This package contains the datasets and source codes used in the PhD thesis entitled Predicting the Brazilian stock market using sentiment analysis, technical indicators and stock prices. The following files are included: File Labeled.zip - financial news labeled in two classes (Positive and Negative), organized to train Sentiment Analysis models. Part of these news were initially presented in [1]. Besides the news in this file, in the related PhD thesis the training dataset was complemented with the labeled news presented in [2]. File Unlabeled.zip - general unlabeled financial news collected during the period 2010-2020 from the following online sources: G1, Folha de São Paulo and Estadão. This file contains news from the Bovespa index and from the following companies: Banco do Brasil, Itau, Gerdau and Ambev. File Stocks.zip - stock prices from the companies Banco do Brasil, Itau, Gerdau, Ambev, and the Bovespa index. The considered period ranges from 2010 to 2020. File Models.zip - contains the source codes of the models used in the PhD thesis (i.e., Multilayer Perceptron, Long Short-Term Memory, Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Support Vector Machines). File Utils.zip - contains the source codes of the preprocessing step designed for the methodology of this work (i.e., load data and generate the word embeddings), alongside with stocks manipulation, and investment evaluation. [1] Carosia, A. E. D. O., Januário, B. A., da Silva, A. E. A., & Coelho, G. P. (2021). Sentiment Analysis Applied to News from the Brazilian Stock Market. IEEE Latin America Transactions, 100. DOI: 10.1109/TLA.2022.9667151 [2] MARTINS, R. F.; PEREIRA, A.; BENEVENUTO, F. An approach to sentiment analysis of web applications in portuguese. Proceedings of the 21st Brazilian Symposium on Multimedia and the Web, ACM, p. 105–112, 2015. DOI: 10.1145/2820426.2820446

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.

Stock Analysis Software Market Outlook

The global stock analysis software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The growth of this market is driven by the increasing adoption of advanced analytics tools by individual investors and financial institutions to make informed investment decisions. The rising demand for automated trading systems and the integration of artificial intelligence (AI) and machine learning (ML) in stock analysis software are significant growth factors contributing to the market expansion.

One of the primary growth factors for the stock analysis software market is the increasing complexity and volume of financial data. With the exponential growth of data from various sources such as social media, news articles, and financial statements, investors and financial analysts require sophisticated tools to process and interpret this information accurately. Stock analysis software equipped with AI and ML algorithms can analyze vast datasets in real-time, providing valuable insights and predictive analytics that enhance investment strategies. Moreover, the growing trend of algorithmic trading, which relies heavily on high-speed data processing and automated decision-making, is further propelling the market growth.

Another crucial growth driver is the rising awareness and adoption of stock analysis software among individual investors. As more individuals seek to actively manage their investment portfolios, there is a growing demand for user-friendly and cost-effective stock analysis tools that offer comprehensive market analysis, technical indicators, and personalized investment recommendations. The proliferation of mobile applications and the increasing accessibility of cloud-based stock analysis solutions have made it easier for retail investors to access advanced analytical tools, thereby contributing to market expansion.

The integration of innovative technologies such as natural language processing (NLP) and sentiment analysis into stock analysis software is also a significant growth factor. These technologies enable the software to interpret and analyze unstructured data from news articles, social media, and other textual sources to gauge market sentiment and predict stock price movements. This capability is particularly valuable in today's fast-paced financial markets, where sentiment and news events can have a substantial impact on stock prices. The continuous advancements in AI and NLP technologies are expected to drive further innovations and improvements in stock analysis software, thereby boosting market growth.

In the evolving landscape of financial technology, Investor Relations Tools have become indispensable for companies seeking to maintain transparent and effective communication with their stakeholders. These tools facilitate seamless interaction between companies and their investors, providing real-time updates, financial reports, and strategic insights. By leveraging these tools, companies can enhance their investor engagement strategies, build trust, and foster long-term relationships with their shareholders. The integration of advanced analytics and AI-driven insights into Investor Relations Tools further empowers companies to tailor their communication strategies, ensuring that they meet the diverse needs of their investor base. As the demand for transparency and accountability in financial markets continues to grow, the adoption of sophisticated Investor Relations Tools is expected to rise, playing a crucial role in the broader ecosystem of stock analysis software.

From a regional perspective, North America is anticipated to hold the largest market share due to the high concentration of financial institutions, brokerage firms, and individual investors in the region. The presence of key market players and the early adoption of advanced technologies also contribute to the dominant position of North America in the global stock analysis software market. Additionally, the Asia Pacific region is expected to witness significant growth during the forecast period, driven by the increasing number of retail investors, rapid economic development, and the growing financial markets in countries such as China and India.

Component Analysis

Investments Time Series for American States Water Company. American States Water Company, through its subsidiaries, provides water and electric services to residential, commercial, industrial, and other customers in the United States. The company operates through three segments: Water, Electric, and Contracted Services. It purchases, produces, distributes, and sells water, as well as distributes electricity. The company provides water service to approximately 264,600 customers located within approximately 80 communities in Northern, Coastal, and Southern California; and distributes electricity to approximately 24,900 customers in the City of Big Bear Lake and surrounding areas in San Bernardino County, California. It also offers water and/or wastewater services at various military installations. American States Water Company was founded in 1929 and is headquartered in San Dimas, California.

Investments Time Series for American Tower Corp. American Tower, one of the largest global REITs, is a leading independent owner, operator and developer of multitenant communications real estate with a portfolio of nearly 150,000 communications sites and a highly interconnected footprint of U.S. data center facilities.

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.

JPMorgan American Investment Trust (JAM): Questioning the Future

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 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.

Techsalerator’s Business Funding Data for North America is an extensive and insightful resource designed for businesses, investors, and financial analysts who need a deep understanding of the Asian funding landscape. This dataset meticulously captures and categorizes critical information about the funding activities of companies across the continent, providing valuable insights into the financial health and investment trends within various sectors.

What the Dataset Includes: Funding Rounds: Detailed records of funding rounds for companies in North America, including the size of the round, the date it occurred, and the stages of investment (Seed, Series A, Series B, etc.).

Investment Sources: Information on the sources of investment, such as venture capital firms, private equity investors, angel investors, and corporate investors.

Financial Milestones: Key financial achievements and benchmarks reached by companies, including valuation increases, revenue milestones, and profitability metrics.

Sector-Specific Data: Insights into how different sectors are performing, with data segmented by industry verticals such as technology, healthcare, finance, and consumer goods.

Geographic Breakdown: An overview of funding trends and activities specific to each North America country, allowing users to identify regional patterns and opportunities.

EU Countries Included in the Dataset: Antigua and Barbuda Bahamas Barbados Belize Canada Costa Rica Cuba Dominica Dominican Republic El Salvador Grenada Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Trinidad and Tobago United States

Benefits of the Dataset: Informed Decision-Making: Investors and analysts can use the data to make well-informed investment decisions by understanding funding trends and financial health across different regions and sectors. Strategic Planning: Businesses can leverage the insights to identify potential investors, benchmark against industry peers, and plan their funding strategies effectively. Market Analysis: The dataset helps in analyzing market dynamics, identifying emerging sectors, and spotting investment opportunities across North America. Techsalerator’s Business Funding Data for North America is a vital tool for anyone involved in the financial and investment sectors, offering a granular view of the funding landscape and enabling more strategic and data-driven decisions.

This description provides a more detailed view of what the dataset offers and highlights the relevance and benefits for various stakeholders.

Techsalerator’s Business Funding Data for Latin America is an extensive and insightful resource designed for businesses, investors, and financial analysts who need a deep understanding of the Latin America funding landscape. This dataset meticulously captures and categorizes critical information about the funding activities of companies across the continent, providing valuable insights into the financial health and investment trends within various sectors.

What the Dataset Includes: Funding Rounds: Detailed records of funding rounds for companies in Latin America, including the size of the round, the date it occurred, and the stages of investment (Seed, Series A, Series B, etc.).

Investment Sources: Information on the sources of investment, such as venture capital firms, private equity investors, angel investors, and corporate investors.

Financial Milestones: Key financial achievements and benchmarks reached by companies, including valuation increases, revenue milestones, and profitability metrics.

Sector-Specific Data: Insights into how different sectors are performing, with data segmented by industry verticals such as technology, healthcare, finance, and consumer goods.

Geographic Breakdown: An overview of funding trends and activities specific to each Asian country, allowing users to identify regional patterns and opportunities.

Latam Countries Included in the Dataset: Argentina Bolivia Brazil Chile Colombia Ecuador Guyana Paraguay Peru Suriname Uruguay Venezuela Central America: Belize Costa Rica El Salvador Guatemala Honduras Nicaragua Panama

Benefits of the Dataset: Informed Decision-Making: Investors and analysts can use the data to make well-informed investment decisions by understanding funding trends and financial health across different regions and sectors. Strategic Planning: Businesses can leverage the insights to identify potential investors, benchmark against industry peers, and plan their funding strategies effectively. Market Analysis: The dataset helps in analyzing market dynamics, identifying emerging sectors, and spotting investment opportunities across Latin America. Techsalerator’s Business Funding Data for Latin America is a vital tool for anyone involved in the financial and investment sectors, offering a granular view of the funding landscape and enabling more strategic and data-driven decisions.

This description provides a more detailed view of what the dataset offers and highlights the relevance and benefits for various stakeholders.

Foreign Direct Investment in the United States increased by 82453 USD Million in the second quarter of 2025. This dataset provides - United States Foreign Direct Investment - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Scottish American's (SAIN) Investment Leap: Where's the Next Destination?

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.

Can American Strategic Investment (NYC) Continue Its Market Outperformance?

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

Stock-Based-Compensation Time Series for Robinhood Markets Inc. Robinhood Markets, Inc. operates financial services platform in the United States. Its platform allows users to invest in stocks, exchange-traded funds (ETFs), American depository receipts, options, gold, and cryptocurrencies. The company offers fractional trading, recurring investments, fully-paid securities lending, access to investing on margin, cash sweep, instant withdrawals, retirement program, around-the-clock trading, joint investing accounts, event contracts, and future contract services. It also provides various learning and education solutions comprise Snacks, an accessible digest of business news stories for a new generation of investors.; Learn, which is an online collection of guides, feature tutorials, and financial dictionary; Newsfeeds that offer access to free, premium news from sites from various sites, such as Barron's, Reuters, and Dow Jones. In addition, the company offers In-App Education, a resource that covers investing fundamentals, including why people invest, a stock market overview, and tips on how to define investing goals, as well as allows customers to understand the basics of investing before their first trade; and Crypto Learn and Earn, an educational module available to various crypto customers through Robinhood Learn to teach customers the basics related to cryptocurrency. Further, it provides Robinhood credit cards, cash card and spending accounts, and wallets. The company also owns and operates a digital currency marketplace that allows companies and individuals from all around the world to buy and sell bitcoin, litecoin, ethereum, ripple, and bitcoin cash. Robinhood Markets, Inc. was incorporated in 2013 and is headquartered in Menlo Park, California.

3302 stocks present on the NASDAQ, with daily information ranging from 1962-01-02 till 2025-06-20.

The Nasdaq Stock Market (National Association of Securities Dealers Automated Quotations Stock Market) is an American stock exchange based in New York City. It is the most active stock trading venue in the US by volume, and ranked second on the list of stock exchanges by market capitalization of shares traded, behind the New York Stock Exporter. The exchange platform is owned by Nasdaq, Inc., which also owns the Nasdaq Nordic stock market network and several U.S.-based stock and options exchanges.

More info: https://en.wikipedia.org/wiki/Nasdaq

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.

General American Investors - Cumulative Preferred Stock (GAM-B): Worth Investing?

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

Other-Long-Term-Assets Time Series for Robinhood Markets Inc. Robinhood Markets, Inc. operates financial services platform in the United States. Its platform allows users to invest in stocks, exchange-traded funds (ETFs), American depository receipts, options, gold, and cryptocurrencies. The company offers fractional trading, recurring investments, fully-paid securities lending, access to investing on margin, cash sweep, instant withdrawals, retirement program, around-the-clock trading, joint investing accounts, event contracts, and future contract services. It also provides various learning and education solutions comprise Snacks, an accessible digest of business news stories for a new generation of investors.; Learn, which is an online collection of guides, feature tutorials, and financial dictionary; Newsfeeds that offer access to free, premium news from sites from various sites, such as Barron's, Reuters, and Dow Jones. In addition, the company offers In-App Education, a resource that covers investing fundamentals, including why people invest, a stock market overview, and tips on how to define investing goals, as well as allows customers to understand the basics of investing before their first trade; and Crypto Learn and Earn, an educational module available to various crypto customers through Robinhood Learn to teach customers the basics related to cryptocurrency. Further, it provides Robinhood credit cards, cash card and spending accounts, and wallets. The company also owns and operates a digital currency marketplace that allows companies and individuals from all around the world to buy and sell bitcoin, litecoin, ethereum, ripple, and bitcoin cash. Robinhood Markets, Inc. was incorporated in 2013 and is headquartered in Menlo Park, California.

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.

AEL^A American Equity Investment Life Holding Company Depositary Shares each representing a 1/1000th interest in a share of 5.95% Fixed-Rate Reset Non-Cumulative Preferred Stock Series A

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.

American Well (AMWL) Stock: A Beneficial Investment?

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

Cash-and-Equivalents Time Series for Robinhood Markets Inc. Robinhood Markets, Inc. operates financial services platform in the United States. Its platform allows users to invest in stocks, exchange-traded funds (ETFs), American depository receipts, options, gold, and cryptocurrencies. The company offers fractional trading, recurring investments, fully-paid securities lending, access to investing on margin, cash sweep, instant withdrawals, retirement program, around-the-clock trading, joint investing accounts, event contracts, and future contract services. It also provides various learning and education solutions comprise Snacks, an accessible digest of business news stories for a new generation of investors.; Learn, which is an online collection of guides, feature tutorials, and financial dictionary; Newsfeeds that offer access to free, premium news from sites from various sites, such as Barron's, Reuters, and Dow Jones. In addition, the company offers In-App Education, a resource that covers investing fundamentals, including why people invest, a stock market overview, and tips on how to define investing goals, as well as allows customers to understand the basics of investing before their first trade; and Crypto Learn and Earn, an educational module available to various crypto customers through Robinhood Learn to teach customers the basics related to cryptocurrency. Further, it provides Robinhood credit cards, cash card and spending accounts, and wallets. The company also owns and operates a digital currency marketplace that allows companies and individuals from all around the world to buy and sell bitcoin, litecoin, ethereum, ripple, and bitcoin cash. Robinhood Markets, Inc. was incorporated in 2013 and is headquartered in Menlo Park, California.

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.

LON:SAIN SCOTTISH AMERICAN INVESTMENT CO. PLC

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