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.

This statistic shows the stock prices of selected food commodities from January 2, 2020 to February 6, 2025. After the Russian invasion of Ukraine in February 2022, wheat prices increased significantly since both Russia and Ukraine are the key suppliers of the product. With the beginning of 2023, prices of selected food commodities started to decrease, but still stood higher than early-2020 levels.

Stock and commodity exchanges can benefit from various sources of revenue, ranging from fees charged through the purchasing and selling of stocks and commodities to the listing of companies on exchanges with IPOs. Yet, this hasn't meant exchanges have been free of challenges, with many companies looking to more attractive overseas markets in countries like the US that embrace stronger growth. The most notable culprits have been ARM and CRH, refusing to put up with the increasingly cheaper valuations offered by UK stock exchanges. Stock and commodity exchange revenue is expected to boom at a compound annual rate of 11.5% over the five years through 2024-25 to £15.4 billion. Boosted by the London Stock Exchange Group's Refinitiv purchase in 2021-22, the growth numbers seem inflated. The industry saw ample consolidations, aided by MiFID II's initiation in 2018. However, M&As have now decreased because of high borrowing costs. New reporting demands have bumped up regulatory costs, resulting in thinner profits. Banks, aligning with Basel IV, are pulling back on investments. Post-COVID market turbulence fuelled trades, but it's slowing down with economic stabilisation. The inflation slowdown pushes investors towards higher-value securities, boosting trade value despite lower volumes. The weak pound has been beneficial for revenue, especially for the LSEG, bolstered by dollar-earning companies in the FTSE 100. Stock and commodity exchange industry revenue is expected to show a moderate increase of 1.3% in 2024-25. Revenue is forecast to climb at a compound annual rate of 4.1% over the five years through 2029-30 to £18.8 billion. The cautious descent of interest rates from the Bank of England will slow down volatility and ensure greater business confidence in the UK. This will bring back up consolidation activity to support revenue growth, reviving the digital information and exchange markets. The most pressing concern for the industry will be potential limitations on access to the EEA for the clearing segment of the industry, which could shatter short-term growth and keep the tap running for companies exiting UK exchanges.

This statistic shows the stock prices of selected oil and gas commodities from January 2, 2020 to February 4, 2025. After the Russian invasion of Ukraine in February 2022, energy prices climbed significantly. The highest increase can be observed for natural gas, whose price peaked in August and September 2022. By the beginning of 2023, natural gas price started to decline.

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

This statistic shows the stock prices of selected raw material commodities from January 2, 2020 to February 5, 2025. After the Russian invasion of Ukraine in February 2022, metal prices increased significantly due to disruptions to supply chain and increased demand. Since then, stock values of raw materials started to decrease albeit with some fluctuations.

Commodity Index Funds Market Outlook

The global commodity index funds market size was valued at approximately $200 billion in 2023 and is projected to reach nearly $400 billion by 2032, growing at a robust CAGR of 7.5% during the forecast period. The significant growth in this market can be attributed to the increasing demand for diversification in investment portfolios and the inherent benefits of hedging against inflation that commodity investments provide. Furthermore, the volatility in global stock markets and geopolitical uncertainties have led investors to seek safer, more stable investment avenues, thus driving the growth of commodity index funds.

One of the primary growth factors propelling the commodity index funds market is the rising awareness among investors about the advantages of commodity investments as a hedge against inflation. Commodities, unlike stocks and bonds, often move inversely to the stock market, providing a cushion during market downturns. This characteristic makes commodity index funds an attractive option for risk-averse investors and those looking to balance their portfolios. Additionally, the globalization of trade and the increasing demand for raw materials in emerging markets have further spurred the demand for commodity investments.

Technological advancements in trading platforms have also significantly contributed to the growth of this market. The advent of sophisticated online platforms has made it easier for retail investors to access and invest in commodity index funds. These platforms offer a range of tools and resources that help investors make informed decisions, thereby democratizing access to commodity investments. Moreover, the rise of robo-advisors and algorithm-based trading strategies has further simplified the investment process, attracting a new generation of tech-savvy investors.

The regulatory landscape has also played a crucial role in shaping the commodity index funds market. Governments and financial regulatory bodies across the globe have been working to create a transparent and secure trading environment. Regulatory reforms aimed at reducing market manipulation and increasing transparency have instilled confidence among investors, thereby boosting the market. Additionally, tax incentives and favorable policies for commodity investments in various countries have also contributed to market growth.

In terms of regional outlook, North America holds a significant share of the global commodity index funds market, followed by Europe and Asia Pacific. The presence of well-established financial markets and a high level of investor awareness in North America are key factors driving the market in this region. Europe, with its strong regulatory framework and increasing adoption of alternative investment strategies, is also witnessing substantial growth. Meanwhile, the Asia Pacific region is emerging as a lucrative market, driven by the rapid economic growth in countries like China and India, and the increasing interest in commodity investments among institutional and retail investors.

Fund Type Analysis

When analyzing the market by fund type, Broad Commodity Index Funds dominate the landscape. These funds invest in a diversified portfolio of commodities, making them a popular choice for investors seeking broad exposure to the commodity markets. The broad commodity index funds are designed to track the performance of a basket of commodities, ranging from energy products to metals and agricultural goods. This diversification helps mitigate risks associated with the volatility of individual commodities, thereby providing a more stable investment option for risk-averse investors.

Single Commodity Index Funds, on the other hand, focus on specific commodities such as gold, oil, or agricultural products. These funds appeal to investors who have a strong conviction about the performance of a particular commodity. For instance, during periods of economic uncertainty, gold-focused funds often see a surge in demand as investors flock to the safe-haven asset. Similarly, energy-focused funds attract investors when there are disruptions in oil supply or significant geopolitical events affecting oil prices. While these funds offer the potential for high returns, they also come with higher risks due to their lack of diversification.

Sector Commodity Index Funds are another important segment within the commodity index funds market. These funds concentrate on commodities within a specific sector, such as energy, agriculture, or metals, allowing investors to target particular segments of the commo

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.

Orange Juice rose to 288.86 USd/Lbs on July 12, 2025, up 9.48% from the previous day. Over the past month, Orange Juice's price has risen 5.38%, but it is still 36.04% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Orange Juice - values, historical data, forecasts and news - updated on July of 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SGI Commodities Optimix TR index Sees Moderate Growth 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

Coffee fell to 288.72 USd/Lbs on July 11, 2025, down 0.41% from the previous day. Over the past month, Coffee's price has fallen 16.63%, but it is still 15.90% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coffee - values, historical data, forecasts and news - updated on July of 2025.

Brain Sentiment Indicator [version Currencies, Cryptocurrencies and Commodities] monitors public financial news for 8 currencies, more than 10 cryptocurrencies and more than 60 commodities from about 2000 financial media sources in 33 languages.

The sentiment scoring technology is based on a combination of various natural language processing techniques.

The sentiment score assigned to each stock is a value ranging from -1 (most negative) to +1 (most positive) that is updated with a daily frequency. The sentiment score corresponds to the average of sentiment for each piece of news and it is available on two time scales; 7 days and 30 days.

1. Financial news are collected every few minutes from various financial media

2. Brain engine assigns a specific category to each piece of news (e.g. “patent win” or “contract lose”) using semantic rules. Each category has a predefined value of sentiment.

3. If the categorization fails a bag of words approach is used based on dictionaries customized for Financial news. The approach includes a strategy for negation handling.

4. Repetition of similar news is kept into account in the sentiment aggregation.

The sentiment data for each piece of news is averaged on two time scales, considering the piece of news of last 7 days and of last 30 days. The data are exported daily and are available by 6.00 AM UTC on a dedicated S3 bucket..

Graph and download economic data for Producer Price Index by Commodity: Lumber and Wood Products: Softwood Cut Stock and Dimension (WPU08110503) from Jan 1987 to May 2025 about stocks, wood, commodities, PPI, inflation, price index, indexes, price, and USA.

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

Search LSEG's Commodities Data, and find global pricing, exchanges, and fundamentals for energy, agriculture, and metals.

General Index provides robust, reliable price transparency for the world’s commodity markets with quick and convenient access through a single desktop.

GSCI rose to 551.39 Index Points on July 11, 2025, up 0.98% from the previous day. Over the past month, GSCI's price has risen 0.10%, but it is still 3.67% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. GSCI Commodity Index - values, historical data, forecasts and news - updated on July of 2025.

The datasets for the Role of Financial Investors on Commodity Futures Risk Premium are weekly datasets for the period from 1995 to 2015 for three commodities in the energy market: crude oil (WTI), heating oil, and natural gas. These datasets contain futures prices for different maturities, open interest positions for each commodity (long and short open interest positions), and S&P 500 composite index. The selected commodities are traded on the New York Mercantile Exchange (NYMEX). The data comes from the Thomson Reuters Datastream and from the Commodity Futures Trading Commission (CFTC).

Returns of the series used in the publication "Return connectedness between energy commodities and stock markets: New evidence from 31 energy sector companies in Europe" (Just M, Kliber A, Echaust K)

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.

StoneX: (SNEX) Riding the Commodities Wave

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