Crude Oil rose to 68.75 USD/Bbl on July 11, 2025, up 3.27% from the previous day. Over the past month, Crude Oil's price has risen 1.04%, but it is still 16.37% lower than a year ago, 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 July of 2025.

The US stock market and crude oil price are closely interconnected as crude oil is a critical component of the global economy, and its price fluctuations directly impact the stock market. This article explores the various factors that influence both the US stock market and crude oil prices, including geopolitical events, global economic conditions, supply and demand dynamics, and government policies.

Brent rose to 70.69 USD/Bbl on July 11, 2025, up 2.99% from the previous day. Over the past month, Brent's price has risen 1.92%, but it is still 16.86% lower than a year ago, 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 July of 2025.

Learn how oil prices impact the stock market through the oil prices stock market chart. Discover the correlation between oil prices and stock market movements and gain insights for informed investment decisions.

The oil price per barrel is a crucial indicator in the stock market, affecting various sectors and industries. This article explores the factors influencing oil prices, the impact on the stock market, and the importance of considering other factors when analyzing stock market trends.

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.

S&P GSCI Crude Oil Index: A Reliable Gauge of Global Oil Prices?

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

The article discusses the relationship between crude oil prices and the stock market, highlighting the impact of oil price fluctuations on various sectors of the economy. It explains how changes in oil prices can affect industries that heavily rely on oil and explores the factors influencing the crude oil market. The article also discusses the potential effects of oil price changes on stock prices and consumer spending. Additionally, it emphasizes the complexity of the relationship between crude oil prices

This statistic shows the stock price development of selected petroleum companies from January 2, 2020 to April 15, 2024. After the Russian invasion of Ukraine in February 2022, oil prices increased sharply in the first quarter of 2022 since many countries depend on Russian oil. Petroleum companies highly benefited from inclined oil prices, and saw significant increases in their share prices.

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.

Heating Oil rose to 2.47 USD/Gal on July 11, 2025, up 3.46% from the previous day. Over the past month, Heating Oil's price has risen 11.10%, but it is still 1.60% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Heating oil - values, historical data, forecasts and news - updated on July of 2025.

The Brent Crude Oil stock market symbol is BZ and it is a major benchmark for global oil prices. Learn more about its significance and how it is traded in financial markets.

Urals Oil fell to 64.96 USD/Bbl on July 10, 2025, down 3.36% from the previous day. Over the past month, Urals Oil's price has risen 2.51%, but it is still 18.27% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Urals Crude.

Dow Jones North America Select Junior Oil index is anticipated to experience moderate to high volatility in the near term. Potential factors influencing the index include global economic growth, geopolitical tensions, crude oil demand and supply dynamics, changes in interest rates, and market sentiment. Key risks associated with these predictions include geopolitical risks, economic downturn, supply chain disruptions, and unexpected changes in oil prices.

Graph and download economic data for CBOE Crude Oil ETF Volatility Index (OVXCLS) from 2007-05-10 to 2025-07-10 about ETF, VIX, volatility, crude, oil, stock market, and USA.

Understanding the relationship between the stock market and barrel oil price, and the factors that influence oil prices, can help investors make informed decisions. This article explores the impact of oil price changes on the stock market and the various factors that drive oil prices.

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.

Oil Prices on the Rise as OPEC+ Deepens Output Cuts

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

Palm Oil rose to 4,175 MYR/T on July 11, 2025, up 0.68% from the previous day. Over the past month, Palm Oil's price has risen 8.72%, and is up 6.67% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Palm Oil - values, historical data, forecasts and news - updated on July of 2025.

The file "fuels.txt" includes daily data for Brent futures (BrentF) and spot (BrentS) prices obtained from nasdaq.com database and three NASDAQ indices: 1) NASDAQ OMX Bio/Clean Fuels Index (GRNBIO). Source: {https://indexes.nasdaqomx.com/Index/Overview/GRNBIO} 2) NASDAQ OMX Fuel Cell Index (GRNFUEL). Source:{https://indexes.nasdaqomx.com/Index/Overview/GRNFUEL} 3) NASDAQ OMX Transportation Index (GRNTRN). Source: {https://indexes.nasdaqomx.com/Index/Overview/GRNTRN} The file "fundamentals.txt" includes monthly data for the following variables: 1) WIP: world industrial production index collected from:{https://sites.google.com/site/cjsbaumeister/datasets?authuser=0} 2) COMM: real commodity price factor - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 3) GECON: global economic condition indicator (standardised) - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 4) S.SH: oil supply shock - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 5) OCDSH: oil consumption demand - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 6) OIDSH: oil inventory demand- obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 7) EASH: oil demand shocks driven by global economic activity - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 8) GEPU: global economic policy uncertainty index - , a normalised index of the volume of news articles discussing economic policy uncertainty; due to the nonstationarity of the data, obtained from: {https://www.policyuncertainty.com/global_monthly.html} 9) EXPT: Brent spot prices expectations formulated by the U.S. Energy Information Association; 10) SPX - end-of-month data of S&P500 11) SPECUL1: Net position of Money Managers (long-short) for Brent contract - based on the ICE Futures Europe Commitments of Traders Reports ({www.ice.com/marketdata/reports/122}); 12) SPECUL2: Speculation measure analogous to Working's (1960) index, which measures the speculative activity of non-commercial traders in the crude oil market.

Sunflower Oil fell to 1,238 INR/10 kg on July 11, 2025, down 0.20% from the previous day. Over the past month, Sunflower Oil's price has fallen 0.13%, but it is still 35.94% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Sunflower Oil.

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.

Oil Prices Reach New Highs as Global Demand Grows

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