Gold rose to 4,148.21 USD/t.oz on October 14, 2025, up 0.50% from the previous day. Over the past month, Gold's price has risen 12.74%, and is up 55.92% 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.

This statistic depicts the average annual prices for gold from 2014 to 2024 with a forecast until 2026. In 2024, the average price for gold stood at 2,388 U.S. dollars per troy ounce, the highest value recorded throughout the period considered. In 2026, the average gold price is expected to increase, reaching 3,200 U.S. dollars per troy ounce.

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.

What happens to gold if CPI increases?

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

Gold prices in , September, 2025 For that commodity indicator, we provide data from January 1960 to September 2025. The average value during that period was 610.94 USD per troy ounce with a minimum of 34.94 USD per troy ounce in January 1970 and a maximum of 3667.68 USD per troy ounce in September 2025. | TheGlobalEconomy.com

After three years of decline, the Indian gold market increased by 162% to $X in 2021. In general, consumption showed a relatively flat trend pattern. As a result, consumption attained the peak level and is likely to continue growth in the immediate term.

Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs’ parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN’s outputs to predict the daily closing gold price.

As of June 25, 2024, gold futures contracts to be settled in June 2030 were trading on U.S. markets at around ***** U.S. dollars per troy ounce. This is above the price of ******* 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.

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 2021, the global gold market decreased by -7.3% to $X for the first time since 2018, thus ending a two-year rising trend. The market value increased at an average annual rate of +3.1% from 2012 to 2021; however, the trend pattern indicated some noticeable fluctuations being recorded in certain years. Over the period under review, the global market reached the maximum level at $X in 2020, and then shrank in the following year.

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.

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 Volatility Amidst Economic Uncertainties

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 Kuwaiti gold market soared to $X in 2021, jumping by 142% against the previous year. This figure reflects the total revenues of producers and importers (excluding logistics costs, retail marketing costs, and retailers' margins, which will be included in the final consumer price). In general, consumption showed prominent growth. As a result, consumption reached the peak level and is likely to continue growth in the immediate term.

Silver rose to 53 USD/t.oz on October 15, 2025, up 3.09% from the previous day. Over the past month, Silver's price has risen 24.47%, and is up 66.95% 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 October of 2025.

Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs’ parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN’s outputs to predict the daily closing gold price.

Gold Bullion Market Outlook 2032

The global gold bullion market size was USD 50 Billion in 2023 and is likely to reach USD 133 Billion by 2032, expanding at a CAGR of 10.6% during 2024–2032. The market growth is attributed to the relative stability of gold investments.



Increasing economic uncertainties and geopolitical tensions are fueling the market. Investors are turning to gold as an asset, due to its inherent value and stability. The growing interest of central banks are leading them to expand their gold reserves. This allows them to diversify their holdings and reduce their dependence on the US dollar.

The growing popularity of gold-backed exchange-traded funds (ETFs) is propelling the market. These investments provide investors with exposure to the price movements of gold without the need to physically store the precious metal. The convenience and ease of investing in gold ETFs are attracting a new generation of investors, spurring the market.

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  According to a January 2024 report published by the World Gold Council, the total value of global gold ETFs rose by 6% to USD 2.4 Billion. This rise was due to a 15% hike in gold prices during 2023.

  
  

Impact of Artificial Intelligence (AI) in Gold Bullion Market

The use of artificial intelligence (AI) is likely to provide substantial propulsion to the gold bullion market. It is enhancing the efficiency and accuracy of gold trading with algorithms. These algorithms rapidly analyze vast amounts of data to make real-time trading decisions. This leads to increasingly profitable trades and reduces the risk of human error.


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In 2021, the Saudi gold market increased by 20% to $X for the first time since 2017, thus ending a three-year declining trend. Over the period under review, consumption, however, continues to indicate a pronounced downturn. Gold consumption peaked at $X in 2017; however, from 2018 to 2021, consumption stood at a somewhat lower figure.

Gold Bullion Market Outlook

According to our latest research, the global gold bullion market size reached USD 248.5 billion in 2024, and it is expected to grow at a CAGR of 4.7% during the forecast period, reaching approximately USD 373.4 billion by 2033. This healthy growth trajectory is primarily attributed to the increasing demand for safe-haven assets amid global economic uncertainties, rising geopolitical tensions, and a persistent appetite for portfolio diversification among both institutional and individual investors. The gold bullion market continues to benefit from its reputation as a reliable store of value, particularly during periods of inflation and currency depreciation, as per our comprehensive market analysis for 2025.

One of the most significant growth factors for the gold bullion market is the heightened volatility and uncertainty in global financial markets. Investors, both retail and institutional, are increasingly turning towards gold bullion as a hedge against inflation, currency fluctuations, and geopolitical risks. The persistent low-interest-rate environment, coupled with concerns over sovereign debt and fiscal imbalances in major economies, has further fueled the demand for physical gold. Central banks, especially in emerging markets, have been augmenting their gold reserves to diversify away from the US dollar and other fiat currencies, providing a strong and sustained impetus to the gold bullion market.

Another key driver propelling the gold bullion market is the growing accessibility and innovation in distribution channels. The proliferation of online platforms and digital gold investment products has democratized access to gold bullion, enabling a broader base of individual investors to participate in the market. This trend is further amplified by the introduction of fractional gold ownership, secure storage solutions, and transparent pricing mechanisms, which have collectively enhanced investor confidence and convenience. Additionally, the rise of gold-backed exchange-traded funds (ETFs) and other financial instruments has expanded the avenues for gold investment, reinforcing the market’s growth momentum.

Sustainability and ethical sourcing concerns are also shaping the gold bullion market landscape. Increasing awareness about responsible mining practices and the environmental and social impact of gold extraction has led to the emergence of certified, conflict-free bullion products. Regulatory initiatives and industry-led standards, such as the London Bullion Market Association (LBMA) Responsible Gold Guidance, are driving transparency and traceability across the supply chain. These developments are not only addressing investor concerns but also attracting a new segment of environmentally and socially conscious buyers, further supporting market expansion.

From a regional perspective, the Asia Pacific region remains the dominant force in the gold bullion market, driven by robust demand in countries like China and India, where gold holds deep cultural and economic significance. North America and Europe also represent substantial market shares, supported by strong institutional investment and central bank activity. Meanwhile, the Middle East & Africa and Latin America are emerging as important markets, buoyed by rising wealth levels, favorable regulatory environments, and increasing financial inclusion. The regional diversity in demand drivers underscores the global appeal and resilience of the gold bullion market.

Product Type Analysis

The gold bullion market is segmented by product type into bars, coins, rounds, and others, each catering to distinct investor preferences and use cases. Gold bars, often regarded as the standard investment vehicle for institutional buyers and high-net-worth individuals, account for the largest share of the market. Their appeal lies in their high purity, lower premiums over spot prices, and ease of storage and transport, making them the preferred choice for those seeking to make substantial investments in physical

The Sri Lankan gold market was finally on the rise to reach $X in 2021, after three years of decline. Over the period under review, consumption saw a noticeable increase. Gold consumption peaked at $X in 2017; however, from 2018 to 2021, consumption remained at a lower figure.

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: What's 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