Brazil's main stock market index, the IBOVESPA, rose to 159976 points on December 2, 2025, gaining 0.86% from the previous session. Over the past month, the index has climbed 6.33% and is up 26.83% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Brazil. Brazil Stock Market (BOVESPA) - values, historical data, forecasts and news - updated on December of 2025.

Actual value and historical data chart for Brazil Stock Market Return Percent Year On Year

This dataset provides a comprehensive overview of the Brazilian stock market, covering various sectors and industries from 2018 to 2023. Each row in the dataset represents a specific asset, identified by its ticker on B3 (the Brazilian stock exchange). The dataset includes detailed information such as sector, industry, trading dates, opening price, closing price, trading volume, dividends, dividend yield, and P/E (price/earnings) ratio. Additionally, it contains daily historical price data for the assets over time.

Key Columns:

•  Ticker: The asset’s code on the stock exchange.
•  Sector: The sector in which the asset operates.
•  Industry: The specific industry within the sector.
•  Date: The reference date for the data.
•  Open: The asset’s opening price on the specified date.
•  Price: The asset’s closing price on the specified date.
•  Volume: The trading volume of the asset.
•  Dividend: The amount of dividends paid.
•  Dividend Yield: The dividend yield as a percentage of the price.
•  P/E Ratio: The price-to-earnings ratio.

Applications:

This dataset is ideal for stock performance analysis, sector and industry studies, investment strategy development, and machine learning models focused on market predictions. It can be used by investors, market analysts, researchers, and finance professionals interested in examining the behavior of the Brazilian stock market over five years.

Key information about Brazil Month End

• Brazil Month End closed at 149,540.4 points in Oct 2025, compared with 146,237.0 points at the previous month end
• Brazil Index: B3: IBOV: Month End data is updated monthly, available from Jan 1983 to Oct 2025, with an average number of 19,544.7 points
• The data reached an all-time high of 149,540.4 points in Oct 2025 and a record low of 0.0 points in Jan 1983

Brazil's B3 Stock Exchange is the one and only public financial market of the 13th largest economy in the world with a 2021 market cap of R$ 5.2 trillion BRL.

With the goal of improving financial literacy, this dataset contains more than 400 stock listings that are updated daily for the purposes of providing data for quantitative analysis.

How to use this dataset

• Create a time series regression model to predict ^IBOV value and/or stock prices.
• Explore the yield and volatility of the stocks listed.
• Identify high and low performance stocks among the list.

Highlighted Notebooks

• Your kernel can be featured here!
• More datasets

Acknowledgements

License

CC0: Public Domain

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Abstract Carvalho, Kayo and Martin (2010) point out that the above-average performance and persistent of an organization can be explained by its ability to properly exploit the resources and skills seen as rare and valuable. This research investigated if persistence of superior performance by Brazilian companies, by sector, can be attributed to tangibility, intangibility, corporate governance levels and the degree of company social responsibility. The sample consisted of Brazilian companies listed on the BM&F Bovespa Brazilian stock exchange and information was collected from the Bloomberg consulting database. We used a dynamic panel approach using the System Generalized of Moments Method (GMM-SYS) from Arellano and Bover (1995) and Blundell and Bond (1998). The results indicate significant evidence that intangibility imposed reductions to the persistence of company performance in most sectors. Tangibility and corporate governance levels have heterogeneous effects on persistent superior performance. Social responsibility levels positively and significantly impact the persistence of superior performance of public companies in Brazil.

ABSTRACT This article aimed to verify the influence of cash liquidity and financial constraints on the market performance of Brazilian companies. According to pecking order theory, organizations choose retained earnings over debts or new share issuances, which may be linked to specific costs. However, trade-off theory highlights that taxes mean that financial policy and debt can be relevant to company value. Thus, this study aims to provide new insights on these topics and knowledge for investigating cash liquidity and financial constraints in relation to performance. The cash liquidity of organizations and financial constraints are important phenomena for performance, given that, in this study, there was an increase in performance. The results suggest that organizations choose to underinvest in the setting due to the difficulty of obtaining credit. Thus, there is an evident need for organizations to present liquidity so as not to lose investment opportunities. Despite the financial constraints, the organizations represent, to some extent, a good investment option, as they prefer excess cash to resources for new investments. The population of this study is formed of companies listed on the B3 S.A. -Brasil, Bolsa, Balcão. The analysis period corresponded to the years from 2014 to 2018 and the KZ index is calculated to classify the organizations regarding their level of constraint. Next, multiple linear regression was run, controlling for year and sector fixed effects. There is a need for organizations to present liquidity to attract new investors. However, companies that find themselves financially constrained can also represent a good investment option as they choose excess cash. In the market, there are some resistances regarding financially constrained organizations, but there may be considerable liquidity in them.

Description

The "B3 Stock Market Dataset: Brazilian Stock Exchange Data" is a comprehensive collection of stock trading data from the B3 (Bolsa de Valores, Mercadorias e Futuros de São Paulo) exchange in Brazil. This dataset provides a detailed record of trading activities for various assets, offering insights into stock market trends and behaviors.

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The Python algorithm provided is essential for transforming the raw data into an organized DataFrame for analysis. Please make sure to follow the provided algorithm instructions to process and analyze the B3 stock market data effectively.

The algorithm is at the end of the description

Features

• TIPREG: Type of record.
• DATPRE: Trading date.
• CODNEG: Asset's trading code.
• NOMRES: Abbreviated name of the issuing company.
• ESPECI: Specification of the type of share or asset.
• PREABE: Opening price.
• PREMAX: Highest price during the trading session.
• PREMIN: Lowest price during the trading session.
• PREMED: Average price during the trading session.
• PREULT: Closing price.
• PREOFC: Best bid price.
• PREOFV: Best ask price.
• QUATOT: Total quantity of traded securities.
• VOLTOT: Total trading volume in currency units.
• CODISI: ISIN code of the asset.
• DISMES: Number of working days in the month.

Usage of Algorithm

The provided algorithm is mandatory for data processing and analysis in tabular format. It ensures that the data is properly organized and processed according to the specified attributes. ``` def tratamento(caminho_arquivo): # File Path ( TXT ) # Lista para armazenar os dados organizados ordem = []

# Leitura do arquivo de dados brutos
with open(caminho_arquivo, 'r') as arquivo:
  linhas = arquivo.readlines()

# Configuração da barra de carregamento
with tqdm(total=len(linhas), desc='Processando', bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt}') as pbar:
  # Iteração pelas linhas do arquivo
  for linha in linhas:

    if linhas == '':
      continue

    # Extrai as informações de cada campo
    tipreg = int(linha[0:2])
    datpre = pd.to_datetime(linha[2:10], format='%Y%m%d', errors='coerce')
    if pd.isnull(datpre):
      continue
    codneg = linha[12:24].strip()
    nomres = linha[27:39].strip()
    especi = linha[39:49].strip()
    preabe = float(linha[56:69]) / 100
    premax = float(linha[69:82]) / 100
    premin = float(linha[82:95]) / 100
    premed = float(linha[95:108]) / 100
    preult = float(linha[108:121]) / 100
    preofc = float(linha[121:134]) / 100
    preofv = float(linha[134:147]) / 100
    quatot = int(linha[152:170])
    voltot = float(linha[170:188]) / 100
    codisi = linha[245:257].strip()
    dismes = int(linha[257:260]) if linha[257:260].strip() else 0

    # Adiciona os dados à lista
    ordem.append([tipreg, datpre, codneg, nomres, especi, preabe, premax, premin,
                 premed, preult, preofc, preofv, quatot, voltot, codisi, dismes])

    # Atualiza a barra de carregamento
    pbar.update(1)

# Criação do DataFrame com os dados organizados
df = pd.DataFrame(ordem, columns=['TIPREG', 'DATPRE', 'CODNEG', 'NOMRES', 'ESPECI',
                        'PREABE', 'PREMAX', 'PREMIN', 'PREMED', 'PREULT',
                        'PREOFC', 'PREOFV', 'QUATOT', 'VOLTOT', 'CODISI', 'DISMES'])

df_dict = {
  'TIPREG': 'Tipo de registro',
  'DATPRE': 'Data de pregão',
  'CODNEG': 'Código de negociação do ativo',
  'NOMRES': 'Nome resumido da empresa emissora do ativo',
  'ESPECI': 'Especificação do tipo de ação ou ativo',
  'PREABE': 'Preço de abertura',
  'PREMAX': 'Preço máximo',
  'PREMIN': 'Preço mínimo',
  'PREMED': 'Preço médio',
  'PREULT': 'Preço de fechamento',
  'PREOFC': 'Preço da melhor oferta de compra',
  'PREOFV': 'Preço da melhor oferta de venda',
  'QUATOT': 'Quantidade total de títulos negociados',
  'VOLTOT': 'Volume total de títulos negociados',
  'CODISI': 'Código ISIN do ativo',
  'DISMES': 'Número de dias úteis do mês'
}

df.rename(columns=df_dict)

# Exibe o DataFrame
return df

Between 2015 and 2025, the S&P 500 recorded a ** percent annualized return, the highest among major stock markets worldwide. Second in the ranking were Bovespa and BSE Sensex, the main indices for the Brazilian and for the Indian stock markets, with ** percent annualized returns.

ABSTRACT The study sought to apply the model developed by Gokhale et al. (2015) to identify the existence of overreaction and behavioral biases in the Brazilian stock market and analyze its performance as an investment strategy on the São Paulo Stock, Commodities, and Futures Exchange (BM&FBOVESPA) in the short term and long term, as well as test its robustness with time window simulations. The impacts of behavioral finance on capital markets can affect economic decisions, perpetuate or increase asset pricing anomalies, and in more extreme and persistent situations contribute to the formation of bubbles that can compromise the entire financial system of a country. The study pioneers an innovative methodology in the Brazilian stock market for identifying behavioral biases and obtaining abnormal returns and higher returns than the Ibovespa. The research uses the model developed by Gokhale, Tremblay, and Tremblay (2015) in three samples with quotations data for Brazilian publicly-traded companies that compose the Ibovespa and IBrA in the period from 2005 to 2016. With the R statistical software, the Fundamental Valuation Index (FVI) was calculated for each sample share and each year. From the FVI index, the undervalued shares were identified, indicating that the sales price does not reflect their economic fundamentals, and portfolio simulations were carried out for investment over three months or the next year. The results indicate the possible existence of overreaction and behavioral biases in the Brazilian stock market, which lead to the possibility of higher abnormal returns than those of the Ibovespa. Similar to the US market, at the end of the 2006-2016 period simulated portfolios yielded more than 274%, while the Ibovespa yielded approximately 80%. The robustness tests attest to the effectiveness of the model. The various investment portfolios, simulated over different time horizons, yielded more than the Ibovespa on average. The study also confirmed the assumptions of Gokhale, Tremblay, and Tremblay (2015) regarding the model's inadequacy for short-term strategies.

Techsalerator's Corporate Actions Dataset in Brazil offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 475 companies traded on the B3 S.A. - Securities, Commodities and Futures Exchange (BVMF).

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Top 5 used data fields in the Corporate Actions Dataset for Brazil:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

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Top 5 corporate actions in Brazil:

Mergers and Acquisitions (M&A): Brazil's large and diverse economy often witnesses M&A activities across various industries, including banking, consumer goods, energy, and technology. Notable transactions include mergers, acquisitions, and strategic partnerships that reshape industry landscapes and enhance market competitiveness.

Initial Public Offerings (IPOs): IPOs of Brazilian companies on domestic and international stock exchanges are significant corporate actions. IPOs offer companies access to capital markets and enable investors to participate in the growth potential of newly listed companies.

Infrastructure Projects: Brazil frequently undertakes infrastructure projects, such as transportation, energy, and telecommunications. Corporate actions in this sector involve public-private partnerships, concessions, and investments to enhance national infrastructure.

Energy Sector Investments: Given Brazil's reliance on renewable energy sources like hydropower, wind, and solar, corporate actions in the energy sector include investments in energy generation and distribution, renewable energy projects, and regulatory changes affecting the industry.

Financial Sector Reforms: Reforms and regulatory changes within Brazil's financial sector can lead to significant corporate actions. These may include changes in banking regulations, fintech innovation, and adjustments to monetary and fiscal policies.

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Top 5 financial instruments with corporate action Data in Brazil

B3 Stock Exchange Domestic Index (IBOVESPA): The main index that tracks the performance of domestic companies listed on B3, the main stock exchange in Brazil. This index reflects the performance of the largest publicly traded companies in Brazil.

B3 Stock Exchange Foreign Company Index: An index that tracks the performance of foreign companies listed on B3, showcasing the international diversity of Brazil's stock market.

SuperMercado Brasil: A Brazil-based supermarket chain with operations across various regions. SuperMercado Brasil focuses on providing essential products to local communities and contributing to the retail sector's growth.

FinanceBrasil Group: A financial services provider in Brazil with a focus on inclusive finance, digital banking, and innovative financial solutions to meet the diverse needs of customers.

AgriTech Brasil: A leading producer and distributor of certified crop seeds, agricultural technology, and solutions, supporting sustainable farming practices and food security in Brazil and beyond.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Brazil, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price ‍

Q&A:

How much does the Corporate Actions Dataset cost in Brazil?

The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequenc...

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

ABSTRACT This study aimed to compare the performance of Tick Rule (TR) and Bulk Volume Classification (BVC) models in classifying assets traded on the Brazilian stock exchange (B3) and indicate which one performs better as an investment decision tool. The assets were split into three groups based on their volume, and actual data was used to assess the accuracy of both algorithms. Data from 2018 was used to estimate the parameters that best fit BVC, and transactions from 2019 were used to test the algorithm’s efficiency. Afterward, the Volume-Synchronized Probability of Informed Trading (VPIN) was calculated for each asset using TR and BVC, and the values obtained were compared against VPIN calculated using real data. In conclusion, the TR algorithm shows betters performance than BVC for all three groups of assets. Analysis of the properties of both methods reveals that the base upon which the TR is built holds up in the Brazilian market, whereas BVC mechanics does not reflect the observed reality.

Context

The IBovespa points are the index tha measurate brazilian stock market through a based system in reais. The average performance of a theoretical portfolio with the most representative and traded shares on the stock exchange.

Content

The data represent variation of points in a day, with open, close, maximum and minimum points that index reached.

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.

LND Brasilagro Brazilian Agric Real Estate Co Sponsored ADR (Brazil)

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 In 2015, the Financial Economics Research Center (NEFIN) of the University of São Paulo proposed an implicit volatility index for the Brazilian stock market based on the daily prices of options for the Bovespa index (Ibovespa) and that measures the expected volatility of the Ibovespa in the next two months. The aim of this study is to determine whether this implicit volatility index can be considered an antecedent indicator of future returns of the Brazilian stock market, given that it represents the expected volatility of the Ibovespa two months into the future. This study contributes to the literature on the implicit volatility index for the Brazilian stock market, which has been scarce until now. This happens due to the recent establishment of the index and due to the fact that there is not an official one published by the B3 S.A. - Brasil, Bolsa, Balcão (B3). Given the relationship found between the Brazilian implicit volatility index and the future returns of the Ibovespa, investors could anticipate instabilities in the Brazilian market by putting together strategies to protect their investment portfolios, as well as identifying opportunities to enter and exit the market. This research corroborates in disclosing the Brazilian implicit volatility index in order for it to become more widely used in academia and in the Brazilian financial market. The increase in studies on this index may also incentivize the launch of an official implicit volatility index by the B3. The relationship between the Brazilian implicit volatility index and the future returns of the Ibovespa is examined using least squares and quantile regressions. The implicit volatility index for the Brazilian stock market could help in predicting the future returns of the Ibovespa, especially for 20-, 60-, 120-, and 250-day future returns.

Supplementary Material 1.

The Brazil Data Center Processor Market report segments the industry into By Processor Type (GPU, CPU, FPGA, AI Accelerator), By Application (Advanced Data Analytics, AI/ML Training and Inferences, High Performance Computing, Security and Encryption, Network Functions, Others), By Architecture (x86, Non-x86 (ARM, Power and other processors)), and By Data Center Type (Enterprise, Colocation, Cloud Service Providers).

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.

Banco Bradesco (BBDO) : Riding the Brazilian Wave?

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

The size of the Brazil Data Center Storage market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 8.40% during the forecast period.This describes the Brazil data center storage market as a conglomeration of the technologies and systems utilized for data storing and handling big data inside Brazilian data centers. Data center storage is specific hardware and software modules applied to secure holding, ordering, and making organizational digital information available. Among its types of storages, are :Disk arrays: A group of hard disks, used in high-performance storage systems, which offers more speed and capacity.Tape libraries: A way of long-term data storage wherein large volumes of data can be archived.Object storage: Scalable systems for the storage and retrieval of unstructured data like images, videos, and documents.Cloud storage: Data storage services provided over the internet, so access and sharing can be accessed from anywhere.Data center storage plays a major role in lots of applications in business operations.Business operations- It has to store and manage a critical business dataset that include; customer records financial transactions and data relating to their employees.Cloud computing: There is the establishing base for services because companies have now become adept at accessing or utilizing data and their applications remotely.Big data analytics: This involves storage and processing of massive datasets for insight and decision-making.AI/ML : Provide for training the AI and ML models, to hold the bulk training data.Business Continuity/Disaster Recovery : Allows businesses to have an operational contingency for regaining its operation if an adverse incident prevents access or interferes with ongoing activities. Recent developments include: March 2024: Dell, in collaboration with NVIDIA, launched NVIDIA DGX systems. With this, Dell PowerScale was validated for NVIDIA DGX SuperPOD. Using Dell's industry-leading network-attached storage, customers can confidently boost their AI and GenAI initiatives. The NVIDIA AI Enterprise software platform, which provides a full-stack, secure, and stable AI supercomputing solution, is part of NVIDIA DGX SuperPOD., April 2023: Hewlett Packard Enterprise announced new file, block, disaster, and backup recovery data services designed to help customers eliminate data silos, reduce cost and complexity, and improve performance. The new file storage data services deliver scale-out, enterprise-grade performance for data-intensive workloads, and the expanded block services provide mission-critical storage with mid-range economics.. Key drivers for this market are: Increased Storage Capacity and Price Reduction Leading to Preference over HDDs, Evolution of Hybrid Flash Arrays and Increased Sales of all Flash Arrays. Potential restraints include: Compatibility and Optimum Storage Performance Issues. Notable trends are: IT and Telecom to Hold Significant Market Share.