Lean Hogs rose to 113.25 USd/Lbs on June 27, 2025, up 0.82% from the previous day. Over the past month, Lean Hogs's price has risen 12.94%, and is up 26.43% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lean Hogs - values, historical data, forecasts and news - updated on June 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.

TR/CC CRB Lean Hogs Index: Where is the Pork Market Headed?

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.

Lean Hogs: TR/CC CRB Points to Near-Term Price Stability for the Hog 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

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.

Lean Hogs: TR/CC CRB Forecasts Continued Rise for the Livestock 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

Mexico Consumer Price Index (CPI): Food: Meat: PMO: Lean Pork data was reported at 156.833 Jun2002=100 in Dec 2010. This records a decrease from the previous number of 157.766 Jun2002=100 for Nov 2010. Mexico Consumer Price Index (CPI): Food: Meat: PMO: Lean Pork data is updated monthly, averaging 42.021 Jun2002=100 from Jan 1980 (Median) to Dec 2010, with 372 observations. The data reached an all-time high of 157.766 Jun2002=100 in Nov 2010 and a record low of 0.166 Jun2002=100 in Jan 1980. Mexico Consumer Price Index (CPI): Food: Meat: PMO: Lean Pork data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.I005: Consumer Price Index: 2002=100.

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.

Lean Hogs Forecast: TR/CC CRB Index Shows Bullish Trend

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

China Agricultural Product Price Index: Producer: Lean Type White Pork Carcass data was reported at 17.770 RMB/kg in 26 Jul 2018. This records a decrease from the previous number of 17.790 RMB/kg for 25 Jul 2018. China Agricultural Product Price Index: Producer: Lean Type White Pork Carcass data is updated daily, averaging 18.490 RMB/kg from Mar 2017 (Median) to 26 Jul 2018, with 327 observations. The data reached an all-time high of 20.630 RMB/kg in 08 Jan 2018 and a record low of 13.560 RMB/kg in 14 May 2018. China Agricultural Product Price Index: Producer: Lean Type White Pork Carcass data remains active status in CEIC and is reported by Ministry of Agriculture and Rural Affairs. The data is categorized under China Premium Database’s Price – Table CN.PA: Ministry of Agriculture and Rural Affairs: Producer Price Index: Lean Type White Pork Carcass: Daily. The index is the weighted price index of 60 sample companies in 16 price-based regions. The PPI of lean type white pork carcass is compiled by the Ministry of Agriculture for the market research of pork price movement and the government price regulation needs. Data released is the actual price. 总指数是16个采价区域内60家样本企业的加权价格指数。 瘦肉型白条猪肉出厂价格指数，为农业部给市场研究猪肉价格及协助政府部门进行价格调控而编制的。 数据发布为实际价格。

Agricultural Product Price Index: Producer: Lean Type White Pork Carcass: Southwest China data was reported at 17.280 RMB/kg in 26 Jul 2018. This stayed constant from the previous number of 17.280 RMB/kg for 25 Jul 2018. Agricultural Product Price Index: Producer: Lean Type White Pork Carcass: Southwest China data is updated daily, averaging 19.150 RMB/kg from Mar 2017 (Median) to 26 Jul 2018, with 327 observations. The data reached an all-time high of 21.150 RMB/kg in 05 Apr 2017 and a record low of 14.180 RMB/kg in 21 May 2018. Agricultural Product Price Index: Producer: Lean Type White Pork Carcass: Southwest China data remains active status in CEIC and is reported by Ministry of Agriculture and Rural Affairs. The data is categorized under China Premium Database’s Price – Table CN.PA: Ministry of Agriculture and Rural Affairs: Producer Price Index: Lean Type White Pork Carcass: Daily.

RP_Fat: weight of the retroperitoneal fat corrected for length of the pig; BMI: Body Mass Index calculated as weight (kg)/(length (m))2; BAI: Body Adiposity Index calculated as abdominal circumference divided by length1.5; ABC: the abdominal circumference; TG: triglycerides; Ct: total cholesterol; Fasting_glu is the glucose level measured after 24H of fasting. ASAT: aspartate amino transferase (U/L). ALAT: alanine amino transferase (U/L). ASAT/ALAT: ratio of ASAT and ALAT. All values are displayed as mean ± SD.Mean values of the phenotypic measures of the lean and obese pigs calculated on all pigs, and the male and female pigs respectively.

贸易指数:同比:价格:进口:HS4:未炼制或用其他方法提取的不带瘦肉的肥猪肉、猪脂肪及家禽脂肪，鲜、冷、冻、干、熏、盐腌或盐渍的在03-01-2025达103.200上年同月=100，相较于02-01-2025的102.300上年同月=100有所增长。贸易指数:同比:价格:进口:HS4:未炼制或用其他方法提取的不带瘦肉的肥猪肉、猪脂肪及家禽脂肪，鲜、冷、冻、干、熏、盐腌或盐渍的数据按月更新，01-01-2018至03-01-2025期间平均值为96.742上年同月=100，共71份观测结果。该数据的历史最高值出现于06-01-2019，达226.505上年同月=100，而历史最低值则出现于02-01-2022，为50.600上年同月=100。CEIC提供的贸易指数:同比:价格:进口:HS4:未炼制或用其他方法提取的不带瘦肉的肥猪肉、猪脂肪及家禽脂肪，鲜、冷、冻、干、熏、盐腌或盐渍的数据处于定期更新的状态，数据来源于海关总署，数据归类于中国经济数据库的国际贸易 – Table CN.JE: Unit Value Index: YoY: HS4 Classification。