Gold rose to 3,642.37 USD/t.oz on September 12, 2025, up 0.29% from the previous day. Over the past month, Gold's price has risen 8.52%, and is up 41.26% 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 September of 2025.

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.

Machine Learning Models for Gold Price Prediction

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

Goldman Sachs raises its year-end gold price target to $3,700 due to economic uncertainties and strong demand. UBS also revises its forecast to $3,500, highlighting gold's status as a secure investment.

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.

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: A Brighter Future Ahead?

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 Gold in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).

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.

Predictions: S&P GSCI Gold index is expected to continue its upward trend in the near term, driven by safe-haven demand amid ongoing geopolitical uncertainties and concerns about global economic growth. The index may face some resistance at higher levels, but it is likely to break through and reach new highs. Risks: The main risks to the S&P GSCI Gold index's upward trend include a significant improvement in the global economic outlook, a sharp decline in geopolitical tensions, and a shift in investor sentiment towards riskier assets. A prolonged period of high inflation could also pose a risk to the index, as investors may seek alternative safe-haven assets such as bonds.

The Egyptian gold market dropped to $X in 2021, reducing by -13% against the previous year. Over the period under review, consumption saw a abrupt descent. As a result, consumption reached the peak level of $X. From 2017 to 2021, the growth of the market 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.

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

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.

IAU:TSX i-80 Gold Corp.

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 Target Market Outlook

The global gold target market size was valued at approximately USD 2.5 trillion in 2023 and is projected to reach around USD 3.7 trillion by 2032, growing at a compound annual growth rate (CAGR) of 4.3% during the forecast period. This steady growth is driven by various factors including increasing geopolitical uncertainties, inflation hedging characteristics of gold, and rising demand across different applications. The intrinsic value and limited supply of gold continue to make it a safe haven investment in times of economic volatility, further solidifying its role in diverse portfolios worldwide.

One of the significant growth factors driving the gold target market is the persistent demand for gold as a hedge against inflation and currency devaluation. In the face of fluctuating global economies and the ongoing volatility in currency markets, investors often turn to gold as a means to preserve wealth. The metalÂ’s ability to maintain its value over time makes it an attractive asset, especially in regions experiencing high inflation rates. Moreover, central banks continue to increase their gold reserves as part of their monetary policy strategies, thereby fueling demand in this market segment.

Another crucial factor contributing to the growth of the gold market is the expanding middle class and rising disposable incomes, particularly in developing economies. As incomes rise, so does the demand for luxury items, including gold jewelry. Countries like India and China, which have deep-rooted cultural affinities with gold, are witnessing significant increases in gold consumption for both investment and ornamental purposes. This cultural significance, combined with economic growth, has positioned the Asia Pacific region as a major consumer of gold, bolstering the market's global expansion.

Technological advancements and innovations in gold mining and refining processes are also propelling market growth. Modern techniques and equipment have improved the efficiency of gold extraction and processing, reducing costs and increasing output. Additionally, the development of new financial products like gold-backed exchange-traded funds (ETFs) has made gold investments more accessible to a broader range of investors. The convenience and flexibility of these products have attracted both retail and institutional investors, further driving market demand.

The emergence of Edible Gold Beverage is an intriguing development in the gold market, blending luxury with culinary innovation. This unique product taps into the growing trend of gourmet experiences, where consumers seek novel and opulent ways to indulge. Edible gold, known for its non-toxic and inert properties, is increasingly being used to enhance beverages, offering a visually stunning and luxurious appeal. This trend is particularly popular in high-end restaurants and events, where presentation and exclusivity are paramount. The incorporation of gold into beverages not only elevates the sensory experience but also aligns with the cultural significance of gold as a symbol of wealth and celebration. As consumer preferences evolve towards unique and extravagant experiences, the Edible Gold Beverage market is poised for growth, attracting both connoisseurs and curious consumers alike.

Regionally, Asia Pacific dominates the gold target market, accounting for a significant share due to its large population, cultural affinity for gold, and increasing economic power. North America and Europe follow with substantial market contributions, driven by investment demand and industrial applications. The Middle East, with its strong cultural and economic ties to gold, also presents a lucrative market, while Latin America is emerging as a notable player due to its rich natural gold reserves and growing investments in mining infrastructure.

Product Type Analysis

The segmentation of the gold market by product type includes bullion, coins, jewelry, and exchange-traded funds (ETFs). Gold bullion, comprising bars and ingots, represents a significant portion of the market due to its traditional use as a store of value and its appeal to both retail and institutional investors. As a tangible asset, bullion is favored for its purity and weight, often considered the most direct way to hold gold. The demand for bullion remains robust amidst economic uncertainties, with investors seeking security against market fluctuations and geopolitical tensions.

Coins are

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 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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A line chart illustrating the forecasted retail price per gram (AED) for 22 and 24 Carat gold in Dubai for the next 2 weeks, 2 months, and 6 months, based on data reference date March 17, 2025.

Content

Daily gold prices (2014-01-01 to 2025-01-06)

Acknowledgements

Raw Data Source: MCX Market This data frame is pre-processed to time series analysis and forecasting

Inspiration

Forecast, Predict Prices, Time Series Forecasting

Note

Prices are in INR (Indian Rupee) for 10gm of Gold 24K.

Gold Prices in this dataset makes no guarantee or warranty on the accuracy or completeness of the data provided.

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