Natural gas rose to 4.94 USD/MMBtu on December 3, 2025, up 2.04% from the previous day. Over the past month, Natural gas's price has risen 13.71%, and is up 62.29% compared to the same time last year, 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 December of 2025.

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.

United States New York Stock Exchange: Index: NYSE Arca Natural Gas Index data was reported at 656.507 NA in Apr 2025. This records a decrease from the previous number of 699.391 NA for Mar 2025. United States New York Stock Exchange: Index: NYSE Arca Natural Gas Index data is updated monthly, averaging 552.633 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 961.450 NA in Jun 2014 and a record low of 165.000 NA in Mar 2020. United States New York Stock Exchange: Index: NYSE Arca Natural Gas Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: Monthly.

TTF Gas fell to 27.92 EUR/MWh on December 3, 2025, down 0.17% from the previous day. Over the past month, TTF Gas's price has fallen 14.22%, and is down 40.94% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. EU Natural Gas - values, historical data, forecasts and news - updated on December 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).

This dataset encapsulates a detailed examination of market dynamics over a five-year period, focusing on the fluctuation of prices and trading volumes across a diversified portfolio. It covers various sectors including energy commodities like natural gas and crude oil, metals such as copper, platinum, silver, and gold, cryptocurrencies including Bitcoin and Ethereum, and key stock indices and companies like the S&P 500, Nasdaq 100, Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta Platforms. This dataset serves as a valuable resource for analyzing trends and patterns in global markets.

Date: The date of the recorded data, formatted as DD-MM-YYYY. Natural_Gas_Price: Price of natural gas in USD per million British thermal units (MMBtu). Natural_Gas_Vol.: Trading volume of natural gas Crude_oil_Price: Price of crude oil in USD per barrel. Crude_oil_Vol.: Trading volume of crude oil Copper_Price: Price of copper in USD per pound. Copper_Vol.: Trading volume of copper Bitcoin_Price: Price of Bitcoin in USD. Bitcoin_Vol.: Trading volume of Bitcoin Platinum_Price: Price of platinum in USD per troy ounce. Platinum_Vol.: Trading volume of platinum Ethereum_Price: Price of Ethereum in USD. Ethereum_Vol.: Trading volume of Ethereum S&P_500_Price: Price index of the S&P 500. Nasdaq_100_Price: Price index of the Nasdaq 100. Nasdaq_100_Vol.: Trading volume for the Nasdaq 100 index Apple_Price: Stock price of Apple Inc. in USD. Apple_Vol.: Trading volume of Apple Inc. stock Tesla_Price: Stock price of Tesla Inc. in USD. Tesla_Vol.: Trading volume of Tesla Inc. stock Microsoft_Price: Stock price of Microsoft Corporation in USD. Microsoft_Vol.: Trading volume of Microsoft Corporation stock Silver_Price: Price of silver in USD per troy ounce. Silver_Vol.: Trading volume of silver Google_Price: Stock price of Alphabet Inc. (Google) in USD. Google_Vol.: Trading volume of Alphabet Inc. stock Nvidia_Price: Stock price of Nvidia Corporation in USD. Nvidia_Vol.: Trading volume of Nvidia Corporation stock Berkshire_Price: Stock price of Berkshire Hathaway Inc. in USD. Berkshire_Vol.: Trading volume of Berkshire Hathaway Inc. stock Netflix_Price: Stock price of Netflix Inc. in USD. Netflix_Vol.: Trading volume of Netflix Inc. stock Amazon_Price: Stock price of Amazon.com Inc. in USD. Amazon_Vol.: Trading volume of Amazon.com Inc. stock Meta_Price: Stock price of Meta Platforms, Inc. (formerly Facebook) in USD. Meta_Vol.: Trading volume of Meta Platforms, Inc. stock Gold_Price: Price of gold in USD per troy ounce. Gold_Vol.: Trading volume of gold

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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.

Junior Gas Futures Poised for Moderate Gains, Analysts Predict

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

UK Gas fell to 72.60 GBp/thm on December 2, 2025, down 1.67% from the previous day. Over the past month, UK Gas's price has fallen 11.75%, and is down 40.33% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. UK Natural Gas - values, historical data, forecasts and news - updated on December of 2025.

Abstract of associated article: This paper analyzes the effects of the Commodity Futures Trading Commission's (CFTC) announcements on the stock returns of oil and gas companies around the financial crisis of 2008. Using event study methodology and regression analyses, we examine a set of 122 oil and gas related stocks listed in the New York Stock Exchange (NYSE) for 35 announcements. Our results indicate that CFTC announcements, depending on their content, can affect the stock returns of oil and gas companies. In particular, this is found to hold true during the period of high-volatility in oil prices, i.e., the period following Lehman Brothers failure. During this period, oil and gas related stock returns respond positively to most regulatory announcements, showing that the CFTC's regulatory interventions are perceived positively by the stock market.

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 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.

Northern Oil and Gas (NOG) - Poised for Growth in the Energy Landscape

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

ExxonMobil ranks first among the United States' top ten oil and gas producing companies based on market capitalization. As of June 18, 2025, the Texas-based oil supermajor had a market cap of ****** billion U.S. dollars. ExxonMobil can not only trace its roots back to the early years of commercial oil production, it has also become one of the largest oil and gas companies in the world. It is active in all areas of the supply chain, from hydrocarbon extraction to retailing of gasoline. What is market cap? As opposed to sales or assets, market capitalization is a metric used to determine a company’s size by the worth of their outstanding shares on the stock market. ExxonMobil often ranks as the leading oil and gas company based on market cap worldwide. However, its net income is often significantly lower than that of state-owned entities such as Saudi Aramco. The differing ratios exemplify how market cap is not a hard figure like net profits, but inflates and fluctuates according to the perceived value of a company, influenced by less quantifiable factors. The role of oil and gas in the world economy The oil and gas industry is involved in exploration, extraction, refining, transport, and marketing of hydrocarbons. Many industries are extremely dependent on oil and gas products, mostly in the form of fuels or raw materials for chemical products. The oil and gas industry is one of the largest worldwide, and it would follow that companies involved within the industry are among the top companies worldwide by revenue.

This file holds the data used for the manuscript 'Shakers and movers: Stock market response to induced seismicity in oil and gas business'. It holds the information about induced seismicity in Oklahoma (US) and Groningen (Netherlands) fields and the information from the stock markets.

Data and Figures 1-9

***** was the leading oil and gas company in the United Kingdom (UK) as of October 15, 2024, with a market capitalization amounting to over *** billion U.S. dollars. ** followed as the second-largest oil and gas company in the country, with a market cap of approximately ***** billion U.S. dollars.

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.

Is the Dow Jones North America Select Junior Gas Index Poised for Growth?

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

Gasoline fell to 1.86 USD/Gal on December 2, 2025, down 0.53% from the previous day. Over the past month, Gasoline's price has fallen 2.79%, and is down 4.95% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on December of 2025.

Net-Buyback-Yield Time Series for Antero Midstream Partners LP. Antero Midstream Corporation owns, operates, and develops midstream energy assets in the Appalachian Basin. It operates in two segments, Gathering and Processing, and Water Handling. The Gathering and Processing segment includes a network of gathering pipelines, compressor stations, and processing and fractionation plants that collects and processes natural gas and NGLs from Antero Resources' wells in West Virginia and Ohio. The Water Handling segment delivers water from sources, including the Ohio River, local reservoirs, and various regional waterways; uses water handling systems to transport flowback and produced water; and offers pumping stations, water storage, and blending facilities. The company was founded in 2002 and is headquartered in Denver, Colorado.

Cash-Flows-Other-Operating Time Series for CNX Resources Corp. CNX Resources Corporation, an independent natural gas and midstream company, engages in the acquisition, exploration, development, and production of natural gas properties in the Appalachian Basin. The company operates in two segments, Shale and Coalbed Methane (CBM). It produces and sells pipeline quality natural gas primarily for gas wholesalers. The company owns rights to extract natural gas from shale properties in Pennsylvania, West Virginia, and Ohio, as well as rights to extract natural gas from other shale and shallow oil and gas formations in Illinois, Indiana, New York, and Virginia. It also owns rights to extract CBM in Virginia, West Virginia, Pennsylvania, Ohio, Illinois, Indiana, and New Mexico. In addition, the company designs, builds, and operates natural gas gathering systems to move gas from the wellhead to interstate pipelines or other local sales points; owns and operates approximately 2,700 miles of natural gas gathering pipelines, as well as various natural gas processing facilities. It also offers turn-key solutions for water sourcing, delivery, and disposal for its natural gas operations and for third parties. The company was formerly known as CONSOL Energy Inc. and changed its name to CNX Resources Corporation in November 2017. CNX Resources Corporation was founded in 1860 and is headquartered in Canonsburg, Pennsylvania.

Selling-General-and-Administrative Time Series for Warrior Met Coal Inc. Warrior Met Coal, Inc. engages in the production and export of non-thermal steelmaking coal for the steel production by metal manufacturers in Europe, South America, and Asia. The company offers hard-coking coal through the operation of underground mines located in Alabama. It also sells natural gas extracted as a byproduct from coal production. The company was incorporated in 2015 and is headquartered in Brookwood, Alabama.