Silver fell to 38 USD/t.oz on August 15, 2025, down 0.03% from the previous day. Over the past month, Silver's price has risen 0.23%, and is up 30.89% 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 August of 2025.

Dataset of historical annual silver prices from 1970 to 2022, including significant events and acts that impacted silver prices.

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 statistic depicts the average monthly prices for silver worldwide from January 2014 through June 2025. In June 2025, the average monthly price for silver worldwide stood at ***** nominal U.S. dollars per troy ounce.

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.

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

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 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.

Silver prices in , July, 2025 For that commodity indicator, we provide data from January 1960 to July 2025. The average value during that period was 9.62 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

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

In 2024, the average nominal price of silver in India was ****** Indian rupees for one kilogram, which was an increase of over ****** rupees from the previous year, and the highest figure during the period of consideration.

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...

Bullion Price: Monthly Average: Mumbai: Silver data was reported at 95,524.000 INR/kg in Feb 2025. This records an increase from the previous number of 90,020.000 INR/kg for Jan 2025. Bullion Price: Monthly Average: Mumbai: Silver data is updated monthly, averaging 19,356.000 INR/kg from Apr 1990 (Median) to Feb 2025, with 419 observations. The data reached an all-time high of 95,524.000 INR/kg in Feb 2025 and a record low of 5,554.000 INR/kg in Mar 1993. Bullion Price: Monthly Average: Mumbai: Silver data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under Global Database’s India – Table IN.PG002: Memo Items: Bullion Price.

United States IP: PS: Silver incl Silver Plated with Gold or Platinum data was reported at 137.300 Dec2021=100 in Mar 2025. This records a decrease from the previous number of 137.500 Dec2021=100 for Feb 2025. United States IP: PS: Silver incl Silver Plated with Gold or Platinum data is updated monthly, averaging 105.150 Dec2021=100 from Dec 2021 (Median) to Mar 2025, with 40 observations. The data reached an all-time high of 141.200 Dec2021=100 in Nov 2024 and a record low of 81.000 Dec2021=100 in Sep 2022. United States IP: PS: Silver incl Silver Plated with Gold or Platinum data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.JA237: Import Price Index: by Harmonized Index.

The Global Precious metals Market size is expected to grow at a CAGR of 5.6% during the forecast period by 2028. The growth can be attributed to industrial applications such as jewelry and medical devices which are anticipated to increase demand for gold and silver medal, respectively over the forecast period.

Precious metals are a group of elements that have been used for centuries to create some of the most beautiful and highest quality objects in history. Gold, silver, platinum, and palladium are precious metals while copper is not typically categorized as a precious metal because it is so abundant on earth. The beauty of precious metals is not their only value. They are also used in applications that range from industry to financial services and even consumer goods such as jewelry.

On the basis of Type, the market is segmented into Gold, Silver Metal, Platinum Group Metals.

Gold:

Gold is a chemical element with the symbol Au and an atomic number of 79. It has been a highly sought-after precious metal for coinage, jewelry, and other arts since the beginning of recorded history. The metal occurs as nuggets or grains in rocks, underground veins, and in alluvial deposits. Gold is dense, soft, shiny and the most malleable and ductile pure metal known to man which means it can be beaten into thin sheets (0.0000001 mm) but not dissolved by any acid.

Silver Metal:

The term ‘silver metal’ is used to refer to the silver-rich alloy of metals. The alloys are also known as Ag or sterling, which has high purity and includes copper, nickel, zinc, etc. Silver metal is increasingly preferred over other precious metals due to its low cost in comparison with gold and platinum group metals (PGM).

Platinum Group Metals:

Platinum Group Metals are a group of metals that have been traditionally used in the industry, but lately, they have become popular for other applications as well. The main features of Platinum Group Metals are that they can be worked into almost any shape or form and their price remains stable even when the market becomes very volatile.

On the basis of Application, the market is segmented into Industry, Consumer Sector, Financial Sector.

Industry:

In the industry sector, precious metals are used in many different aspects of production. This includes anything from electronics to car manufacturing and even healthcare applications. A lot of technology devices require these materials for their construction, most notably smartphones. Smartphone manufacturers use gold foils on circuit boards due to their low electrical resistance properties that help with signal transmission between components.

Consumer Sector:

Precious metals are used in various consumer electronics. The use of gold, silver, and platinum for manufacturing electronic equipment has increased due to the increasing penetration of smartphones, tablets, etc., which have grown rapidly over the past decade or so. The consumer sector is expected to be the largest market for precious metals, and this trend will continue over the forecast period.

Financial Sector:

Gold and silver are used in a range of financial products, from coins to bars. Gold has been held as an asset for thousands of years because it is scarce, durable, liquid, and does not need any other material to produce jewelry or be shaped into ornaments. Silver's physical characteristics have also resulted in its widespread use in industry: being highly conductive means that silver metal dramatically lowers energy costs when compared with traditional materials such as a copper wire.

On the basis of Region, the market is segmented into North America, Latin America, Europe, Asia Pacific, and Middle East & Africa.

The North American market is expected to account for the largest share, due to its increasing demand from several end-use industries. The U.S., which contributes a major share of this region’s revenue, has been witnessing an upsurge in public and private investment opportunities across different sectors such as the oil & gas exploration and the construction industry. This trend is likely to boost the availability of raw materials required by downstream segments including Gold Jewelry manufacturer companies, thereby driving growth over the next few years. The Latin American market is estimated to be the fastest-growing during the forecast period. The Asia Pacific will witness significant growth due to factors such as developments in the mining industr

Copper fell to 4.45 USD/Lbs on August 18, 2025, down 0.43% from the previous day. Over the past month, Copper's price has fallen 20.47%, but it is still 6.49% 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.

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

The price of gold per troy ounce increased considerably between 1990 and 2025, despite some fluctuations. A troy ounce is the international common unit of weight used for precious metals and is approximately **** grams. At the end of 2024, a troy ounce of gold cost ******* U.S. dollars. As of * June 2025, it increased considerably to ******** U.S. dollars. Price of – additional information In 2000, the price of gold was at its lowest since 1990, with a troy ounce of gold costing ***** U.S. dollars in that year. Since then, gold prices have been rising and after the economic crisis of 2008, the price of gold rose at higher rates than ever before as the market began to see gold as an increasingly good investment. History has shown, gold is seen as a good investment in times of uncertainty because it can or is thought to function as a good store of value against a declining currency as well as providing protection against inflation. However, unlike other commodities, once gold is mined it does not get used up like other commodities (for example, such as gasoline). So while gold may be a good investment at times, the supply demand argument does not apply to gold. Nonetheless, the demand for gold has been mostly consistent.

United States - Producer Price Index by Industry: Gold Ore and Silver Ore Mining was 206.30000 Index Dec 1984=100 in August of 2018, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Gold Ore and Silver Ore Mining reached a record high of 326.80000 in September of 2011 and a record low of 52.10000 in April of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Gold Ore and Silver Ore Mining - last updated from the United States Federal Reserve on August of 2025.

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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