Silver fell to 43.93 USD/t.oz on September 23, 2025, down 0.30% from the previous day. Over the past month, Silver's price has risen 13.89%, and is up 36.63% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Silver - values, historical data, forecasts and news - updated on September of 2025.

Stay informed with real-time charts of international precious metal prices. Monitor spot prices for Silver in USD, GBP, and EUR. Access live updates here >>

MAG Silver 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.

Predicting Silver Prices with Machine Learning: Promising Approaches and Considerations

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

Silver Standard Resources 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.

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

The price of an ounce of silver increased sharply in 2021, rising around 17 percent from January 28 to February 1. The cause of this increase is attributed to retail investors mobilized via social media with the intention of causing losses to professional investors, similar to the rise in the stock price of video game retailer GameStop, and the stock price of cinema operator AMC several days beforehand. As of midnight July 18, 2023, the price of silver was trading at 24.9 U.S. dollars per troy ounce.

First Majestic Silver 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.

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

Stock Price Time Series for Sprott Physical Silver. Sprott Physical Silver Trust is an exchange traded commodity launched and managed by Sprott Asset Management LP. The fund invests in the commodity markets. It primarily invests in physical silver bullion in London Good Delivery bar form. Sprott Physical Silver Trust was formed on June 30, 2010 and is domiciled in Canada.

Pan American Silver stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Silver prices may remain elevated due to rising inflationary pressures and increased demand for safe-haven assets amid geopolitical uncertainties. However, potential risks include a stronger US dollar, reduced industrial activity due to economic slowdown, and volatility in equity markets.

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.

Silver's Uncertain 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

Fortuna Silver Mines stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Stock Price Time Series for Aya Gold & Silver Inc. Aya Gold & Silver Inc., together with its subsidiaries, engages in the exploration, evaluation, and development of precious metals projects in Morocco. The company primarily explores for gold and silver deposits. Its flagship project holds 100% interests in the Zgounder property located approximately 260 kms east of Agadir in the Proterozoic Siroua Massif of the Anti-Atlas Range, Morocco covering an area of approximately 350 square kilometers. The company was incorporated in 2007 and is based in Mount Royal, Canada.

First Majestic Silver reported $0.24 in Dividend Yield for its fiscal quarter ending in June of 2025. Data for First Majestic Silver | FR - Dividend Yield including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Index Time Series for abrdn Physical Silver Shares ETF. The frequency of the observation is daily. Moving average series are also typically included. The shares are intended to constitute a simple and cost-effective means of making an investment similar to an investment in silver. An investment in physical silver requires expensive and sometimes complicated arrangements in connection with the assay, transportation, warehousing and insurance of the metal. Although the shares are not the exact equivalent of an investment in silver, they provide investors with an alternative that allows a level of participation in the silver market through the securities market.

According to Cognitive Market Research, the global silver bullion market size is USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of 4.00% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD XX million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.0% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.4% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.7% from 2024 to 2031.
The US had the most significant global silver bullion market revenue share in 2024.

Market Dynamics of Silver Bullion Market

Key Drivers of Silver Bullion Market

Increasing Demand for Safe Haven Investments

The increasing wish for safe haven investments is driving the market for silver bullion to continue growing. Investors look for assets that deliver stability and wealth preservation throughout difficult economic, geopolitical, and market situations. Due to its inherent worth and historical importance as a wealth vault, silver is drawing more and more attention from investors trying to diversify their holdings and protect themselves from inflation and currency depreciation. The COVID-19 pandemic's aftereffects, trade disputes, and geopolitical tensions have all contributed to the current state of the global economy, which has raised investor anxieties and increased demand for silver bullion. Concerns about possible inflationary pressures are developing as governments execute large stimulus programs and central banks adopt loose monetary policies; this is pushing investors into physical assets like silver.

Increasing Industrial Applications Will Promote Market Expansion

The market for silver bullion is also expected to rise significantly due to the growing number of industrial uses. Due to its special qualities, which include its high conductivity, malleability, and resistance to corrosion, silver is used in a wide range of industries, including electronics, healthcare, automotive, and renewable energy. The industrial demand for silver is anticipated to grow in the upcoming years due to technological developments and advancements boosting demand in developing applications including solar panels, electric vehicles, and 5G technology. Silver's industrial demand is further bolstered by its antibacterial characteristics, which render it increasingly desirable in therapeutic applications. The market for silver bullion is expected to increase steadily as long as industries keep innovating and creating new goods that need silver. Investors who are eager to profit from the growing industrial need for this precious metal will be drawn to this market.

Restraint Factors Of Silver Bullion Market

Volatility in Precious Metal Prices will hinder market growth.

The price volatility of precious metals can have a substantial impact on the development of the silver bullion market. The price of silver can vary due to changes in currency values, geopolitical tensions, and global economic conditions. Investors get indeterminate as a result of these swings, which could make them unwilling to buy silver bullion. Investors who bought silver at higher prices may lose money as a result of abrupt price reductions, which could affect market liquidity and confidence. Businesses that use silver as a raw resource, such as manufacturers, face difficulties due to the unpredictable nature of silver pricing. Businesses may find it challenging to correctly manage expenses and plan production schedules in the face of shifting silver prices. Price variations can disrupt the supply chain, as suppliers and buyers are driving the changing market conditions.

Market participants may use hedging techniques or look for alternate investments to lessen the impact of price volatility, which could result in money being taken out of...

The U.S. silver market rose sharply to $7.5B in 2024, increasing by 6.2% against the previous year. Over the period under review, consumption showed a relatively flat trend pattern. Over the period under review, the market reached the peak level at $7.5B in 2012; afterwards, it flattened through to 2024.

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 Ultimate Guide to Precious Metal 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