Of the five largest copper mining companies, U.S. mining company Freeport-McMoRan saw the largest growth in its share price over recent years. Between January 2018 and March 2024, the share price of the Phoenix-based company saw its share price increase by over *** percent. Conversely, over this period, Canada-based mining and metals company First Quantum Minerals saw slight decreases in their share price.

Copper rose to 4.46 USD/Lbs on August 12, 2025, up 0.41% from the previous day. Over the past month, Copper's price has fallen 18.86%, but it is still 9.99% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Copper - values, historical data, forecasts and news - updated on August of 2025.

Ero Copper 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.

Copperindex: The Future of Copper Pricing?

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.

Is Copper's Index a Reliable Guide?

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

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

Indonesia Mining Production: Stock: Value: Copper data was reported at 67,015.072 IDR bn in 2015. This records an increase from the previous number of 38,510.430 IDR bn for 2014. Indonesia Mining Production: Stock: Value: Copper data is updated yearly, averaging 36,422.868 IDR bn from Dec 1999 (Median) to 2015, with 17 observations. The data reached an all-time high of 97,709.947 IDR bn in 2006 and a record low of 199.475 IDR bn in 2000. Indonesia Mining Production: Stock: Value: Copper data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Mining and Manufacturing Sector – Table ID.BAE003: Mining Production: Stock.

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.

M.A.C. Copper Shares Could See Upswing, Analysts Say (MTAL)

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

Capstone Copper Corp stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Understanding copper metal stocks is crucial for gauging supply-demand dynamics and potential price movements. This article explains the sources of copper metal stocks, their importance, and the monitoring process, providing valuable insights for investors, traders, and policymakers.

In June 2025, the average monthly price for copper stood at over ***** U.S. dollars per metric ton. This is down from a monthly high exceeding ****** U.S. dollars in March 2024, which was among the highest monthly values observed in the past decade.

Investing in TSX copper stocks allows investors to gain exposure to the copper industry and potentially benefit from the demand and price fluctuations of copper in the global market. Learn about the advantages, including potential capital appreciation, diversification opportunities, and potential dividend income. However, it's important to understand the risks and conduct thorough research before investing.

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.

Southern Copper (SCCO) - A Copper Bottom?

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.

Copperindex: The Key to Understanding Copper's Future?

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

MAC Copper PE ratio as of June 28, 2025 is 0.00. Current and historical p/e ratio for MAC Copper (MTAL) from 2022 to 2024. The price to earnings ratio is calculated by taking the latest closing price and dividing it by the most recent earnings per share (EPS) number. The PE ratio is a simple way to assess whether a stock is over or under valued and is the most widely used valuation measure. Please refer to the Stock Price Adjustment Guide for more information on our historical 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.

Copper in Focus as TR/CC CRB Copper Index Outlook Matures

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

Southern Copper reported $1.01B in Stock for its fiscal quarter ending in June of 2025. Data for Southern Copper | SCCO - Stock including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Copper-Core Stock-Pot Market Outlook

According to our latest research, the global copper-core stock-pot market size reached USD 1.24 billion in 2024, with a robust compound annual growth rate (CAGR) of 6.8% observed over the past few years. The market is projected to expand further, reaching a forecasted value of USD 2.19 billion by 2033, driven by rising culinary awareness, increasing demand for high-performance cookware, and the growing trend of home cooking. This growth is fueled by the superior heat conductivity and durability of copper-core stock-pots, making them a preferred choice among both residential and commercial users.

One of the primary growth factors for the copper-core stock-pot market is the increasing consumer preference for premium cookware that ensures even heat distribution and energy efficiency. Copper-core technology, which typically involves a layer of copper sandwiched between stainless steel or aluminum, offers rapid and uniform heating, reducing cooking time and improving food quality. As consumers become more health-conscious and seek to prepare meals at home, the demand for reliable and efficient cookware has surged. This trend is particularly pronounced in urban areas, where busy lifestyles necessitate kitchen tools that deliver consistent results quickly and safely.

Another significant driver is the expansion of the foodservice industry, which includes restaurants, hotels, and catering services. Commercial kitchens require durable and high-performing cookware that can withstand heavy usage and frequent cleaning. Copper-core stock-pots meet these requirements, offering longevity and superior cooking performance. Additionally, the rise in culinary arts education and the popularity of cooking shows have inspired both professionals and home cooks to invest in premium cookware. Manufacturers are responding by introducing innovative designs and multi-functional stock-pots that cater to diverse cooking needs, further stimulating market growth.

Technological advancements and product innovations have also played a pivotal role in shaping the copper-core stock-pot market. The integration of nonstick coatings, ergonomic handles, and induction compatibility has broadened the appeal of these products. Furthermore, the increasing penetration of e-commerce and online retail platforms has made it easier for consumers to access a wide range of copper-core stock-pots, compare prices, and read reviews before making a purchase. This digital transformation has not only expanded the market reach but also intensified competition, encouraging brands to offer better quality and value-added features.

From a regional perspective, North America and Europe have traditionally dominated the copper-core stock-pot market due to high disposable incomes, established culinary cultures, and a strong presence of leading cookware brands. However, the Asia Pacific region is emerging as a significant growth engine, driven by rising urbanization, a burgeoning middle class, and increasing awareness of the benefits of premium cookware. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by changing lifestyles and growing hospitality sectors. This regional diversification is expected to sustain the market’s upward trajectory in the coming years.

Product Type Analysis

The product type segment of the copper-core stock-pot market is broadly categorized into Stainless Steel Copper-Core Stock-Pots, Aluminum Copper-Core Stock-Pots, Nonstick Copper-Core Stock-Pots, and Others. Stainless steel copper-core stock-pots remain the most popular choice among consumers due to their exceptional durability, corrosion resistance, and compatibility with various heat sources. The fusion of stainless steel with a copper core ensures not only rapid and even heating but also a sleek aesthetic appeal that resonates with modern kitchens. The growing inclination towards professional-grade cookware in both residential and commercial kitchens has further fueled demand for this product

Southern Copper reported $84.33B in Market Capitalization this August of 2025, considering the latest stock price and the number of outstanding shares.Data for Southern Copper | SCCO - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last August in 2025.

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.

Ero Copper's Copper Future: Shining Bright or Tarnishing? (ERO)

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