Learn about the complex relationship between crude oil prices and the DJIA (Dow Jones Industrial Average) and how they impact the economy and stock market. Understand the various factors that influence both indicators and how traders and investors analyze them to make investment decisions.

Brent rose to 64.35 USD/Bbl on October 3, 2025, up 0.37% from the previous day. Over the past month, Brent's price has fallen 3.94%, and is down 17.55% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Brent crude oil - values, historical data, forecasts and news - updated on October of 2025.

Graph and download economic data for Spot Oil Price: West Texas Intermediate (DISCONTINUED) (OILPRICE) from Jan 1946 to Jul 2013 about west, WTI, intermediate, oil, commodities, price, and USA.

Learn the difference between the Dow Jones Industrial Average (DJIA) and oil prices, and how to access live oil prices from reputable financial platforms for up-to-date information on commodity prices.

The Dow Jones oil price per barrel is an important indicator used to track the performance and trends of the oil market. Learn how it is influenced by major oil and gas companies, global supply and demand dynamics, and other factors. Discover why it should be considered alongside other indicators for a comprehensive understanding of the oil market.

Today, the Dow Jones Crude Oil Prices have shown a significant increase. Find out the key factors impacting the rise in oil prices, including OPEC+ production cuts, COVID-19 vaccine rollout, geopolitical tensions, and global economic indicators. Understand why crude oil prices are volatile and the implications for investors, energy companies, and governments worldwide.

Graph and download economic data for Spot Oil Price: West Texas Intermediate (DISCONTINUED) from Jan 1946 to Jul 2013 about west, WTI, intermediate, oil, commodities, price, and USA.

The Dow Jones U.S. Select Oil Equipment & Services index forecasts a positive trend. However, there are risks associated with this prediction, including a potential downturn in the oil and gas industry, supply chain disruptions, and geopolitical uncertainties.

Index Time Series for DWCOGS: Dow Jones U.S. Oil & Gas Tota. The frequency of the observation is daily. Moving average series are also typically included.

The Dow Jones North America Select Junior Oil index may exhibit upward momentum, potentially reaching slightly higher levels. However, it's important to note that this prediction carries moderate risk due to potential market fluctuations and geopolitical uncertainties that could influence the oil industry.

Today, the Dow Jones Industrial Average had a mixed day, opening at a record high but closing slightly lower. On the other hand, crude oil prices experienced a significant rise due to factors such as economic recovery optimism, supply constraints, and anticipation of increased summer demand.

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.

Will the Dow Jones U.S. Oil & Gas Index Continue its Climb?

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.

Will the Dow Jones U.S. Oil & Gas Index Fuel Further Gains?

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.

Dow Jones U.S. Select Oil Equipment & Services: Poised for Growth or Facing Headwinds?

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

Discover how the performance of oil stocks can have a substantial impact on the Dow Jones Industrial Average, as oil prices are influenced by various factors such as supply and demand dynamics, geopolitical events, and economic indicators.

Discover the impact of Israel-Iran tensions on global markets, with fluctuating US stock futures and rising oil prices amid geopolitical concerns.

The Dow Jones Live Crude Oil is a real-time index that tracks the performance of crude oil futures contracts. It provides investors and traders with valuable information on the current price and movement of crude oil in the market.

The global market for pour point depressants for crude oil is experiencing steady growth, projected to reach a value of $625.6 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 3.3% from 2025 to 2033. This growth is fueled by several key factors. The increasing demand for crude oil transportation and extraction, particularly in challenging environments with low temperatures, necessitates the use of pour point depressants to maintain the fluidity of crude oil and prevent pipeline blockages. Furthermore, advancements in polymer and surfactant-based depressant technologies are leading to more efficient and environmentally friendly solutions, further driving market expansion. The segmentation of the market into application (crude oil transportation, extraction, and processing) and type (polymer, surfactant, and compound) reflects the diverse needs of the oil and gas industry. Major players like Evonik, Clariant, Dow, BASF, and Huntsman are actively involved in research and development, fostering innovation and competition within the sector. Regional variations in market share are expected, with North America and the Asia-Pacific region likely to remain significant contributors due to their extensive oil and gas infrastructure and production activities. However, emerging markets in regions like the Middle East and Africa also present considerable growth opportunities. The continued expansion of the oil and gas industry, coupled with stricter environmental regulations promoting cleaner technologies, will shape the future landscape of the pour point depressant market. Growth will likely be influenced by fluctuations in crude oil prices, geopolitical factors, and technological advancements in both depressant formulations and application methods. The increasing focus on sustainable practices within the oil and gas sector will likely drive demand for biodegradable and less environmentally impactful pour point depressants. Competition among existing players and the emergence of new entrants will further intensify, necessitating strategic collaborations, mergers and acquisitions, and continuous innovation to maintain market share and profitability. The long-term outlook remains positive, driven by the ongoing need to efficiently transport and process crude oil, particularly in harsh climatic conditions.

The dataset was collected for the period spanning between 01/07/2019 and 31/12/2022.The historical Twitter volume were retrieved using ‘‘Bitcoin’’ (case insensitive) as the keyword from bitinfocharts.com. Google search volume was retrieved using library Gtrends. 2000 tweets per day using 4 times interval were crawled by employing Twitter API with the keyword “Bitcoin. The daily closing prices of Bitcoin, oil price, gold price, and U.S stock market indexes (S&P 500, NASDAQ, and Dow Jones Industrial Average) were collected using R libraries either Quantmod or Quandl.

The global wax inhibitor market is experiencing robust growth, driven by the increasing demand for efficient crude oil transportation and processing. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 5% from 2025 to 2033, reaching an estimated market size of $3.8 billion by 2033. This growth is fueled by several key factors, including the rising production and transportation of high-wax crude oil, stringent environmental regulations promoting efficient flow assurance, and technological advancements leading to the development of more effective and environmentally friendly wax inhibitor formulations. Major market players, including BASF, Dow Chemical, and Chemiphase International, are actively investing in research and development to enhance product performance and expand their market share. The increasing adoption of polymer-based wax inhibitors, owing to their superior performance and cost-effectiveness compared to traditional surfactant-based solutions, is further driving market expansion. Segment-wise, the crude oil transportation application holds a significant share, owing to the crucial role wax inhibitors play in preventing pipeline blockages. However, the crude oil processing segment is also witnessing substantial growth due to the rising demand for efficient wax removal during refining processes. Geographic segmentation reveals a strong presence in North America and Europe, attributed to the established oil and gas infrastructure and significant crude oil production and refining activities. Asia-Pacific is also a rapidly growing market, driven by increasing energy demands and infrastructure development in countries such as China and India. However, factors such as fluctuating oil prices and the development of alternative solutions may pose challenges to market growth in the forecast period. Continued innovation in inhibitor chemistry and the expansion into emerging markets will be crucial for sustained market expansion. This report provides a detailed analysis of the global wax inhibitor market, projecting a market value exceeding $2.5 billion by 2028. It delves into market dynamics, key players, and future growth prospects, focusing on crucial aspects like concentration levels, regulatory impacts, and emerging trends. This report is essential for industry stakeholders, investors, and researchers seeking a comprehensive understanding of this crucial segment of the oil and gas industry. Keywords: Wax inhibitor, oil and gas, pipeline flow assurance, polymer wax inhibitor, surfactant wax inhibitor, crude oil processing, flow improver, paraffin inhibitor.