GSCI rose to 548.35 Index Points on September 12, 2025, up 0.46% from the previous day. Over the past month, GSCI's price has risen 2.83%, and is up 5.64% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. GSCI Commodity Index - values, historical data, forecasts and news - updated on September of 2025.

Graph and download economic data for Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Fiber Cans, Tubes, and Similar Fiber Products (WPU091507) from Dec 1963 to Aug 2025 about fiber, composite, paper, commodities, PPI, inflation, price index, indexes, price, and USA.

CRB Index rose to 373.85 Index Points on September 12, 2025, up 0.48% from the previous day. Over the past month, CRB Index's price has risen 2.69%, and is up 15.08% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on September of 2025.

United States - Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Fiber Cans, All Fiber and Composite was 199.75900 Index Dec 2009=100 in June of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Fiber Cans, All Fiber and Composite reached a record high of 199.75900 in June of 2025 and a record low of 100.00000 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Fiber Cans, All Fiber and Composite - last updated from the United States Federal Reserve 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.

Trading Signals (NASDAQ Composite Index 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

Korea LDCI: MoM: Commodity Price Index data was reported at -3.100 % in Dec 2012. This records a decrease from the previous number of -2.200 % for Nov 2012. Korea LDCI: MoM: Commodity Price Index data is updated monthly, averaging 0.800 % from Feb 1994 (Median) to Dec 2012, with 227 observations. The data reached an all-time high of 17.300 % in Jan 1998 and a record low of -9.200 % in Dec 2008. Korea LDCI: MoM: Commodity Price Index data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.S003: Composite Economic Index: 2005=100.

Graph and download economic data for Producer Price Index by Commodity: Chemicals and Allied Products: Titanium Dioxide, Composite and Pure (WPU0622020N1) from Jan 1947 to Feb 2019 about titanium, composite, chemicals, production, commodities, PPI, price index, indexes, price, and USA.

Russia Composite Price Index: Ferrous Metals data was reported at 576.290 09Feb2001=100 in 15 May 2020. This stayed constant from the previous number of 576.290 09Feb2001=100 for 14 May 2020. Russia Composite Price Index: Ferrous Metals data is updated daily, averaging 376.830 09Feb2001=100 from May 2005 (Median) to 15 May 2020, with 4595 observations. The data reached an all-time high of 623.690 09Feb2001=100 in 25 Jul 2019 and a record low of 226.460 09Feb2001=100 in 31 Mar 2006. Russia Composite Price Index: Ferrous Metals data remains active status in CEIC and is reported by Metal.Com.Ru Trade System. The data is categorized under Daily Database’s Commodity Prices and Futures – Table PG003: Metals Trading Price.

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.

NASDAQ Composite Index NASDAQ Composite Index

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

Lumber rose to 586.50 USD/1000 board feet on September 12, 2025, up 2.62% from the previous day. Over the past month, Lumber's price has fallen 3.30%, but it is still 17.40% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lumber - values, historical data, forecasts and news - updated on September of 2025.

United States - Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Waferboard and Oriented Strandboard (OSB) was 166.20000 Index Dec 1982=100 in January of 2019, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Waferboard and Oriented Strandboard (OSB) reached a record high of 394.90000 in April of 2004 and a record low of 90.70000 in November of 1991. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Waferboard and Oriented Strandboard (OSB) - last updated from the United States Federal Reserve on September 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.

IDX Composite Index: The Definitive Measure of Market Health?

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

Japan Index: NSE: Stock Price Index: 2nd Section Composite data was reported at 3,638.890 04Jan1968=100 in Oct 2018. This records an increase from the previous number of 3,634.600 04Jan1968=100 for Sep 2018. Japan Index: NSE: Stock Price Index: 2nd Section Composite data is updated monthly, averaging 1,350.530 04Jan1968=100 from Feb 1999 (Median) to Oct 2018, with 237 observations. The data reached an all-time high of 3,655.090 04Jan1968=100 in Jul 2018 and a record low of 871.670 04Jan1968=100 in Nov 2002. Japan Index: NSE: Stock Price Index: 2nd Section Composite data remains active status in CEIC and is reported by Nagoya Stock Exchange. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

Japan Index: NSE: Stock Price Index: 1st Section Composite data was reported at 1,296.280 04Jan1968=100 in Oct 2018. This records a decrease from the previous number of 1,416.020 04Jan1968=100 for Sep 2018. Japan Index: NSE: Stock Price Index: 1st Section Composite data is updated monthly, averaging 1,115.530 04Jan1968=100 from Feb 1999 (Median) to Oct 2018, with 237 observations. The data reached an all-time high of 1,842.610 04Jan1968=100 in Jun 2007 and a record low of 672.580 04Jan1968=100 in May 2012. Japan Index: NSE: Stock Price Index: 1st Section Composite data remains active status in CEIC and is reported by Nagoya Stock Exchange. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

China's main stock market index, the SHANGHAI, fell to 3871 points on September 12, 2025, losing 0.12% from the previous session. Over the past month, the index has climbed 5.08% and is up 43.14% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on September of 2025.

Steel rose to 3,046 CNY/T on September 12, 2025, up 0.79% from the previous day. Over the past month, Steel's price has fallen 5.29%, but it is still 0.30% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Steel - values, historical data, forecasts and news - updated on September of 2025.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Commodities Less Food and Energy Commodities in U.S. City Average (CUSR0000SACL1E) from Jan 1957 to Aug 2025 about core, urban, consumer, CPI, commodities, inflation, price index, indexes, price, and USA.

Current price of Pork Belly Composite. Daily U.S. Pork Cuts prices per pound, based on negotiated prices and volume of boxed pork cuts delivered within 0-14 days and on average industry cutting yields.

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.

IDX Composite: A Leading Indicator of Indonesian Economic Recovery?

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

Containerized Freight Index fell to 1,398.11 Points on September 12, 2025, down 3.21% from the previous day. Over the past month, Containerized Freight Index's price has fallen 6.15%, and is down 44.32% 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 Containerized Freight Index.