may also call for more stringent security measures, which would raise expenses even further. Potential investors may be put off by these costs, especially those with tighter budgets. They may instead choose alternative investment vehicles such as gold exchange-traded funds (ETFs), which don't need to be physically stored.

Limited Liquidity in Large Transactions

While gold is generally considered a liquid asse...

Barrick Gold reported $1.46B in Trade Creditors for its fiscal quarter ending in June of 2025. Data for Barrick Gold | ABX - Trade Creditors including historical, tables and charts were last updated by Trading Economics this last October in 2025.