Crude Oil rose to 64.01 USD/Bbl on August 12, 2025, up 0.08% from the previous day. Over the past month, Crude Oil's price has fallen 4.43%, and is down 18.30% 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.

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.

Brent rose to 66.87 USD/Bbl on August 12, 2025, up 0.37% from the previous day. Over the past month, Brent's price has fallen 3.37%, and is down 17.12% 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 August 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.

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

Graph and download economic data for CBOE Crude Oil ETF Volatility Index (OVXCLS) from 2007-05-10 to 2025-08-08 about ETF, VIX, volatility, crude, stock market, oil, and USA.

Heating Oil fell to 2.28 USD/Gal on August 12, 2025, down 0.56% from the previous day. Over the past month, Heating Oil's price has fallen 4.67%, and is down 4.98% compared to the same time last year, 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 August of 2025.

Short-term predictions indicate a cautiously optimistic outlook for S&P GSCI Crude Oil, with potential for moderate upside driven by a combination of supply constraints and rising global energy demand. However, risks to this prediction include geopolitical uncertainties, increased interest rates, and the potential for new COVID-19 variants to disrupt economic recovery and demand.

This data is made up of daily stock prices and commodities' futures of a range of variables including NASDAQ clean focused price index, ARCA technology price index, Brent oil futures, Henry hub natural gas futures, Newcastle coal futures, carbon emission futures and green information technology stock price. The dataset supports empirical analysis which examines the volatility of clean energy stock returns (CERs) given the aggregate influence of energy security elements (ESEs) internal to CERs and the individuals influences of a range of exogenous variables including oil futures, natural gas futures, coal futures, carbon emission futures and green information technology stock price.

Palm Oil rose to 4,400 MYR/T on August 12, 2025, up 0.37% from the previous day. Over the past month, Palm Oil's price has risen 4.07%, and is up 19.27% 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 August 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.

Is the Heating Oil Index a Reliable Indicator of Market Trends?

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

Urals Oil fell to 62.56 USD/Bbl on August 8, 2025, down 0.97% from the previous day. Over the past month, Urals Oil's price has fallen 6.93%, and is down 16.53% compared to the same time last year, 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.

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 rose to 1,300.80 INR/10 kg on August 8, 2025, up 0.46% from the previous day. Over the past month, Sunflower Oil's price has risen 5.03%, and is up 45.54% compared to the same time last year, 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 study investigates the impact of oil market uncertainty on the volatility of Chinese sector indexes. We utilize commonly used realized volatility of WTI and Brent oil price along with the CBOE crude oil volatility index (OVX) to embody the oil market uncertainty. Based on the sample span from Mar 16, 2011 to Dec 31, 2019, this study utilizes vector autoregression (VAR) model to derive the impacts of the three different uncertainty indicators on Chinese stock volatilities. The empirical results show, for all sectors, the impact of OVX on sectors volatilities are more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices, especially after the Chinese refined oil pricing reform of March 27, 2013. That implies OVX is more informative than traditional WTI and Brent oil prices with respect to volatility spillover from oil market to Chinese stock market. This study could provide some important implications for the participants in Chinese stock market.

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

The oil stock market today shows a mixed trend with some companies experiencing gains while others are facing losses. Exxon Mobil reported a 5% increase in its stock price, Chevron saw a decline of 2%, BP experienced a modest increase of 1%, Royal Dutch Shell witnessed a 3% decline, and Halliburton reported a 2% increase. Fluctuations in oil prices, global demand, and geopolitical factors continue to be key drivers of stock market performance in the oil industry.

Graph and download economic data for Global price of Olive Oil (POLVOILUSDM) from Jan 1990 to Jun 2025 about oil, World, food, and price.

The S&P GSCI Crude Oil index is expected to remain volatile, influenced by geopolitical tensions, supply and demand dynamics, and central bank policies. The risk of further price fluctuations persists due to the ongoing Russia-Ukraine conflict, potential supply disruptions, and the impact of economic headwinds on global energy consumption. Market participants should monitor these factors closely and consider hedging strategies to mitigate risks associated with price movements in the index.

Maximum unit root module of each VAR models (GARCH volatilities).

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.