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Dataset Card for Sentiment Analysis of Commodity News (Gold)

This is a news dataset for the commodity market which has been manually annotated for 10,000+ news headlines across multiple dimensions into various classes. The dataset has been sampled from a period of 20+ years (2000-2021). The dataset was curated by Ankur Sinha and Tanmay Khandait and is detailed in their paper "Impact of News on the Commodity Market: Dataset and Results." It is currently published by the authors on… See the full description on the dataset page: https://huggingface.co/datasets/SaguaroCapital/sentiment-analysis-in-commodity-market-gold.

Sharp economic volatility, the continued effects of high interest rates and mixed sentiment among investors created an uneven landscape for stock and commodity exchanges. While trading volumes soared in 2020 due to the pandemic and favorable financial conditions, such as zero percent interest rates from the Federal Reserve, the continued effects of high inflation in 2022 and 2023 resulted in a hawkish pivot on interest rates, which curtailed ROIs across major equity markets. Geopolitical volatility amid the Ukraine-Russia and Israel-Hamas wars further exacerbated trade volatility, as many investors pivoted away from traditional equity markets into derivative markets, such as options and futures to better hedge on their investment. Nonetheless, the continued digitalization of trading markets bolstered exchanges, as they were able to facilitate improved client service and stronger market insights for interested investors. Revenue grew an annualized 0.1% to an estimated $20.9 billion over the past five years, including an estimated 1.9% boost in 2025. A core development for exchanges has been the growth of derivative trades, which has facilitated a significant market niche for investors. Heightened options trading and growing attraction to agricultural commodities strengthened service diversification among exchanges. Major companies, such as CME Group Inc., introduced new tradeable food commodities for investors in 2024, further diversifying how clients engage in trades. These trends, coupled with strengthened corporate profit growth, bolstered exchanges’ profit. Despite current uncertainty with interest rates and the pervasive fear over a future recession, the industry is expected to do well during the outlook period. Strong economic conditions will reduce investor uncertainty and increase corporate profit, uplifting investment into the stock market and boosting revenue. Greater levels of research and development will expand the scope of stocks offered because new companies will spring up via IPOs, benefiting exchange demand. Nonetheless, continued threat from substitutes such as electronic communication networks (ECNs) will curtail larger growth, as better technology will enable investors to start trading independently, but effective use of electronic platforms by incumbent exchange giants such as NASDAQ Inc. can help stem this decline by offering faster processing via electronic trade floors and prioritizing client support. Overall, revenue is expected to grow an annualized 3.5% to an estimated $24.8 billion through the end of 2031.

Gold traded flat at 3,335.62 USD/t.oz on August 15, 2025. Over the past month, Gold's price has fallen 0.34%, but it is still 33.01% higher than a year ago, 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 August of 2025.

Graph and download economic data for Equity Market Volatility Tracker: Commodity Markets (EMVCOMMMKT) from Jan 1985 to Jul 2025 about volatility, uncertainty, equity, commodities, and USA.

The global commodity trading services market is experiencing robust growth, driven by increasing globalization, fluctuating commodity prices, and the need for efficient supply chain management. The market size in 2025 is estimated at $2 trillion, exhibiting a Compound Annual Growth Rate (CAGR) of 6% between 2025 and 2033. This growth is fueled by several key factors. Firstly, the rising demand for raw materials across various sectors, including metals, energy, and agriculture, is creating lucrative opportunities for commodity trading firms. Secondly, technological advancements in areas like data analytics and blockchain technology are improving transparency, efficiency, and risk management within commodity trading, further stimulating market expansion. Finally, the increasing complexity of global supply chains necessitates the expertise of specialized commodity traders to navigate market volatility and ensure secure and timely delivery of goods. The market is segmented by commodity type (metals, energy, agricultural, and others) and by the size of the businesses served (large enterprises and SMEs). While large enterprises dominate the market currently, the SME segment shows strong potential for future growth as businesses increasingly rely on external expertise for commodity sourcing. The geographical distribution of the commodity trading services market is diverse, with North America, Europe, and Asia Pacific representing the major regions. However, emerging markets in Asia and Africa are showing significant growth potential due to rapid industrialization and rising consumer demand. Competitive pressures within the industry are high, with numerous large multinational corporations vying for market share. These companies, including Vitol, Glencore, Trafigura, Mercuria, and Cargill, possess extensive global networks, strong financial capabilities, and deep expertise in risk management, allowing them to dominate the market. Nevertheless, smaller, specialized trading firms are also finding success by focusing on niche markets or employing innovative trading strategies. The overall outlook for the commodity trading services market remains optimistic, with continued growth expected over the coming years, albeit with some potential challenges related to geopolitical instability and regulatory changes.

Commodity Trading Platform Market Outlook

The global commodity trading platform market size was valued at approximately USD 3.5 billion in 2023 and is expected to reach around USD 7.2 billion by 2032, growing at a CAGR of 8.2% from 2024 to 2032. This growth is driven by increasing digitalization, expanding global trade, and rising demand for efficient trading solutions. The digital transformation in trading activities, coupled with the need for real-time data and analytics, is propelling the adoption of advanced trading platforms across the globe.

One of the significant growth factors for the commodity trading platform market is the increasing adoption of digital technologies in trading activities. As the trading landscape becomes more complex and competitive, institutional and retail investors are seeking more sophisticated tools that can offer real-time data analysis, risk management, and automated trading capabilities. The integration of AI and machine learning in these platforms is further enhancing their efficiency and decision-making capabilities, thereby driving market growth.

Another crucial factor contributing to the market's expansion is the globalization of trade. With the world becoming increasingly interconnected, there is a growing need for platforms that can handle the complexities of international trading. These platforms offer features such as multi-currency support, compliance with regional regulations, and real-time tracking of global market trends, making them indispensable tools for traders operating on a global scale. Additionally, the rise in cross-border e-commerce and international investments is further fueling the demand for advanced commodity trading platforms.

The growing focus on sustainability and ethical trading practices is also influencing the market positively. As more investors and companies prioritize Environmental, Social, and Governance (ESG) criteria in their trading activities, there is a rising demand for platforms that can provide transparency and traceability in commodity sourcing and trading. This trend is particularly evident in the agriculture and energy sectors, where there is increasing scrutiny on the environmental and social impacts of trading activities.

The role of Derivatives And Commodities Brokerage is becoming increasingly pivotal in the commodity trading platform market. These brokerages act as intermediaries, facilitating trades between buyers and sellers in the commodities market. With the rise of digital trading platforms, brokerages are evolving to offer more sophisticated services, including real-time data analytics, risk management tools, and automated trading options. This evolution is crucial as it enables traders to navigate the complexities of the global commodities market more efficiently. The integration of AI and machine learning technologies by these brokerages is further enhancing their ability to provide tailored trading solutions, thereby attracting a broader range of clients from institutional to retail investors.

From a regional perspective, North America currently holds a significant share of the commodity trading platform market, driven by the presence of major market players and high adoption rates of advanced trading technologies. However, regions like Asia Pacific are expected to witness the highest growth rates during the forecast period. The rapid economic growth, expanding middle-class population, and increasing digital literacy in countries like China and India are key factors contributing to this regional growth. Moreover, the liberalization of trade policies and investment in digital infrastructure are further supporting the market's expansion in these regions.

Component Analysis

The commodity trading platform market can be segmented by component into software and services. The software segment includes various types of platforms such as trading software, risk management software, and analytical tools. These software solutions are designed to provide traders with real-time data, automated trading options, and advanced analytical capabilities. The increasing complexity of trading activities and the need for high-speed transactions are driving the demand for sophisticated software solutions. Moreover, the integration of AI and machine learning technologies in trading software is enhancing their functionality and efficiency, making them more attractive to traders.

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Analysis of ‘Sentiment Analysis of Commodity News (Gold)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ankurzing/sentiment-analysis-in-commodity-market-gold on 14 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This is a news dataset for the commodity market where we have manually annotated 11,412 news headlines across multiple dimensions into various classes. The dataset has been sampled from a period of 20+ years (2000-2021).

Content

The dataset has been collected from various news sources and annotated by three human annotators who were subject experts. Each news headline was evaluated on various dimensions, for instance - if a headline is a price related news then what is the direction of price movements it is talking about; whether the news headline is talking about the past or future; whether the news item is talking about asset comparison; etc.

Acknowledgements

Sinha, Ankur, and Tanmay Khandait. "Impact of News on the Commodity Market: Dataset and Results." In Future of Information and Communication Conference, pp. 589-601. Springer, Cham, 2021.

https://arxiv.org/abs/2009.04202 Sinha, Ankur, and Tanmay Khandait. "Impact of News on the Commodity Market: Dataset and Results." arXiv preprint arXiv:2009.04202 (2020)

We would like to acknowledge the financial support provided by the India Gold Policy Centre (IGPC).

Inspiration

Commodity prices are known to be quite volatile. Machine learning models that understand the commodity news well, will be able to provide an additional input to the short-term and long-term price forecasting models. The dataset will also be useful in creating news-based indicators for commodities.

Apart from researchers and practitioners working in the area of news analytics for commodities, the dataset will also be useful for researchers looking to evaluate their models on classification problems in the context of text-analytics. Some of the classes in the dataset are highly imbalanced and may pose challenges to the machine learning algorithms.

--- Original source retains full ownership of the source dataset ---

Natural gas rose to 2.92 USD/MMBtu on August 15, 2025, up 2.83% from the previous day. Over the past month, Natural gas's price has fallen 17.73%, but it is still 37.60% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Natural gas - values, historical data, forecasts and news - updated on August of 2025.

Real-time commodities pricing data allows you to grasp where the market is, was and will be – from exchange data and OTC prices to specialist fundamentals.

Wheat rose to 507 USd/Bu on August 15, 2025, up 0.70% from the previous day. Over the past month, Wheat's price has fallen 6.33%, and is down 4.34% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Wheat - values, historical data, forecasts and news - updated on August of 2025.

Graph and download economic data for Global Price Index of All Commodities (PALLFNFINDEXQ) from Q1 2003 to Q2 2025 about World, commodities, price index, indexes, and price.

Crude Oil fell to 63.14 USD/Bbl on August 15, 2025, down 1.28% from the previous day. Over the past month, Crude Oil's price has fallen 3.14%, and is down 16.42% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on August of 2025.

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China Commodity Trading Market over 100 M Yuan: Turnover: Retail: Books, Newspapers and Magazines Market data was reported at 1.835 RMB bn in 2023. This records an increase from the previous number of 1.778 RMB bn for 2022. China Commodity Trading Market over 100 M Yuan: Turnover: Retail: Books, Newspapers and Magazines Market data is updated yearly, averaging 1.399 RMB bn from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 2.094 RMB bn in 2021 and a record low of 1.233 RMB bn in 2010. China Commodity Trading Market over 100 M Yuan: Turnover: Retail: Books, Newspapers and Magazines Market data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJA: Commodity Trading Market over 100 Million Yuan: Turnover: Retail.

Stay informed about recent news updates and trends related to the crude oil commodity market, including OPEC production cuts, geopolitical tensions, demand recovery amid COVID-19, renewable energy initiatives, and inventory levels and stockpile data.

This dataset aggregates daily wholesale price data for a wide spectrum of agricultural commodities traded across India’s regulated markets (mandis). It captures minimum, maximum, and modal prices, enabling detailed analysis of price dispersion and volatility over time. Data is sourced directly from the AGMARKNET portal and made available under the National Data Sharing and Accessibility Policy (NDSAP). With over 165,000 views and nearly 400,000 downloads, it’s a cornerstone resource for economists, agronomists, and data scientists studying India’s commodity markets.

This dataset provides daily wholesale minimum, maximum, and modal prices for a wide variety of agricultural commodities across India’s mandis, sourced from the AGMARKNET portal and published on Data.gov.in under NDSAP, with records dating back to 2013 and updated as of 19 May 2025 via a REST API; it includes key fields like Arrival_Date, State, District, Market, Commodity, Variety, Min_Price, Max_Price, and Modal_Price, making it ideal for time-series analysis, price-trend visualizations, and commodity forecasting.

China Commodity Trading Market over 100 M Yuan: Number of Booth: Wood Market data was reported at 7,122.000 Unit in 2023. This records a decrease from the previous number of 7,673.000 Unit for 2022. China Commodity Trading Market over 100 M Yuan: Number of Booth: Wood Market data is updated yearly, averaging 16,080.000 Unit from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 20,682.000 Unit in 2012 and a record low of 7,122.000 Unit in 2023. China Commodity Trading Market over 100 M Yuan: Number of Booth: Wood Market data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJA: Commodity Trading Market over 100 Million Yuan: Number of Booth.

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.

TR/CC CRB Ex Energy Index: A Reliable Indicator of Commodity Market Health?

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

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