Silver fell to 57.28 USD/t.oz on December 2, 2025, down 1.22% from the previous day. Over the past month, Silver's price has risen 19.11%, and is up 84.81% 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 December of 2025.

Silver prices have hit a fourteen-year high, driven by supply-demand dynamics and geopolitical factors. Explore the trends and potential for further growth in the silver market.

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.

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.

Permutable AI’s Silver Intelligence dataset (XAG) provides deep insights into silver’s dual role as both a precious and industrial metal. The system captures industrial demand drivers such as solar panels, EVs, and 5G technology, alongside monetary policy and geopolitical influences. With story signal analysis, the dataset identifies new narratives and evolving sentiment that impact silver prices. Historical datasets and the Co-Pilot API enable systematic traders and analysts to model silver’s performance across macroeconomic cycles.

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...

The size of the Silver Market was valued at USD 29.4 Billion in 2023 and is projected to reach USD 59.14 Billion by 2032, with an expected CAGR of 10.5% during the forecast period. 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..

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 global precious metal trading platform market is experiencing robust growth, driven by increasing investor interest in gold, silver, platinum, and palladium as safe-haven assets and diversification tools. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is fueled by several key factors. Technological advancements, including the rise of mobile trading apps and sophisticated charting tools, are making precious metal trading more accessible to a wider range of investors. Furthermore, the increasing volatility in global financial markets is prompting investors to seek refuge in precious metals, bolstering demand for platforms facilitating their trading. Regulatory changes aiming to improve market transparency and investor protection are also indirectly supporting market expansion. However, challenges remain, including potential regulatory hurdles in specific regions and the inherent risks associated with volatile commodity markets. The market is segmented by platform type (web-based, mobile-based), trading style (spot, futures, options), and investor type (retail, institutional). Key players like GAIN Global Markets Inc., AxiTrader Limited, LMAX Global, IG Group, and CMC Markets are vying for market share through innovation, strategic partnerships, and expansion into new geographic markets. Competition is intense, forcing providers to continuously enhance their offerings and improve customer experience to retain a competitive edge. The forecast period of 2025-2033 presents significant opportunities for expansion, particularly in emerging markets with growing retail investor bases. The continued growth of the precious metal trading platform market is projected to be influenced by several ongoing trends. The increasing adoption of artificial intelligence (AI) and machine learning (ML) for algorithmic trading and risk management is expected to further enhance the efficiency and sophistication of trading platforms. The integration of blockchain technology for improved security and transparency is also gaining traction. However, potential restraints include cybersecurity threats, the need for robust compliance frameworks, and the ongoing evolution of investor preferences which necessitate platform adaptation. The expanding availability of educational resources and improved investor awareness about precious metals trading is expected to positively impact market growth. Furthermore, strategic mergers and acquisitions within the industry are likely to reshape the competitive landscape. Geographic expansion into underpenetrated regions, coupled with the development of tailored products to meet the specific needs of diverse investor segments, will be crucial for achieving sustained growth in the coming years.

Explore the factors behind silver's surge past $33, driven by robust safe-haven demand and industrial advancements.

Monthly and long-term silver price data (US$/toz): historical series and analyst forecasts curated by FocusEconomics.

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

HSBC predicts higher silver prices due to gold's rally, industrial demand, and potential Fed rate cuts. The bank forecasts $35.14/oz in 2025 with a persistent market deficit.

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

Permutable AI’s Metals Intelligence platform provides real-time insights into both precious and industrial metals, including gold (XAU), silver (XAG), copper, and platinum. The system tracks supply-demand fundamentals, mine production, trade flows, and regional price effects, while detecting new story breakouts, volume build-up, direction shifts, and persistent narratives in global metals markets. Historical datasets and advanced story signal analysis enable traders and researchers to anticipate price volatility driven by monetary policy, industrial demand, and geopolitical disruptions, with millisecond-latency access via the Co-Pilot API.

The Silver Market Report is Segmented by Type (Fine Silver, Sterling Silver, Argentium Silver, Coin Silver, Other Types), Application (Physical Investment, Electrical and Electronics, Photographic Films, Brazing Alloys and Solders, Jewelry and Silverware, Pharmaceuticals and Biomedical, Other Applications), and Geography (Asia-Pacific, North America, Europe, South America, Middle-East and Africa).

Continuously updated Monex bid/ask prices for Silver spot and common bullion products.

Price-To-Tangible-Book-Ratio 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.

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