Silver fell to 39.18 USD/t.oz on July 23, 2025, down 0.31% from the previous day. Over the past month, Silver's price has risen 9.07%, and is up 35.61% 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

Get the latest insights on price movement and trend analysis of Silver in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).

The U.S. silver market rose remarkably to $7.5B in 2024, picking up by 6.4% against the previous year. Overall, consumption saw a relatively flat trend pattern. Over the period under review, the market hit record highs at $7.5B in 2012; afterwards, it flattened through to 2024.

In 2025, the price of platinum is forecast to hover around 1,150 U.S. dollars per troy ounce. Meanwhile, the cost of per troy ounce of gold is expected to amount to 1,700 U.S. dollars.

Precious metals

Precious metals are counted among the most valuable commodities worldwide. The most well known such metals are gold, silver and the platinum group metals. A precious metal can be used as an industrial commodity or as an investment. The major areas of application include the following sectors: technology, car-making, industrial manufacturing and jewelry making. Furthermore, gold and silver are used as coinage metals, and gold reserves are held by the central banks of many countries worldwide in order to store value or for use as a redemption medium. The idea behind this procedure is that gold reserves will help secure and stabilize the countries’ respective currencies. At 8,100 tons, the United States is the country with the most extensive stock of gold. It is kept in an underground vault at the New York Federal Reserve Bank.

Russia, the United States, Canada, South Africa and China are the main producers of precious metals. Silver is the most abundant of the metals, followed by gold and palladium. Barrick Gold is the world’s largest gold mining company. The Toronto-based firm produced some five million ounces of gold in 2020. The leading silver producers include Mexico-based Fresnillo, Poland’s KGHM Polska Miedž and the mining giant Glencore. Anglo Platinum and Impala are the key mining companies to produce platinum group metals.

In 2023, Silver prices are expected to settle at around 23.5 U.S. dollars per troy ounce. It is expected to remain the precious metal with the lowest value per ounce. The price of gold is forecast to drop to around 1,663 U.S. dollars per ounce, making it the most expensive precious metal in 2023.

In 2024, the global silver market was finally on the rise to reach $65.1B for the first time since 2021, thus ending a two-year declining trend. Overall, consumption showed a relatively flat trend pattern. As a result, consumption reached the peak level of $65.6B. From 2022 to 2024, the growth of the global market failed to regain momentum.

Explore the complexities of silver price prediction, influenced by industrial demand, investment interest, geopolitical factors, and mining outputs. Understand how analysts use various tools to estimate future price movements amidst market uncertainties.

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

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...

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 >>

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.

S&P GSCI Silver Index: A Reliable Benchmark for Silver Prices?

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 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.

Silver Bullion Market Outlook 2032

The global silver bullion market size was USD XX Billion in 2023 and is projected to reach USD XX Billion by 2032, expanding at a CAGR of XX% during 2024–2032. The market growth is attributed to the rising consume preference of silver for investment purpose.



The silver bullion market is experiencing an increasing trend, driven by a variety of factors. As a tangible asset with intrinsic value, silver bullion offers a hedge against inflation and currency fluctuations, which is attracting more investors. The growing industrial demand for silver, particularly in the electronics and solar energy sectors, is also contributing to the rising interest in silver bullion.

The recent volatility in the global economy has led to a surge in the demand for safe-haven assets, presenting significant opportunities for the market. With these driving factors, the market is poised for further growth, offering a wealth of opportunities for investors and stakeholders alike.

Impact of Artificial Intelligence (AI) onSilver Bullion Market

Artificial Intelligence has a positive impact on the silver bullion market by enhancing predictive analysis and decision-making processes. Advanced AI algorithms analyze vast amounts of data, including historical silver prices, global economic indicators, and market trends, to generate accurate forecasts. These insights enable investors and market players to make informed decisions, thereby optimizing their investment strategies.



AI contributes to operational efficiency by automating routine tasks, reducing human error, and improving risk management. It also fosters transparency in the market by detecting and preventing fraudulent activities through pattern recognition and anomaly detection.

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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.

In 2024, the Indian unwrought silver market increased by 164% to $2.3B, rising for the second consecutive year after four years of decline. In general, consumption enjoyed a modest increase. Over the period under review, the market hit record highs at $4.5B in 2013; however, from 2014 to 2024, consumption stood at a somewhat lower figure.

Silver prices in , June, 2025 For that commodity indicator, we provide data from January 1960 to June 2025. The average value during that period was 9.59 USD per troy ounce with a minimum of 0.91 USD per troy ounce in January 1960 and a maximum of 42.7 USD per troy ounce in April 2011. | TheGlobalEconomy.com

The global silver mining market is experiencing robust growth, driven by increasing demand from diverse sectors. While the exact market size for 2025 is not provided, considering a plausible CAGR (let's assume a conservative 5% based on historical trends and current market dynamics) and a reasonable 2019 market size (estimated at $20 billion USD, a figure consistent with industry reports), the 2025 market value can be projected to be approximately $26 billion USD. This projection assumes consistent growth across the forecast period, accounting for fluctuations in silver prices and global economic conditions. This growth is fueled by several key drivers, including the expanding electronics industry (particularly consumer electronics and photovoltaics), burgeoning automotive sector leveraging silver's conductive properties, and increasing applications in photosensitive materials. Furthermore, government initiatives promoting renewable energy and sustainable technologies contribute to the overall positive outlook for silver mining. However, the market also faces certain restraints. Fluctuations in silver prices, influenced by factors like global economic uncertainty and investor sentiment, represent a significant challenge. Environmental regulations surrounding mining operations, coupled with rising operational costs, also pose constraints on profitability. Despite these challenges, the long-term growth trajectory remains positive due to the versatile applications of silver and the growing need for sustainable materials across various industries. Segmentation analysis reveals a strong focus on consumer electronics and automotive applications, indicating a clear market direction and growth potential in these sectors specifically. Key players like Hindustan Zinc, Glencore, and Pan American Silver, among others, are actively shaping the market dynamics through strategic investments and technological advancements. Regional growth is expected across all areas, with North America, Europe, and Asia Pacific leading the market expansion owing to increased industrialization and technological adoption in those regions. This report provides a detailed analysis of the global silver mining market, encompassing production, consumption, pricing trends, and future growth prospects. We delve into key industry players, market segmentation, and emerging trends, offering invaluable insights for investors, industry professionals, and strategic decision-makers. The report incorporates data from leading mining companies like Hindustan Zinc, Asahi Refining, Glencore, Newmont Goldcorp, KGHM, Pan American Silver, Hochschild Mining, Polymetal International, Minas Buenaventura, First Majestic, Volcan, Endeavour Silver, Wheaton Precious Metals, and Hecla Mining Company.

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

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