Use our Stock prices dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

DataSpark is a financial and investment research cloud platform that gives access to a universe of alternative and ESG datasets, to capture unique investment insights, signals, and analytics and make optimal investment and trading decisions.

The platform is designed for institutional investors, traders, and organizations and it's a uniquely powerful tool to augment investment and financial strategies.

The DataSpark license includes:

💎 A MegaTrend Radar: weekly updated lists of high-performing stocks curated by top analysts 💎 Quant Trackers: custom stocks scanners, unusual volume scanners, short interest lists, technical alerts and more...

🌱 A powerful dashboard for stocks analysis 🌱 Sentiment Data 🌱 Insiders Holdings 🌱 Large Funds Holdings and Trades 🌱 ESG Scores of Global companies

The Center for Research in Security Prices (CRSP) stock databases provide time-series and event data on individual stocks, augmented with market time-series. Daily and monthly time-series variables include returns, closing, low bid and high ask prices, and trading volume. Event data includes distributions, shares outstanding, names, etc.

Dataset is an external database available here for Cornell affiliates: https://johnson.library.cornell.edu/database/wharton-research-data-services-wrds/

Context

This is a Data set for Stock Price of Netflix . This Data set start from 2002 to 2022 . It was collected from Yahoo Finance.

Source

Yahoo Finance

The Investment Research Software market is experiencing robust growth, projected to reach $331 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 9.5% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing complexity of financial markets necessitates sophisticated software solutions for effective investment analysis. Secondly, the growing adoption of cloud-based technologies offers scalability and accessibility, fostering wider market penetration. Thirdly, a rising demand for data-driven insights and automation in investment decision-making fuels the demand for advanced analytical tools provided by these software solutions. The competitive landscape includes established players like Bloomberg Terminal (though not explicitly listed, it's a major player in this space) alongside a range of specialized providers like those mentioned – INVRS, dummies, ANALEC ResearchWise, StockGround, New Constructs, Valuatum, FinFolio, FundCount, inStream, and Backstop. These companies cater to diverse needs, from individual investors to large institutional firms, offering varying levels of functionality and pricing. The market segmentation (although not explicitly provided) likely includes solutions tailored to different user types (e.g., retail investors, professional fund managers, financial analysts), investment strategies (e.g., quantitative analysis, fundamental analysis), and asset classes (e.g., equities, bonds, derivatives). Future growth will depend on ongoing technological advancements, increasing regulatory scrutiny demanding enhanced compliance features within the software, and the broader adoption of artificial intelligence and machine learning capabilities for enhanced predictive modeling and risk assessment. The market's historical growth from 2019-2024, while not explicitly stated, is likely to have mirrored or slightly lagged the projected future growth, given that the base year is 2025. Competitive pressures, including pricing strategies and the integration of innovative features, will also play significant roles in shaping the market's trajectory over the coming years.

The Predictive AI in Stock Market is estimated to reach USD 4,100.6 Mn By 2034, Riding on a Strong 17.3% CAGR throughout the forecast period.

Complete list of dividend aristocrats stocks with real-time data, financial metrics, and screening criteria

In 2024, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years, and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges, where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the Financial Crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

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.

The Dow Jones U.S. Completion Total Stock Market 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

The global stock analysis software market is experiencing robust growth, driven by increasing adoption of algorithmic trading, rising retail investor participation, and the expanding use of advanced analytical tools. The market, currently valued at approximately $2.5 billion in 2025 (estimated based on typical market sizes for similar software segments and a logical extrapolation considering the provided CAGR), is projected to witness a Compound Annual Growth Rate (CAGR) of 12% over the forecast period (2025-2033). Key segments driving this expansion include the banking, financial services, and insurance (BFSI) sector, alongside the rapidly growing healthcare, telecom, and IT industries. The preference for sophisticated fundamental and technical analysis tools is fueling demand, with evolutionary analysis gaining traction as a promising emerging segment. Regional dominance is currently held by North America, attributable to a mature financial market and high technology adoption. However, Asia Pacific is anticipated to exhibit the highest growth rate, fueled by increasing market awareness and expanding internet penetration. The market's expansion is further propelled by the rising availability of user-friendly, cloud-based stock analysis platforms. However, challenges remain. These include the high initial investment costs for advanced software and the potential for complexities in data interpretation for less experienced users. Nonetheless, innovative features such as AI-powered predictive analytics and integration with brokerage accounts are expected to mitigate these barriers and enhance market adoption. The competitive landscape is marked by both established players and emerging startups, leading to innovation and further driving market growth. Competitive differentiation is achieved through advanced features, user experience, and robust customer support. The consistent need for accurate, timely, and actionable insights ensures the continued importance of this sector in navigating global financial markets.

A list of the top 50 James Investment Research Inc holdings showing which stocks are owned by James Investment Research Inc's hedge fund.

The global stock software market size was estimated at USD X.X million in 2022 and is projected to reach a value of USD X.X million by 2033, expanding at a CAGR of X.X% over the forecast period. The growth of the market is attributed to increasing adoption of online trading platforms, rising demand for advanced analytical tools, and growing awareness about financial markets. Additionally, government initiatives promoting financial literacy and access to investment tools are contributing to market expansion. The market is segmented based on type, application, and region. By type, the market is categorized into charting, analysis, and trading platform. The charting segment holds the largest market share due to its widespread use for technical analysis of stock market data. By application, the market is divided into financials, consumer goods, industrials, technology, consumer services, telecommunications, healthcare, basic materials, and oil and gas. The financials segment accounts for the highest market share as stock software is extensively used by financial institutions and individual investors for portfolio management and trading. Geographically, the market is segmented into North America, South America, Europe, Middle East & Africa, and Asia Pacific. North America is the dominant region in the stock software market due to the presence of a large number of established financial institutions and active traders.

Graph and download economic data for Stocks, Number of Shares Sold on the New York Stock Exchange for United States (M11002USM444NNBR) from Jan 1875 to Aug 1966 about stock market and USA.

Historical AI model predictions and analysis for Amazon stock across multiple timeframes and confidence levels

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.

FTSE 100: Where to Next?

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 Index of Common Stock Prices, New York Stock Exchange for United States (M11007USM322NNBR) from Jan 1902 to May 1923 about New York, stock market, indexes, 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.

RAG Stock: A Risky 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

Market Analysis The global stock trading and investing applications market size stood at approximately XXX million in 2023 and is projected to grow at a CAGR of XX% during the forecast period of 2025-2033. The rising popularity of online trading platforms, coupled with increasing smartphone penetration and financial literacy, drives market growth. Moreover, the integration of advanced technologies like AI and machine learning in trading applications enhances usability and improves decision-making, further fueling market expansion. Segments and Competitive Landscape The market is segmented based on type (mobile-based, web-based), application (professional traders, individuals), and region. Among the application segments, the individuals segment holds a significant share due to the increasing accessibility and affordability of trading platforms. Key players in the market include Charles Schwab, Fidelity Investments, Merrill Edge, Ally Invest, Interactive Brokers, Robinhood, and Social Finance. These established players offer a wide range of features and services, including stock trading, investment analysis tools, and customer support, to cater to the diverse needs of traders and investors.

Market Size statistics on the Online Stock Brokerages industry in the US

Snapshot img

Securities Exchanges Market Size 2025-2029

The securities exchanges market size is forecast to increase by USD 56.67 billion at a CAGR of 12.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for investment opportunities. This trend is fueled by a global economic recovery and a rising interest in various asset classes, particularly in emerging markets. Another key driver is the increasing focus on sustainable and environmental, social, and governance (ESG) investing. This shift reflects a growing awareness of the importance of long-term value creation and the role of exchanges in facilitating socially responsible investments. This trend is driven by the expanding securities business units, including stocks, bonds, mutual funds, and other securities, which cater to the needs of investment firms and individual investors. However, the market is not without challenges. Increasing market volatility poses a significant risk for exchanges and their clients.
Furthermore, the rapid digitization of trading and the emergence of alternative trading platforms are disrupting traditional exchange business models. To navigate these challenges, exchanges must adapt by investing in technology, expanding their product offerings, and building strong regulatory frameworks. Data analytics and big data are also crucial tools for e-brokerage firms to gain insights and make informed decisions. By doing so, they can capitalize on the market's growth potential and maintain their competitive edge. Geopolitical tensions, economic instability, and regulatory changes can all contribute to market fluctuations and uncertainty.

What will be the Size of the Securities Exchanges Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic market, financial instrument classification plays a crucial role in facilitating efficient trade matching through advanced execution quality metrics and order book liquidity. Quantitative trading models leverage options clearing corporation data to optimize portfolio holdings, while trade matching engines utilize high-speed data storage solutions and portfolio optimization algorithms to minimize latency and enhance market depth indicators. Data center infrastructure and network bandwidth capacity are essential components for supporting complex algorithmic trading strategies, including latency reduction and price volatility forecasting. Market impact measurement and risk assessment methodologies are integral to managing market impact and mitigating fraud, ensuring regulatory compliance through transaction reporting standards and regulatory compliance software.

Exchange traded funds (ETFs) have gained popularity, necessitating robust quote dissemination systems and trade surveillance analytics. Server virtualization and cybersecurity threat mitigation strategies further strengthen the market's resilience, enabling seamless integration of data-driven quantitative models and sophisticated fraud detection algorithms. Additionally, users of online trading platforms can easily monitor the performance of their assets thanks to real-time stock data.

How is this Securities Exchanges Industry segmented?

The securities exchanges 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.

Service

  Market platforms
  Capital access platforms
  Others


Trade Finance Instruments

  Equities
  Derivatives
  Bonds
  Exchange-traded funds
  Others


Type

  Large-cap exchanges
  Mid-cap exchanges
  Small-cap exchanges


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Switzerland
    UK


  APAC

    China
    Hong Kong
    India
    Japan


  Rest of World (ROW)

By Service Insights

The Market platforms segment is estimated to witness significant growth during the forecast period. The market is characterized by advanced technologies and systems that enable efficient price discovery, manage settlement risk, and ensure regulatory compliance. Market platforms, which include trading platforms, order-matching systems, and market data dissemination, hold the largest share of the market. These platforms facilitate the buying and selling of securities, providing market liquidity and transparency. Real-time market surveillance and high-frequency trading infrastructure are crucial components, ensuring fair and orderly markets and enabling efficient trade execution. Financial modeling techniques and algorithmic trading platforms optimize trading strategies, while electronic communication networks and central counterparty clearing minimize r