Silver rose to 36.11 USD/t.oz on July 2, 2025, up 0.21% from the previous day. Over the past month, Silver's price has risen 4.60%, and is up 18.28% 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 July of 2025.

During the last quarter of 2024, the silver prices in the USA reached 31.3 USD/toz (H2 2024 Avg Price) in December. As per the silver price chart, prices surged during the latter half of 2024, largely influenced by the Federal Reserve’s interest rate cuts, which diminished the dollar. This shift made silver an attractive investment, particularly as a lower-cost alternative to gold.

Explore IMARC’s newly published report, titled “Silver Prices, Trend, Chart, Demand, Market Analysis, News, Historical and Forecast Data Report 2025 Edition,” offers an in-depth analysis of silver pricing, covering an analysis of global and regional market trends and the critical factors driving these price movements.

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 prices have surged recently, driven by dynamics influencing gold. However, analysts remain cautious due to silver's intrinsic volatility and market uncertainties.

The dataset shows average price in domestic and foreign markets of gold and silver

Note: The data sources are: 1. Gold and Silver Prices in Mumbai: Business Standard, Business Line, or The Economic Times (Mumbai) , IBJA Website. 2. Gold Price in London: LBMA. 3. Silver Price in New York:Thomson Reuters.

Silver futures contracts to be settled in December 2028 were trading on U.S. markets at around ** U.S. dollars per troy ounce on June 20, 2023. This is above the price of ***** U.S. dollars per troy ounce for contracts to be settled in May 2024, indicating silver traders expect the price of silver to decrease over the next five years. Silver futures are contracts that effectively lock in a price for an amount of silver to be purchased at a time in the future, which can then be traded on markets. Futures markets therefore provide an indicator of how investors think a commodities market will develop in the future.

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 >>

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

Fine Silver: Pure silver with a fineness of 99.9% or higher Sterling Silver: An alloy of silver and copper, typically 92.5% silver German Silver: An alloy of copper, nickel, and zinc Coin Silver: An alloy used in the production of coins Britannia Silver: A type of silver alloy used in the production of British coins Mexican Silver: Traditionally produced in Mexico, renowned for its high purity Russian Silver: Historic and collectible silver produced in Russia Oxidized Silver: Silver that has undergone a chemical reaction to create a dark, antique look Recent developments include: Silver market forecasts from 2021 and the upcoming years suggest that most key industrial players focus on exploring new technologies that can be merged with the silver market production. This greatly helps in the purchase of commodities. Several regulatory bodies are implemented for the detection and quality verification of the silver as well that disclose a proper commodity market. Not only consumers but also manufacturers and retailers help with the same. This now provides numerous opportunities for marketers due to the rising demand of the prominent key players of the market like Goldcorp, Gold Fields, and Barrick Gold.. Key drivers for this market are: Growing global population and rising disposable income. Increasing demand for silver in electronics and technology. Concerns about energy security and the need for renewable energy sources.. Potential restraints include: Price volatility and fluctuations in supply and demand. Competition from substitute materials, such as copper and aluminum. Stringent environmental regulations governing silver mining and processing activities.. Notable trends are: Development of new technologies for silver extraction and processing. Rising adoption of silver-based antimicrobial and antibacterial technologies. Growing interest in silver as a sustainable investment asset..

The dataset shows average price in domestic and foreign markets of gold and silver

Note: 1. The data source for this Table for the period till 1999-2000 are Bombay Bullion Association and Press Trust of India. For the period 2000-01 onwards, the data sources are (i) Business Standard/ Business Line and Economic Times, Mumbai/IBJA Website for gold and silver price in Mumbai and LBMA for gold price in London and (ii) Thomson Reuters for silver price in New York. 2. Data provided in this Table for the period 1979-80 to 1999-2000 and 2000-01 to 2020-21 may not be strictly comparable due to different sources of information.

Silver prices soar past $35 per ounce, marking a 13-year high, fueled by industrial demand and supply challenges. Analysts predict further growth.

The unwrought silver price in April 2023 was $869K per ton (FOB, Mexico), representing a 5% increase from the previous month.

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

Silver and platinum prices soar to new heights, driven by strong industrial demand and market deficits. Discover the factors behind this bullish trend in precious metals.

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

The Silver Market report segments the industry into Type (Fine Silver, Sterling Silver, Argentium Silver, Coin Silver, Other Types), Application (Physical Investment (bars), Electrical and Electronics, Photographic Films, Brazing Alloys, Jewelry and silverware, Pharmaceuticals, Other Applications), and Geography (Production Analysis, Consumption Analysis). Get five years of market history and forecasts for future trends.

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 for 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

In 2016, approx. 72K tons of silver were imported worldwide- coming down by -5.6% against the previous year figure. In general, silver imports continue to indicate a moderate decline. The pace of gr...

In June 2023, the Silver price was $750K per ton (CIF, Brazil), decreasing by -3.2% compared to the previous month.

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