Crude Oil rose to 63.21 USD/Bbl on August 18, 2025, up 0.66% from the previous day. Over the past month, Crude Oil's price has fallen 4.15%, and is down 14.18% 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.

Crude oil stock price forecast is a prediction of the future movement and value of stock prices in the crude oil industry. This article explains the methods used in forecasting, such as time series analysis, regression analysis, and machine learning, and emphasizes the importance of considering multiple forecast models and consulting with financial experts for more accurate predictions.

Crude oil stock forecast is the prediction of future movements in the stock market related to crude oil companies. Learn about the methods used for forecasting and the challenges faced in this process. Discover how this forecast plays a vital role in investor decision-making and the importance of combining various analytical approaches.

API Crude Oil Stock Change in the United States decreased to -2.40 BBL/1Million in August 15 from 1.50 BBL/1Million in the previous week. This dataset provides - United States API Crude Oil Stock Change- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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 Stock Forecast: A Speculative Trend For The Next 16 Weeks

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

MetaData

Crude Oil Stock Price Data (2000-2024)

Dataset Description

This dataset contains historical stock price data for Crude Oil from 2000 to 2024. This data is extracted by using Python's yfinance library and it provides detailed insights into Crude Oil's stock performance over the years. It includes daily values for the stock's opening and closing prices, adjusted close price, high and low prices, and trading volume. This dataset is ideal for time series analysis, stock trend analysis, and financial machine learning projects such as price prediction models and volatility analysis.

The dataset is extracted from Yahoo Finance

Column Descriptions

Date: The trading date for each entry, in the format.

Adj_Close: Adjusted closing price of Crude Oil stock for each trading day, reflecting stock splits, dividends, and other adjustments.

Close: The raw closing price of Crude Oil stock at the end of each trading day.

High: The highest price reached by Crude Oil stock during the trading day.

Low: The lowest price reached by Crude Oil stock during the trading day.

Open: The price of Crude Oil stock at the start of the trading day.

Volume: The total number of shares traded during the trading day.

Brent fell to 66.16 USD/Bbl on August 15, 2025, down 1.02% from the previous day. Over the past month, Brent's price has fallen 3.44%, and is down 16.97% 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 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 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

Find out the latest prices of oil stocks, including Brent Crude Oil, WTI Crude Oil, ExxonMobil Corporation (XOM), Chevron Corporation (CVX), and British Petroleum (BP). Learn about the factors influencing oil stock prices such as global oil demand, OPEC production decisions, geopolitical events, and economic indicators. Understand the risks involved in investing in oil stocks and the importance of staying updated on market trends.

Forecast: Soybean Oil Stock Variation in Canada 2023 - 2027 Discover more data with ReportLinker!

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.

Buy, sell or hold: NSE OIL Stock Forecast

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

Forecast: Olive Oil Stock Variation in China 2022 - 2026 Discover more data with ReportLinker!

Stocks of crude oil in the United States increased by 3.04million barrels in the week ending August 8 of 2025. This dataset provides the latest reported value for - United States Crude Oil Stocks Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

Crude Oil Price Forecasting

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.

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

Forecast: Crude Oil Stock Change in the UK 2024 - 2028 Discover more data with ReportLinker!

AI-powered price forecasts for OIL stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

This DataSet contains the real time Crude Oil Prices in USD from 2012 to 2022. In this Dataset Date - Date on which Price is Noted Close - Close Price of the oil Volume - Sum of buy's and sell's of oil commodity open - open price of a oil on that particular day High - High price of oil on that particular day Low - Low price of oil on that particular day

Brent crude oil is projected to have an average annual spot price of 65.85 U.S. dollars per barrel in 2025, according to a forecast from May 2025. This would mean a decrease of nearly 15 U.S. dollars compared to the previous year, and also reflects a reduced forecast WTI crude oil price. Lower economic activity, an increase in OPEC+ production output, and uncertainty over trade tariffs all impacted price forecasting. All about Brent Also known as Brent Blend, London Brent, and Brent petroleum, Brent Crude is a crude oil benchmark named after the exploration site in the North Sea's Brent oilfield. It is a sweet light crude oil but slightly heavier than West Texas Intermediate. In this context, sweet refers to a low sulfur content and light refers to a relatively low density when compared to other crude oil benchmarks. Price development in the 2020s Oil prices are volatile, impacted by consumer demand and discoveries of new oilfields, new extraction methods such as fracking, and production caps routinely placed by OPEC on its member states. The price for Brent crude oil stood at an average of just 42 U.S. dollars in 2020, when the coronavirus pandemic resulted in a sudden demand drop. Two years later, sanctions on Russian energy imports, had pushed up prices to a new decade-high, above 100 U.S. dollars per barrel.