Cocoa fell to 6,334.21 USD/T on October 24, 2025, down 0.08% from the previous day. Over the past month, Cocoa's price has fallen 8.53%, and is down 6.69% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Cocoa - values, historical data, forecasts and news - updated on October of 2025.

Cocoa beans stock price is determined by factors such as supply and demand, weather conditions, political stability, and global economic conditions. This article explores how these factors influence the stock price of cocoa beans and discusses their significance for investors and traders in the commodities market.

Graph and download economic data for Global price of Cocoa (PCOCOUSDM) from Jan 1990 to Jun 2025 about cocoa, World, and price.

The closure of the HoReCa segment caused the demand for cocoa beans to collapse in 2020, while the production remained stable. This led to a surge in unsold cocoa bean stocks in exporter-countries. EU chocolate producers are now trying to enlist a range of measures to ensure stable demand and secure supply chains on the cocoa bean 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.

Cocoa Futures Show Bullish Trend, DJ Commodity Cocoa Index Forecasts Rise

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

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Cocoa Beans Market Size 2025-2029

The cocoa beans market size is valued to increase by USD 3.58 billion, at a CAGR of 4.5% from 2024 to 2029. Rising popularity of chocolate-flavored beverages will drive the cocoa beans market.

Major Market Trends & Insights

Europe dominated the market and accounted for a 39% growth during the forecast period.
By Application - Confectionery segment was valued at USD 5.14 billion in 2023
By Product - Cocoa butter segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 42.75 million
Market Future Opportunities: USD 3576.90 million
CAGR from 2024 to 2029 : 4.5%

Market Summary

Cocoa beans, the primary ingredient in chocolate, have long held a significant place in the global market due to their versatility and universal appeal. The demand for cocoa beans is driven by the growing popularity of chocolate-based products, particularly beverages, which have witnessed a notable increase in consumer preference. This trend is fueled by the expanding health-conscious population seeking alternatives to sugar-laden beverages. The market is characterized by a dynamic business landscape, with numerous players vying for market share. Mergers and acquisitions have become a common strategy for companies aiming to expand their operations and strengthen their market position.
Despite this competition, the market remains volatile due to various factors, including weather conditions in cocoa-producing regions and fluctuations in supply and demand. However, the market is not without challenges. Volatility in the prices of cocoa beans, influenced by weather conditions and supply chain disruptions, poses a significant risk for market participants. Cocoa bean prices have experienced significant volatility, reflecting the market's complexities. Factors such as disease outbreaks, weather conditions, and geopolitical instability can significantly impact prices. However, the long-term outlook for the market remains positive, with continued growth expected due to increasing consumer demand and ongoing innovation in chocolate-based products. In conclusion, the market is a dynamic and complex business environment, shaped by various factors including consumer preferences, competition, and external influences. Companies operating in this sector must stay informed about market trends and be prepared to adapt to changing conditions to remain competitive.

What will be the Size of the Cocoa Beans Market during the forecast period?

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How is the Cocoa Beans Market Segmented ?

The cocoa beans industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Confectionery
  Functional food and beverage
  Cosmetics
  Others


Product

  Cocoa butter
  Cocoa powder
  Cocoa beverages
  Others


Type

  Forastero
  Trinitario
  Criollo


Nature

  Organic
  Conventional


Sale Channel

  B2B
  B2C


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Application Insights

The confectionery segment is estimated to witness significant growth during the forecast period.

The market continues to evolve, driven by the increasing demand for low-fat alkalized and natural cocoa powders in various confectionery items, such as toffee, hard candy, and marshmallows. This trend is fueling the growth of the confectionery segment, with companies like Mars and Nestle focusing on producing chocolate liquor with higher cocoa mass for stronger flavors. For example, Prodigy Snacks Ltd, a UK confectionery firm, recently introduced Phenomenoms, a new chocolate biscuit range with two flavors, highlighting this market development. Additionally, ongoing activities include the implementation of sustainable farming practices, yield optimization techniques, and soil nutrient management to enhance production efficiency.

Bean drying methods, packaging technology, and traceability systems are also evolving to ensure product quality and consumer safety, with a focus on reducing aflatoxin contamination levels. Theobroma cacao cultivation continues to adapt to climate change, with research on pest management strategies, microbial community composition, and disease resistance mechanisms. The market is further characterized by improvements in processing efficiency, sensory evaluation protocols, and polyphenol oxidase activity analysis for assessing health benefits. Cocoa butter extraction and flavonoid conte

This statistic depicts the average annual prices for cocoa from 2014 through 2026*. In 2024, the average price for cocoa stood at 7.33 nominal U.S. dollars per kilogram.

View monthly updates and historical trends for Cocoa Price. Source: World Bank. Track economic data with YCharts analytics.

The Cocoa Beans Market Report is Segmented by Geography (North America, Europe, Asia-Pacific, and More). The Report Includes Production Analysis (Volume), Consumption Analysis (Value and Volume), Export Analysis (Value and Volume), Import Analysis (Value and Volume), and Price Trend Analysis. The Market Forecasts are Provided in Terms of Value (USD) and Volume (Metric Tons).

View monthly updates and historical trends for Cocoa Bean Price. Source: International Monetary Fund. Track economic data with YCharts analytics.

Lindt & Spruengli AG enjoys a 28% rise in shares by passing costs to consumers, while Barry Callebaut AG struggles with a 30% decline amid soaring cocoa prices.

This table contains 11 series, with data for years 1946 - 1994 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Production and stocks (11 items: Cereals ready to serve; Macaroni, et cetera, dry; Prepared cake mixes; Mixes, all other flour based; ...).

WORLD : COCOA SUPPLY AND DEMAND (SEASONAL BALANCE), 2017 - 2021 Unit : 000 Tonnes Note : e Estimate, r Revised, f Forecasts, 1 The net world crop is obtained by adjusting the gross world for 1% loss in weight, 2 Total Availability = Opening stock + net world crop, 3 Closing Stocks = Total Availability – Seasonal Grindings, * The cocoa season is from October of the current year to September of the following year

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.

Cocoa Index Forecast: Mixed Outlook

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

Common-Stock Time Series for Barry Callebaut AG. Barry Callebaut AG, together with its subsidiaries, engages in the manufacture and sale of chocolate and cocoa products. The company provides chocolates, compounds, chips and chunks, cocoa, cacao fruit, fillings, coatings, nuts, decorations and inclusions, and food colorants, as well as personalization sheets. It also offers cocoa powder, chocolate drinks, cappuccinos, ice cream and desserts, dairy and non-dairy products, tea, and coffee. The company provides products under the American Almond, Barry Callebaut, Cacao Barry, Callebaut, Caprimo, Carma, Chocovic, Classic, Mona Lisa, Gertrude Hawk Ingredients, Ogonblink, Satro Quality Drinks, Sicao Chocolate, Stewart & Arnold, Van Houten Professional, Van Houten, Van Leer Chocolates, Cabosse Naturals, D'Orsogna Dolciaria, Dings Décor, Le Royal, and La Morella Nuts names. In addition, it offers centralized treasury and management services, captive reinsurance, and research and development services, as well as conference and training services through CHOCOLATE ACADEMY centers. The company serves food manufacturers and artisans; and professional users of chocolate, including chocolatiers, pastry chefs, and bakers. It also offers products for vending machines. The company operates primarily in the Western Europe, North America, Central and Eastern Europe, Latin America, and internationally. Barry Callebaut AG was incorporated in 2016 and is headquartered in Zurich, Switzerland.

The study was conducted at 15 sites across Côte d’Ivoire, covering a South-North climate and vegetation gradient from evergreen to semi-deciduous forests. These sites were selected to represent varying cocoa field characteristics, including structural complexity (ranging from monocultures to agroforests), cocoa field age (young to mature), and cocoa tree productivity (low to high). Each site had 10 cocoa plots, ranging from 0.3 to 5 hectares. The carbon stocks of all trees, including remnant, spontaneous, and planted trees, have been estimated using the BIOMASS R package. This tool provides a robust and widely accepted methodology for calculating tree biomass and carbon stocks, ensuring accurate and reliable estimates for this dataset. Methods Study Sites Study plots have been selected from 15 locations throughout the cocoa production area (the forest zone) of Côte d’Ivoire (Fig. 1). These sites are situated along a South-North climate gradient, with mean annual temperatures ranging from 22.6°C to 26.2°C and annual rainfall varying between 1,900 mm and 1,100 mm. This climate gradient corresponds to a South-North vegetation gradient, transitioning from evergreen forests to semi-deciduous forests. To capture the diversity of cocoa fields, we identified 10 cocoa fields at each site, focusing on three key criteria: (i) structural complexity, ranging from nearly full-sun cocoa monocultures to complex cocoa agroforests; (ii) age of the cocoa fields, encompassing both young (< 20 years) and mature (> 20 years) plots; and (iii) cocoa tree yield, varying from low productivity (< 300 kg) to high productivity (> 300 kg). The study plots vary in size from 0.3 to 5 hectares and serve as the sampling units, corresponding to the management units of the farmers. Data Collection All cocoa fields were mapped using GPS technology for precise delineation. Each companion tree with a minimum diameter at breast height (dbh) of 10 cm was identified to the species level. The dbh and the height of each tree was recorded. The origin of each tree—classified as planted, remnant, or spontaneous— and the age of each planted and spontaneous tree was recorded based on the farmer's declaration. Four socio-environmental factors of interest were identified for this study:

Two social factors related to the cocoa farmer: botanical knowledge and land ownership status of the cocoa field, obtained through interviews with the farmers. Two field-related factors: cocoa tree density and previous land use. Cocoa tree density was estimated by counting the number of cocoa trees in a 1000 m² area within each cocoa field, and previous land use was obtained through interviews with the farmers.

The aboveground biomass of each individual tree was estimated and then converted to Carbon, using the computeAGB() function from the BIOMASS package in the R software. Carbon Stocks per plot and per cohort was calculated by adding the stock of each tree.

Cocoa Market set to hit USD 165.2 Billion by 2034, expanding at a CAGR of 7.5%, driven by rising demand in food, beverage, and cosmetic industries

The revenue of the cocoa market in Papua New Guinea amounted to $X in 2018, going up by X% against the previous year. Over the period under review, cocoa consumption continues to indicate a buoyant increase. The pace of growth appeared the most rapid in 2016 when the market value increased by X% year-to-year. Cocoa consumption peaked in 2018 and is expected to retain its growth in the near future.

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.

Cocoa Index Outlook Signals Potential Shifts

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 covers data on production, processing and trading activities in Ghana's cocoa value chain that were used to populate a stock-and flow diagram representing the baseline model of the cocoa value chain. Data are retrieved from different secondary sources, collated in excel sheets