Sugar rose to 16.69 USd/Lbs on June 9, 2025, up 1.11% from the previous day. Over the past month, Sugar's price has fallen 5.46%, and is down 10.29% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Sugar - values, historical data, forecasts and news - updated on June of 2025.

Sugar Price Index in World decreased to 109.40 Index Points in May from 112.30 Index Points in April of 2025. This dataset includes a chart with historical data for World Sugar Price Index.

Global sugar manufacturers have endured fluctuations in global sugar prices over the five years to 2024. Nonetheless, global sugar manufacturers' revenue is anticipated to strengthen at a CAGR of 5.6% to $83.2 billion over the five years to 2024, including a drop of 8.5% in 2024. Brazil is very influential in the industry's health. The country produces and exports the most sugar of any nation and is also the second-largest producer of ethanol, which is often produced from sugarcane. As energy prices have strengthened over the past five years, Brazil has expansively diverted more of its sugar stock toward ethanol production. Brazil's changing production and export levels have impacted the world supply of sugar, which, in turn, has disturbed world sugar prices. For example, prior to the current period, in 2011, when Brazil cut its production of sugar by 2.0 million tons, the world price of sugar shot up 25.6%; the following year, as Brazil boosted production by more than 2.0 million tons, the world price of sugar dropped 18.5%. These fluctuations in production, coupled with other countries following Brazil's lead and diverting their sugar stock toward ethanol production or other more valuable crops, have led revenue for the entire industry to endure intense volatility during the current five-year period. Profit, measured as earnings before interest and taxes, inched upward to 6.1% of revenue in 2024. These factors are expected to continue driving volatility in the world price of sugar and global sugar manufacturers' revenue over the five years to 2029. Despite ongoing fluctuations, the world price of sugar will moderately drop as global demand for sugar and sugar-heavy products dips, along with lower energy prices, which will likely prompt demand for alternative fuel sources, like ethanol. Also, as demand from developing nations continues to swell and as trade barriers are expansively removed, global production and international trade of sugar will strengthen. As a result of these factors, global sugar manufacturers' revenue will drop at a CAGR of an estimated 1.2% over the next five years to $78.5 billion in 2029.

In the final quarter of 2024, sugar markets in North America saw varied trends, shaped by seasonal demand, weather conditions, and production costs. In December, U.S. sugar prices stabilized after earlier increases, supported by moderate retail demand and sufficient inventories. The USDA revised the U.S. sugar ending stocks-to-use ratio upward to 13.5%, signaling a stable supply outlook. While domestic beet sugar production was lower, strong imports from Mexico and Brazil helped balance the 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.

Is the Sugar Index a Sweet Spot for Investors?

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.

Sugar Futures Signal Potential Price Volatility for CRB Commodities 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

Dangote Sugar Refinery PLC reported NGN151.51B in Stock for its fiscal quarter ending in September of 2024. Data for Dangote Sugar Refinery PLC | DANGSUGA - Stock including historical, tables and charts were last updated by Trading Economics this last June in 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 Sugar Index a Reliable Indicator of TR/CC CRB Performance?

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 Intermediate Inputs Share for Manufacturing: Sugar and Confectionery Product Manufacturing (NAICS 3113) in the United States (IPUEN3113P031000000) from 1988 to 2021 about confectionery, sugar, shares, cost, intermediate, purchase, NAICS, IP, production, manufacturing, and USA.

Graph and download economic data for Capital Share for Manufacturing: Sugar and Confectionery Product Manufacturing (NAICS 3113) in the United States (IPUEN3113C031000000) from 1988 to 2021 about confectionery, sugar, shares, cost, NAICS, capital, IP, production, manufacturing, and USA.

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.

Will the Sugar Index Sweeten Your Portfolio?

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

Abstract copyright UK Data Service and data collection copyright owner.

The objective of the project was to provide econometric analysis and theory for modelling energy and soft commodity prices. This necessitated data analysis and modelling together with theoretical econometrics, dealing with the specific stylised facts of commodity prices. In order to analyse energy and soft commodity prices, the determination of spot energy prices in regulated markets was first considered, from the point of view of the regulator. Direct data analysis of futures commodity prices was then undertaken, resulting in the collection of an extensive dataset of most traded futures commodity prices at a daily frequency, covering 16 different commodities over a 10-year period.

Main Topics:

The commodities covered include electricity, oil, gas and other commodities, including coffee, cocoa, sugar, cereal and pulse crops. Other energy utility company details are also included in the data.

Graph and download economic data for Labor Share for Manufacturing: Sugar and Confectionery Product Manufacturing (NAICS 3113) in the United States (IPUEN3113L030000000) from 1987 to 2021 about confectionery, sugar, shares, cost, NAICS, IP, production, labor, manufacturing, and USA.

Dangote Sugar Refinery PLC reported NGN204.67B in Cost of Sales for its fiscal quarter ending in March of 2025. Data for Dangote Sugar Refinery PLC | DANGSUGA - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last June in 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.

DJ Commodity Sugar Index: Analysts Predict Further Volatility.

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.

DJ Commodity Sugar index projects moderate gains.

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.

TR/CC CRB Sugar index forecast: Upward trend anticipated.

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

Raizen stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Overview
This study uses a case study approach to demonstrate the potential to estimate farm shares and price spreads in Australia using a relatively simple methodology developed by the United States Department of Agriculture Economic Research Service. In this instance, the methodology has been applied to Australian sugar price data.

Key Points
• The study demonstrates that data is available that allows an analysis of farm share and price spread for raw sugar exports and refined sugar sold at retail outlets. • The analysis shows that trends in farm shares of retail and export prices were relatively flat between 1984-85 and 2014-15. So too were trends in farm-to-retail and farm-to-export price spreads. • If it is assumed that the emergence of market power beyond the farm gate is likely to be reflected in changes in trends in farm share and price spread, then these results suggest that there has been no obvious change in market power within the sugar industry over this period.

The sugar production industry has an oligopolistic market structure. The combined market shares of the three players Südzucker, Nordzucker and Pfeifer & Langen cover almost the entire German sugar market. However, sugar-producing companies have faced major challenges in recent years. For example, the abolition of the EU sugar quota in October 2017 resulted in an overproduction of sugar and an associated fall in the price of sugar, which put the industry players under high pressure on margins. Between 2019 and 2024, revenue from sugar production increased by an average of 5.5% per year. Since 2021, the industry has recorded strong revenue growth as players have been able to pass on rising prices for input factors such as sugar beet and energy to their customers for the most part. IBISWorld expects turnover to grow by 2.2% to €4 billion in 2024.In recent years, the increase in health awareness has at times led to consumers reducing their sugar consumption or replacing sugar with alternative sweeteners such as honey or agave syrup. Food and beverage manufacturers have also responded to the health trend and are increasingly developing sugar-reduced and sugar-free products. At the same time, however, global sugar consumption is growing continuously, which is primarily due to the increasing consumption of sugar in emerging countries. As a result, exports are becoming more important for sugar manufacturers and downstream industries. The exception is 2020, which was characterised by a decline in exports by German confectionery manufacturers due to the coronavirus crisis, which had a slightly negative impact on sugar sales. In 2021, the industry was able to benefit from the increased global market price for sugar and offset some of the decline in sales from previous years.By 2029, IBISWorld expects industry turnover to fall by an average of 1.5% per year to €3.7 billion. Although the number of industry players is unlikely to change in the coming years, the number of sugar factories is expected to fall slightly due to ongoing competitive pressure.