Gold fell to 4,023.41 USD/t.oz on October 27, 2025, down 2.15% from the previous day. Over the past month, Gold's price has risen 4.96%, and is up 46.60% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on October of 2025.

The average monthly prices for gold increased worldwide between January 2014 and May 2025, although with some fluctuations. In January 2014, the average monthly price for gold worldwide stood at ******** nominal U.S. dollars per troy ounce. Significant jumps in the gold prices were observed, especially in the periods of uncertainty, as the investors tend to see gold as a safe investment option. For instance, the Corona pandemic acted as a shock to the economy, resulting in substantial increases in gold prices in 2020. As of May 2025, gold valued at ******** U.S. dollars per ounce, the highest value reported during this period.

This dataset contains monthly gold prices from 1950-01 to 2020-07. Gold is a precious metal that has been used as a store of value and a medium of exchange for thousands of years, and is still widely traded in financial markets today. The gold price is influenced by a variety of factors, including global economic conditions, geopolitical events, and supply and demand dynamics.

The dataset includes a total of 847 data points, with each row representing the gold price for a particular month. The data was sourced from the World Gold Council and is in USD per troy ounce.

This dataset can be used for a variety of applications, including financial analysis, time series forecasting, and machine learning modeling. Potential use cases include predicting future gold prices based on historical trends, analyzing the relationship between gold prices and other economic indicators, and developing trading strategies for gold-related assets.

Data Source: World Gold Council

Description for Kaggle Project

Title: Gold Price Prediction

Subtitle: Analysis and Forecasting Using Gold Price Data from Kaggle's goldstock.csv

Description This project aims to analyze and forecast gold prices using a comprehensive dataset spanning from January 19, 2014, to January 22, 2024. The dataset, sourced from Kaggle, includes daily gold prices with key financial metrics such as opening and closing prices, trading volume, and the highest and lowest prices recorded each trading day. Through this project, we perform time series analysis, develop predictive models, formulate and backtest trading strategies, and conduct market sentiment and statistical analyses.

Upload an Image - Choose a relevant image such as a graph of gold price trends, a gold bar, or an illustrative image related to financial data analysis.

Datasets - Source: Kaggle - File: goldstock.csv

Context, Sources, and Inspiration -Context: Understanding the dynamics of gold prices is crucial for investors and financial analysts. This project provides insights into historical price trends and equips users with tools to predict future prices. - Sources: The dataset is sourced from Kaggle and contains historical gold price data obtained from Nasdaq. Inspiration: The inspiration behind this project is to enable researchers, analysts, and data enthusiasts to make informed decisions, develop trading strategies, and contribute to a broader understanding of market behavior.

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.

Gold Price Outlook: S&P GSCI Gold index Faces 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: 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

In 2025, the price of platinum is forecast to hover around ***** U.S. dollars per troy ounce. Meanwhile, the cost of per troy ounce of gold is expected to amount to ***** 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 ***** 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 **** 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 **** 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 ***** U.S. dollars per ounce, making it the most expensive precious metal in 2023.

EGPB

About Dataset This benchmark dataset consisting of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset. Key variables & Features include: • Previous gold prices • Future gold prices with predictions for one day, one week, and one month • Oil prices •… See the full description on the dataset page: https://huggingface.co/datasets/Farah42/EGPBD-An-Event-based-Gold-Prices-Benchmark-Dataset.

As of June 25, 2024, gold futures contracts to be settled in June 2030 were trading on U.S. markets at around 2,795 U.S. dollars per troy ounce. This is above the price of 2,482.6 U.S. dollars per troy ounce for contracts to be settled in June 2025, indicating that gold traders expect the price of gold to rise over the next five years. Gold futures are contracts that effectively lock in a price for an amount of gold 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.

Retail Sales Nowcast: YoY: Contribution: Precious Metals Price/Gold Price: Gold: China: Shanghai Future Exchange: Settlement Price: 1st Month data was reported at 0.000 % in 12 May 2025. This stayed constant from the previous number of 0.000 % for 05 May 2025. Retail Sales Nowcast: YoY: Contribution: Precious Metals Price/Gold Price: Gold: China: Shanghai Future Exchange: Settlement Price: 1st Month data is updated weekly, averaging 0.010 % from Feb 2021 (Median) to 12 May 2025, with 224 observations. The data reached an all-time high of 15.432 % in 07 Aug 2023 and a record low of 0.000 % in 12 May 2025. Retail Sales Nowcast: YoY: Contribution: Precious Metals Price/Gold Price: Gold: China: Shanghai Future Exchange: Settlement Price: 1st Month data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s China – Table CN.CEIC.NC: CEIC Nowcast: Retail Sales.

In 2021, the Israeli gold market increased by 125% to $X, rising for the second consecutive year after five years of decline. Overall, consumption showed resilient growth. Over the period under review, the market hit record highs in 2021 and is expected to retain growth in the near future.

The global plain gold jewelry market, encompassing 24K, 22K, and 18K gold variations, across online and offline retail channels, is experiencing robust growth. While precise market size figures for 2025 are unavailable, leveraging the provided CAGR (let's assume a conservative 5% for illustration) and a hypothetical 2019 market size of $50 billion, a 2025 market size of approximately $64 billion can be reasonably inferred. This growth is fueled by several key factors. Firstly, gold's enduring appeal as a safe-haven asset and its cultural significance in various regions drive consistent demand. Secondly, evolving consumer preferences, including a rising interest in minimalist designs and sustainable sourcing, are positively impacting the market. The online segment is witnessing rapid expansion due to increased e-commerce penetration and the convenience it offers. Major players like Chow Tai Fook, Tanishq, and others are leveraging online platforms to expand their reach and cater to a broader customer base. However, market growth faces certain challenges. Economic fluctuations, particularly inflation and recessionary pressures, can impact consumer spending on non-essential items like jewelry. Furthermore, fluctuating gold prices directly influence market dynamics and consumer purchasing decisions. The market is segmented geographically, with Asia-Pacific (particularly India and China) remaining a dominant region due to strong cultural ties to gold and significant consumer purchasing power. North America and Europe also exhibit considerable demand, driven by both investment and adornment motivations. The segmentation by karat (24K, 22K, 18K) reflects varying preferences for purity and color, offering diverse product options to meet consumer needs. Future growth will depend on managing price volatility, adapting to changing consumer preferences, and strategically navigating the evolving retail landscape. The competitive landscape is marked by a mix of established global players and regional brands. Large jewelry conglomerates benefit from established supply chains and strong brand recognition. However, smaller, specialized brands are gaining traction by offering unique designs and catering to niche market segments. Success in this market will require a combination of strong brand building, efficient supply chain management, and the ability to adapt quickly to shifting consumer preferences and macroeconomic conditions. The continued rise of e-commerce and the increasing importance of sustainable practices are likely to further shape the market landscape in the coming years. Understanding regional nuances and tailoring marketing strategies accordingly will be crucial for long-term market success.

China Settlement Price: Shanghai Future Exchange: Gold: 5th Month data was reported at 785.840 RMB/g in Apr 2025. This records an increase from the previous number of 725.480 RMB/g for Mar 2025. China Settlement Price: Shanghai Future Exchange: Gold: 5th Month data is updated monthly, averaging 287.775 RMB/g from Jan 2008 (Median) to Apr 2025, with 208 observations. The data reached an all-time high of 785.840 RMB/g in Apr 2025 and a record low of 159.600 RMB/g in Oct 2008. China Settlement Price: Shanghai Future Exchange: Gold: 5th Month data remains active status in CEIC and is reported by Shanghai Futures Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZB: Shanghai Futures Exchange: Commodity Futures: Settlement Price.

The North American gold market expanded markedly to $X in 2021, rising by 15% against the previous year. Over the period under review, consumption recorded a prominent increase. Over the period under review, the market attained the peak level in 2021 and is likely to see steady growth in the near future.

Forecasting gold prices remains vital in financial markets, given gold’s dual role as both a hedge against inflation and a safe-haven asset during economic uncertainty. This study proposes a hybrid model integrating SARIMA, LSTM, and RF to improve predictive accuracy by capturing both linear and nonlinear dependencies in historical gold price data. SARIMA models linear trends and seasonal components, LSTM captures nonlinear patterns from SARIMA residuals, and RF refines predictions using macroeconomic indicators such as the USD Index, Federal Interest Rate, US CPI, Oil Prices, S&P 500 Index, and Bond Yields. Utilizing real-world data, the model effectively tracks market trends with reduced forecasting errors, indicating continued price fluctuations and potential long-term growth. The findings provide valuable insights for investors and policymakers, with future research focusing on additional macroeconomic factors and advanced hybrid forecasting techniques. This study introduces a hybrid SARIMA–LSTM–RF model that enhances the accuracy of gold price forecasting by capturing both linear and nonlinear market dynamics. By integrating macroeconomic indicators such as the USD Index, Federal Interest Rate, CPI, Oil Prices, S&P 500 Index, and Bond Yields, the model effectively reflects real-world financial interactions influencing gold prices. The results demonstrate reduced prediction errors and improved tracking of short-term fluctuations as well as long-term growth trends. These findings provide valuable implications for investors and policymakers in managing financial risk and optimizing investment portfolios, while contributing to the advancement of hybrid forecasting frameworks for complex financial time series.

The global gold and silver jewelry market exhibits robust growth, driven by increasing disposable incomes, particularly in developing economies, and a persistent preference for gold and silver as investment assets and heirloom pieces. The market's expansion is further fueled by evolving fashion trends, incorporating intricate designs and diverse styles to cater to a wider range of consumer preferences. Technological advancements in jewelry manufacturing, enabling greater precision and efficiency, also contribute to market growth. While economic downturns can temporarily restrain spending on luxury items like jewelry, the enduring cultural significance of gold and silver, coupled with their perceived value as a safe haven asset, ensures sustained market demand. The market is segmented by various factors, including metal type (gold, silver, and combinations thereof), jewelry type (necklaces, earrings, bracelets, rings), and price points (luxury, mid-range, budget-friendly). Key players in the market leverage strong brand recognition, extensive distribution networks, and innovative marketing strategies to maintain a competitive edge. Emerging trends include the integration of sustainable sourcing practices and ethical manufacturing processes, increasingly influencing consumer purchasing decisions. The competitive landscape is characterized by a mix of established international brands and regional players. Large multinational companies often command a significant market share through their global presence and established brand recognition. However, regional businesses are thriving, capitalizing on local cultural preferences and crafting unique styles. Competition is intensifying due to the market's attractiveness, pushing players to enhance product offerings, improve distribution channels, and strengthen their brand identities. The forecast period (2025-2033) anticipates continued growth, although the pace may vary depending on macroeconomic conditions and shifts in consumer spending habits. The market's future will likely be shaped by the increasing adoption of e-commerce, the growing demand for personalized jewelry, and a greater emphasis on transparency and sustainability throughout the supply chain. To maintain their position, companies must adapt to changing consumer behaviors and invest in innovation and sustainable practices.

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: Analysts Bullish on Future Prospects.

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 revenue of the gold market in Slovenia amounted to $X in 2018, rising by X% against the previous year. Overall, gold consumption continues to indicate a resilient expansion. The pace of growth appeared the most rapid in 2016 with an increase of X% year-to-year. Slovenia gold consumption peaked in 2018 and is likely to continue its growth in the near future.

China Settlement Price: Shanghai Future Exchange: Gold: 2nd M data was reported at 782.760 RMB/g in Apr 2025. This records an increase from the previous number of 723.080 RMB/g for Mar 2025. China Settlement Price: Shanghai Future Exchange: Gold: 2nd M data is updated monthly, averaging 286.525 RMB/g from Jan 2008 (Median) to Apr 2025, with 208 observations. The data reached an all-time high of 782.760 RMB/g in Apr 2025 and a record low of 160.000 RMB/g in Oct 2008. China Settlement Price: Shanghai Future Exchange: Gold: 2nd M data remains active status in CEIC and is reported by Shanghai Futures Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZB: Shanghai Futures Exchange: Commodity Futures: Settlement Price.

Worldwide gold demand amounted to ******* metric tons in 2024, an increase from ******* metric tons in the previous year. Furthermore, 2020 was the first time demand for gold was lower than ***** metric tons throughout the period considered, and the driving force behind that drop was the coronavirus pandemic. Gold supply The supply of gold depends largely on the mine production of gold. Production, in turn, depends on two factors. The countries with higher reserves of gold work harder to extract their gold when the price of gold increases, following the standard theory that the quantity supplied increases with price. Similarly, the expectation of higher prices in the future prompts speculators to explore for new reserves. As new lodes are discovered, the supply of gold increases. Investments in gold Gold is subject to cyclical volatility in its rate of return, and many investors speculate on its value. However, for historic reasons, many view it as a symbol of price stability. After World War II, the Bretton Woods system tied the price of all major currencies to the price of gold until the 1970s. This legacy means that most countries still maintain large gold reserves. While this can drive gold demand, it also reduces the supply of gold in circulation by locking huge amounts of gold in central bank vaults. Gold demand was noticeably higher following the Financial Crisis, until the coronavirus pandemic hit. Many investors look to gold in periods of market turmoil because they believe that it holds value through recessions better than other assets.