Explore LSEG's Private Equity Deals Data, including data and insight regarding a wide range of global private equity activities.

The Forager.ai Global Private Equity (PE) Funding Data Set is a leading source of firmographic data, backed by advanced AI and offering the highest refresh rate in the industry.

| Volume and Stats |

• Every company record refreshed twice a month, offering an unparalleled update frequency.
• Delivery is made every hour, ensuring you have the latest data at your fingertips.
• Each record is the result of an advanced AI-driven process, ensuring high-quality, accurate data.

| Use Cases |

Sales Platforms, ABM and Intent Data Platforms, Identity Platforms, Data Vendors:

Example applications include:

1. Uncover trending technologies or tools gaining popularity.

2. Pinpoint lucrative business prospects by identifying similar solutions utilized by a specific company.

3. Study a company's tech stacks to understand the technical capability and skills available within that company.

B2B Tech Companies:

• Enrich leads that sign-up through the Company Search API (available separately).
• Identify and map every company that fits your core personas and ICP.
• Build audiences to target, using key fields like location, company size, industry, and description.

Venture Capital and Private Equity:

• Discover new investment opportunities using company descriptions and industry-level data.
• Review the growth of private companies and benchmark their strength against competitors.
• Create high-level views of companies competing in popular verticals for investment.

| Delivery Options |

• Flat files via S3 or GCP
• PostgreSQL Shared Database
• PostgreSQL Managed Database
• API
• Other options available upon request, depending on the scale required

Our dataset provides a unique blend of volume, freshness, and detail that is perfect for Sales Platforms, B2B Tech, VCs & PE firms, Marketing Automation, ABM & Intent. It stands as a cornerstone in our broader data offering, ensuring you have the information you need to drive decision-making and growth.

Tags: Company Data, Company Profiles, Employee Data, Firmographic Data, AI-Driven Data, High Refresh Rate, Company Classification, Private Market Intelligence, Workforce Intelligence, Public Companies.

Success.ai’s Private Equity (PE) Funding Data provides reliable, verified access to the contact details of investment professionals, fund managers, analysts, and executives operating in the global private equity landscape. Drawn from over 170 million verified professional profiles, this dataset includes work emails, direct phone numbers, and LinkedIn profiles for key decision-makers in PE firms. Whether you’re seeking new investment opportunities, looking to pitch your services, or building strategic relationships, Success.ai delivers continuously updated and AI-validated data to ensure your outreach is both precise and effective.

Why Choose Success.ai’s Private Equity Professionals Data?

1. Comprehensive Contact Information

   • Access verified work emails, direct phone numbers, and social profiles for PE fund managers, analysts, partners, and principals.
   • AI-driven validation ensures 99% accuracy, reducing wasted efforts and enabling confident communication with industry leaders.

2. Global Reach Across Private Equity Markets

   • Includes profiles of professionals involved in leveraged buyouts, growth capital, venture investments, and secondary market deals.
   • Covers North America, Europe, Asia-Pacific, South America, and the Middle East, ensuring a global perspective on PE investments.

3. Continuously Updated Datasets

   • Real-time updates keep you informed about changes in roles, firm structures, and portfolio focus, helping you stay aligned with an ever-evolving investment environment.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other international data privacy regulations, ensuring your outreach is both ethical and legally compliant.

Data Highlights:

• 170M+ Verified Professional Profiles: Includes private equity professionals, decision-makers, and influential players worldwide.
• 50M Work Emails: AI-validated for direct, accurate communication.
• 30M Company Profiles: Gain insights into private equity firms, their portfolio companies, investment stages, and sector focuses.
• 700M Global Professional Profiles: Enriched datasets supporting broad market analysis, strategic planning, and competitive assessments.

Key Features of the Dataset:

1. Investment Decision-Maker Profiles

   • Identify and connect with fund managers, dealmakers, and senior executives overseeing capital allocation, portfolio management, and exit strategies.
   • Engage with professionals who influence investment theses, valuation approaches, and cross-border deals.

2. Advanced Filters for Precision Targeting

   • Refine outreach by region, deal size, industry preference, fund type, or specific job functions within the PE firm.
   • Tailor campaigns to align with unique investment philosophies, market segments, and strategic focuses.

3. AI-Driven Enrichment

   • Profiles are enriched with actionable data, equipping you with insights to personalize messaging, highlight unique value propositions, and enhance engagement outcomes.

Strategic Use Cases:

1. Deal Origination and Pipeline Building

   • Reach out to PE fund managers and analysts to present investment opportunities, co-investment deals, or M&A prospects.
   • Identify partners receptive to new growth capital deployments, early-stage investments, or strategic acquisitions.

2. Advisory and Professional Services

   • Offer due diligence, valuation, legal, or consulting services directly to decision-makers at private equity firms.
   • Position your expertise to streamline deal execution, portfolio optimization, or exit planning.

3. Fundraising and Investor Relations

   • Connect with IR professionals, placement agents, and fund administrators to discuss fundraising targets, investor outreach strategies, or institutional capital requirements.
   • Engage with professionals managing limited partner relationships, capital calls, and reporting obligations.

4. Market Research and Competitive Intelligence

   • Utilize PE data for comprehensive market analysis, competitor benchmarking, and trend identification.
   • Understand investment patterns, portfolio performance, and sector preferences to refine your business strategies.

Why Choose Success.ai?

1. Best Price Guarantee

   • Secure premium-quality verified data at competitive prices, maximizing the ROI of your outreach and lead-generation efforts.

2. Seamless Integration

   • Incorporate verified contact data into your CRM or marketing automation platforms using APIs or downloadable formats for streamlined data management.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to guide data-driven decisions, improve targeting, and enhance the effectiveness of your investment-related initiatives.

4. Customizable and Scalable Solutions

   • Adapt datasets to focus on particular geographies, deal sizes, or industry sectors, adjusting as your business needs evolve.

...

Comprehensive dataset of 1,505 Private equity firms in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 9 Private equity firms in Kentucky, United States as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

ABSTRACT The objective of this study was to verify the effects of the lock-up expiration on the behavior of prices and volumes in IPOs and follow-ons in the Brazilian market and to identify factors that may explain the existence and magnitude of abnormal returns. Few studies were found to investigate this phenomenon in Brazil, which were limited to the analysis of IPOs without examining the effect on follow-ons and the construction of abnormal accumulated returns compared to the Ibovespa, instead of benchmarks appropriate to each stock's risk. Lock-up clauses exist to mitigate the problem of information asymmetry in public offers but expose investors to the risk of a price drop after its expiration. Understanding the magnitude of this impact is essential for investors in the stock market. Through this article's analysis, investors will be able to estimate the magnitude of the price variation around the lock-up expiration, what factors explain the returns, and whether there are indications of short selling limitations. The event study method was applied, comparing returns to the Ibovespa and an individual reference portfolio composed of similar companies. Database: 313 offers that occurred on the Brazilian stock market between 2004 and 2019. Evidence of volume increase was found around the expiry of lock-up in IPOs, but the price drop was verified only in companies with private equity funds as shareholders. In follow-ons, in which the asymmetry of information about the issuer is less pronounced, the opposite situation was verified. There are several extensions and lock-up formats worldwide, which provide different impacts on volume and price. This article contributes to the literature when analyzing this event in Brazil and extending the analysis to follow-ons. A possible interpretation for the phenomenon is the restrictions on short-selling in the Brazilian market.

Success.ai presents an exclusive opportunity to connect directly with top-tier decision-makers in the finance sector through our CEO Contact Data, specifically designed for venture capital and private equity investors based in the USA. This tailored database is part of our expansive collection that draws from over 700 million global profiles, meticulously verified to ensure the highest quality and reliability.

Why Choose Success.ai’s CEO Contact Data?

Specialized Investor Profiles: Access detailed profiles of CEOs and senior executives from leading venture capital and private equity firms across the United States. Investment Insights: Gain valuable insights into investment trends, fund sizes, and sectors of interest directly from the decision-makers. Verified Contact Details: We provide up-to-date email addresses and phone numbers, ensuring that you reach the right people without the hassle of outdated information. Data Features:

Targeted Financial Sector Data: Directly target influential figures in the financial sector who have the authority to make investment decisions. Comprehensive Executive Information: Profiles include not just contact information but also professional backgrounds, areas of investment focus, and operational histories. Geographic Precision: Focus your outreach efforts on US-based investors with our geographically segmented data. Flexible Delivery and Integration: Choose from various delivery options including API access for real-time integration or static files for periodic campaign use, allowing for seamless incorporation into your CRM or marketing automation tools.

Competitive Pricing with Best Price Guarantee: Success.ai is committed to providing competitive pricing without compromising on quality, backed by our Best Price Guarantee.

Effective Use Cases for CEO Contact Data:

Fundraising Initiatives: Connect with venture capital and private equity firms for fundraising activities or financial endorsements. Partnership Development: Forge strategic partnerships and collaborations with leading investors in the industry. Event Invitations: Send personalized invites to investment summits, roundtables, and networking events catered to top financial executives. Market Analysis: Utilize executive insights to better understand the investment landscape and refine your market strategies. Quality Assurance and Compliance:

Rigorous Data Verification: Our data undergoes continuous verification processes to maintain accuracy and completeness. Compliance with Regulations: All data handling practices adhere to GDPR and other relevant data protection laws, ensuring ethical and lawful use. Support and Custom Solutions:

Client Support: Our team is available to assist with any queries or specific data needs you may have. Tailored Data Solutions: Customize data sets according to specific criteria such as investment size, sector focus, or geographic location. Start Connecting with Venture Leaders: Empower your business strategy and network building by accessing Success.ai’s CEO Contact Data for venture capital and private equity investors. Whether you're looking to initiate funding rounds, explore investment opportunities, or engage with top financial leaders, our reliable data will pave the way for meaningful connections and successful outcomes.

Contact Success.ai today to discover how our precise and comprehensive data can transform your business approach and help you achieve your strategic goals.

Comprehensive dataset of 52 Private equity firms in Colorado, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Stock Price Time Series for Heidrick & Struggles International. Heidrick & Struggles International, Inc., together with its subsidiaries, provides executive search, consulting, and on-demand talent services to businesses and business leaders worldwide. The company offers services to its clients to build leadership teams by facilitating the recruitment, management, and development of senior executives. Its on-demand services provide its clients independent talent, including professionals with industry and functional expertise for interim leadership roles and critical, and project-based initiatives; and consulting services, such as leadership assessment and development, team and organization acceleration, digital acceleration and innovation, diversity and inclusion advisory services, and culture shaping services. The company provides its services to Fortune 1000 companies; major U.S. and non-U.S. companies; middle market and emerging growth companies; private equity firms; governmental, higher education, and not-for-profit organizations; and other private and public entities. Heidrick & Struggles International, Inc. was founded in 1953 and is headquartered in Chicago, Illinois.

Success.ai’s Company Financial Data for Banking & Capital Markets Professionals in the Middle East offers a reliable and comprehensive dataset designed to connect businesses with key stakeholders in the financial sector. Covering banking executives, capital markets professionals, and financial advisors, this dataset provides verified contact details, decision-maker profiles, and firmographic insights tailored for the Middle Eastern market.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach and strategic initiatives are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution empowers your organization to build meaningful connections in the region’s thriving financial industry.

Why Choose Success.ai’s Company Financial Data?

1. Verified Contact Data for Financial Professionals

   • Access verified email addresses, direct phone numbers, and LinkedIn profiles of banking executives, capital markets advisors, and financial consultants.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and minimizing data inefficiencies.

2. Targeted Insights for the Middle East Financial Sector

   • Includes profiles from major Middle Eastern financial hubs such as Dubai, Riyadh, Abu Dhabi, and Doha, covering diverse institutions like banks, investment firms, and regulatory bodies.
   • Gain insights into region-specific financial trends, regulatory frameworks, and market opportunities.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, market activities, and organizational structures.
   • Stay ahead of emerging opportunities and align your strategies with evolving market dynamics.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible data usage and compliance with legal standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with decision-makers and professionals in banking, investment management, and capital markets across the Middle East.
• 30M Company Profiles: Access detailed firmographic data, including organization sizes, revenue ranges, and geographic footprints.
• Leadership Contact Information: Connect directly with CEOs, CFOs, risk managers, and regulatory professionals driving financial strategies.
• Decision-Maker Insights: Understand key decision-makers’ roles and responsibilities to tailor your outreach effectively.

Key Features of the Dataset:

1. Decision-Maker Profiles in Banking & Capital Markets

   • Identify and connect with executives, portfolio managers, and analysts shaping investment strategies and financial operations.
   • Target professionals responsible for compliance, risk management, and operational efficiency.

2. Advanced Filters for Precision Targeting

   • Filter institutions by segment (retail banking, investment banking, private equity), geographic location, revenue size, or workforce composition.
   • Tailor campaigns to align with specific financial needs, such as digital transformation, customer retention, or risk mitigation.

3. Firmographic and Leadership Insights

   • Access detailed firmographic data, including company hierarchies, financial health indicators, and service specializations.
   • Gain a deeper understanding of organizational structures and market positioning.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and enhance engagement outcomes.

Strategic Use Cases:

1. Sales and Lead Generation

   • Offer financial technology solutions, consulting services, or compliance tools to banking institutions and investment firms.
   • Build relationships with decision-makers responsible for vendor selection and financial strategy implementation.

2. Market Research and Competitive Analysis

   • Analyze trends in Middle Eastern banking and capital markets to guide product development and market entry strategies.
   • Benchmark against competitors to identify market gaps, emerging niches, and growth opportunities.

3. Partnership Development and Vendor Evaluation

   • Connect with financial institutions seeking strategic partnerships or evaluating service providers for operational improvements.
   • Foster alliances that drive mutual growth and innovation.

4. Recruitment and Talent Solutions

   • Engage HR professionals and hiring managers seeking top talent in finance, compliance, or risk management.
   • Provide staffing solutions, training programs, or workforce optimization tools tailored to the financial sector.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality financial data at competitive prices, ensuring strong ROI for your outreach, marketing, and partners...

Discover the Huge Stock Market Dataset with historical price and volume data from NYSE, NASDAQ, and NYSE MKT.

Other-Non-Cash-Items Time Series for SL Private Equity. SL Private Equity specializes in fund of funds and direct investments. It seeks to invest in mid-market buyouts and expansion capital. It focuses on investments in technology, healthcare, industrials, consumer discretionary, consumer staples, oil and gas services, financials, educational publishing, aero-engineering, and capital goods outside Europe. Within fund of fund investments it seeks to invest in private equity funds focused on mid to large sized buyouts. It seeks to invest in funds investing in companies with enterprise values ranging between EUR100 million ($111.10 million) and EUR2 billion ($2,881.60 million). The fund prefers to invest in the companies based in Europe. It prefer to have majority stake in companies.

🌍 Worldwide B2B Company Dataset | 65M+ Verified Records | Firmographics & API Access Power your sales, marketing, and investment strategies with the most comprehensive global B2B company data—verified, AI-driven, and updated bi-weekly.

The Forager.ai Global Company Dataset delivers 65M+ high-quality firmographic records, covering public and private companies worldwide. Leveraging AI-powered validation and bi-weekly updates, our dataset ensures accuracy, freshness, and depth—making it ideal for sales intelligence, market analysis, and CRM enrichment.

📊 Key Features & Coverage ✅ 65M+ Company Records – The largest, most reliable B2B firmographic dataset available. ✅ Bi-Weekly Updates – Stay ahead with refreshed data every two weeks. ✅ AI-Driven Accuracy – Sophisticated algorithms verify and enrich every record. ✅ Global Coverage – Companies across North America, Europe, APAC, and emerging markets.

📋 Core Data Fields: ✔ Company Name, LinkedIn URL, & Domain ✔ Industries ✔ Job postings, Revenue, Employee Size, Funding Status ✔ Location (HQ + Regional Offices) ✔ Tech Stack & Firmographic Signals ✔ LinkedIn Profile details

🎯 Top Use Cases 🔹 Sales & Lead Generation

Build targeted prospect lists using firmographics (size, industry, revenue).

Enhance lead scoring with technographic insights.

🔹 Market & Competitive Intelligence

Track company growth, expansions, and trends.

Benchmark competitors using real-time private company data.

🔹 Venture Capital & Private Equity

Discover investment opportunities with granular sector-level insights.

Monitor portfolio companies and industry shifts.

🔹 ABM & Marketing Automation

Enrich CRM data for hyper-targeted campaigns.

Power intent data and predictive analytics.

⚡ Delivery & Integration Choose the best method for your workflow:

REST API – Real-time access for developers.

Flat Files (CSV, JSON) – Delivered via S3, Wasabi, Snowflake.

Custom Solutions – Scalable enterprise integrations.

🔒 Data Quality & Compliance 95%+ Field Completeness – Minimize gaps in your analysis.

Ethically Sourced – Compliant with GDPR, CCPA, and global privacy laws.

Transparent Licensing – Clear usage terms for peace of mind.

🚀 Why Forager.ai? ✔ AI-Powered Accuracy – Better data, fewer false leads. ✔ Enterprise-Grade Freshness – Bi-weekly updates keep insights relevant. ✔ Flexible Access – API, bulk files, or custom database solutions. ✔ Dedicated Support – Onboarding and SLA-backed assistance.

Tags: B2B Company Data |LinkedIn Job Postings | Firmographics | Global Business Intelligence | Sales Leads | VC & PE Data | Technographics | CRM Enrichment | API Access | AI-Validated Data

Comprehensive dataset of 9 Private equity firms in Alabama, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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.

The data shows the Total Venture capital (code VENTURE) expressed as a percentage of GDP (Gross domestic product at market prices). Venture capital investment (VCI) is a subset of a private equity raised for investment in companies not quoted on stock market and developing new products and technologies. It is used to fund an early-stage (seed and start-up) or expansion of venture (later stage venture). The product has been discontinued since: 01 Apr 2019.

This dataset provides comprehensive, up-to-date information about the top 100 Software-as-a-Service (SaaS) companies globally as of 2025. It includes detailed financial metrics, company fundamentals, and operational data that are crucial for market research, competitive analysis, investment decisions, and academic studies.

Key Features

• 100 leading SaaS companies across various industries
• 11 comprehensive data points per company
• Current 2025 data including latest valuations and ARR figures
• Verified information from multiple reliable sources
• Clean, analysis-ready format with consistent data structure

Use Cases

1. Market Research: Analyze SaaS industry trends and market dynamics
2. Investment Analysis: Evaluate growth patterns and valuation multiples
3. Competitive Intelligence: Benchmark companies within sectors
4. Academic Research: Study business models and growth strategies
5. Data Science Projects: Build predictive models for SaaS metrics
6. Business Strategy: Identify successful patterns in SaaS businesses

Industries Covered

Enterprise Software (CRM, ERP, HR) Developer Tools & DevOps Cybersecurity Data Analytics & Business Intelligence Marketing & Sales Technology Financial Technology Communication & Collaboration E-commerce Platforms Design & Creative Tools Infrastructure & Cloud Services

Why This Dataset? The SaaS industry has grown to over $300 billion globally, with companies achieving unprecedented valuations and growth rates. This dataset captures the current state of the industry leaders, providing insights into what makes successful SaaS companies tick.

Sources/Proof of Data: Data Sources The data has been meticulously compiled from multiple authoritative sources:

Company Financial Reports (Q4 2024 - Q1 2025)

Official earnings releases and investor relations documents SEC filings for public companies

Investment Databases

Crunchbase, PitchBook, and CB Insights for funding data Venture capital and private equity announcements

Market Research Reports

Gartner, Forrester, and IDC industry analyses SaaS Capital Index and valuation reports

Industry Publications

TechCrunch, Forbes, Wall Street Journal coverage Company press releases and official announcements

Product Review Platforms

G2 Crowd ratings and reviews Capterra and GetApp user feedback

Data Verification

Cross-referenced across multiple sources for accuracy Updated with latest available information as of May 2025 Validated against official company statements where available

Techsalerator’s Business Funding Data for Europe is an extensive and insightful resource designed for businesses, investors, and financial analysts who need a deep understanding of the European 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 Europe, 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 European country, allowing users to identify regional patterns and opportunities.

EU Countries Included in the Dataset: Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden 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 Europe. Techsalerator’s Business Funding Data for Europe 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.

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Venture Capital Investment Market Size 2025-2029

The venture capital investment market size is forecast to increase by USD 2920.2 billion, at a CAGR of 37.9% between 2024 and 2029.

The Venture Capital (VC) investment market is experiencing significant growth, particularly in the biotech sector, driven by advancements in technology and innovation. This trend is fueled by an increasing number of high-net-worth individuals (HNWIs) worldwide, who are seeking to diversify their portfolios and invest in promising startups. However, this market faces challenges, including foreign exchange volatility, which can impact the returns on investments made across borders. As HNWIs continue to invest in VC funds, they bring not only capital but also expertise and industry connections, further enhancing the potential for successful ventures.
Simultaneously, biotech companies, with their innovative solutions, are attracting substantial VC interest, presenting significant opportunities for growth and returns. Navigating foreign exchange risks and identifying promising biotech startups will be crucial for VC firms seeking to capitalize on these trends and outperform their competitors.

What will be the Size of the Venture Capital Investment 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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The venture capital (VC) investment market continues to evolve, shaped by dynamic market conditions and diverse sector applications. Dividend yields and capital gains remain key drivers for investors, as they seek to maximize returns. Big data and growth hacking are increasingly integral to investment theses, enabling industry analysis and informed decision-making. Limited partnerships (LPs) and funds collaborate, with GPs overseeing operations and risk management. Deal sourcing and due diligence are essential components of the investment process, ensuring portfolio companies align with the fund's objectives. Revenue growth and marketing strategies are critical for portfolio companies, as they aim to scale and attract investment.

Term sheets outline investment details, while advisory boards provide strategic guidance. Financial modeling and cash flow management are essential for effective fund management. Technology infrastructure, including AI, cloud computing, and blockchain technology, underpins innovation and growth. Joint ventures and technology licensing offer opportunities for collaboration and expansion. Sales strategy and burn rate analysis help optimize portfolio performance. Private equity and data analytics provide valuable insights for investment opportunities. Stock options and Series A and B funding rounds offer potential for significant returns. Legal agreements and intellectual property (IP) rights are crucial for protecting investments and ensuring long-term success.

How is this Venture Capital Investment Industry segmented?

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

Sector

  Software
  Pharmaceutical and biotechnology
  Media and entertainment
  Medical devices and equipments
  Others


Type

  First-time venture funding
  Follow-on venture funding


Variant

  Institutional Investors
  Corporate venture capital
  Private equity firms
  Angel investors
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    The Netherlands
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Sector Insights

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

The market has witnessed significant activity in the software industry, with a focus on disruptive technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Blockchain technology. VC firms have invested billions of dollars in these areas, with some companies achieving unicorn status. The software sector includes application software, system infrastructure software, software as a service (SaaS), operating systems, database software, and analytics software. The growing number of entrepreneurs and businesses, estimated to be over 450 million and 300 million, respectively, is fueling the growth of the software segment in the market. VC funds have been actively involved in Series A funding, providing capital for early-stage startups, and Series B funding, for growth-stage companies.

Limited partnerships (LPs) have been essential in providing capital for these funds. Risk management is a critical factor in venture capital investment, with due diligence, financial modeling, and market analysis being crucial c

CompanyKG is a heterogeneous graph consisting of 1,169,931 nodes and 50,815,503 undirected edges, with each node representing a real-world company and each edge signifying a relationship between the connected pair of companies.

Edges: We model 15 different inter-company relations as undirected edges, each of which corresponds to a unique edge type. These edge types capture various forms of similarity between connected company pairs. Associated with each edge of a certain type, we calculate a real-numbered weight as an approximation of the similarity level of that type. It is important to note that the constructed edges do not represent an exhaustive list of all possible edges due to incomplete information. Consequently, this leads to a sparse and occasionally skewed distribution of edges for individual relation/edge types. Such characteristics pose additional challenges for downstream learning tasks. Please refer to our paper for a detailed definition of edge types and weight calculations.

Nodes: The graph includes all companies connected by edges defined previously. Each node represents a company and is associated with a descriptive text, such as "Klarna is a fintech company that provides support for direct and post-purchase payments ...". To comply with privacy and confidentiality requirements, we encoded the text into numerical embeddings using four different pre-trained text embedding models: mSBERT (multilingual Sentence BERT), ADA2, SimCSE (fine-tuned on the raw company descriptions) and PAUSE.

Evaluation Tasks. The primary goal of CompanyKG is to develop algorithms and models for quantifying the similarity between pairs of companies. In order to evaluate the effectiveness of these methods, we have carefully curated three evaluation tasks:

• Similarity Prediction (SP). To assess the accuracy of pairwise company similarity, we constructed the SP evaluation set comprising 3,219 pairs of companies that are labeled either as positive (similar, denoted by "1") or negative (dissimilar, denoted by "0"). Of these pairs, 1,522 are positive and 1,697 are negative.
• Competitor Retrieval (CR). Each sample contains one target company and one of its direct competitors. It contains 76 distinct target companies, each of which has 5.3 competitors annotated in average. For a given target company A with N direct competitors in this CR evaluation set, we expect a competent method to retrieve all N competitors when searching for similar companies to A.
• Similarity Ranking (SR) is designed to assess the ability of any method to rank candidate companies (numbered 0 and 1) based on their similarity to a query company. Paid human annotators, with backgrounds in engineering, science, and investment, were tasked with determining which candidate company is more similar to the query company. It resulted in an evaluation set comprising 1,856 rigorously labeled ranking questions. We retained 20% (368 samples) of this set as a validation set for model development.
• Edge Prediction (EP) evaluates a model's ability to predict future or missing relationships between companies, providing forward-looking insights for investment professionals. The EP dataset, derived (and sampled) from new edges collected between April 6, 2023, and May 25, 2024, includes 40,000 samples, with edges not present in the pre-existing CompanyKG (a snapshot up until April 5, 2023).

Background and Motivation

In the investment industry, it is often essential to identify similar companies for a variety of purposes, such as market/competitor mapping and Mergers & Acquisitions (M&A). Identifying comparable companies is a critical task, as it can inform investment decisions, help identify potential synergies, and reveal areas for growth and improvement. The accurate quantification of inter-company similarity, also referred to as company similarity quantification, is the cornerstone to successfully executing such tasks. However, company similarity quantification is often a challenging and time-consuming process, given the vast amount of data available on each company, and the complex and diversified relationships among them.

While there is no universally agreed definition of company similarity, researchers and practitioners in PE industry have adopted various criteria to measure similarity, typically reflecting the companies' operations and relationships. These criteria can embody one or more dimensions such as industry sectors, employee profiles, keywords/tags, customers' review, financial performance, co-appearance in news, and so on. Investment professionals usually begin with a limited number of companies of interest (a.k.a. seed companies) and require an algorithmic approach to expand their search to a larger list of companies for potential investment.

In recent years, transformer-based Language Models (LMs) have become the preferred method for encoding textual company descriptions into vector-space embeddings. Then companies that are similar to the seed companies can be searched in the embedding space using distance metrics like cosine similarity. The rapid advancements in Large LMs (LLMs), such as GPT-3/4 and LLaMA, have significantly enhanced the performance of general-purpose conversational models. These models, such as ChatGPT, can be employed to answer questions related to similar company discovery and quantification in a Q&A format.

However, graph is still the most natural choice for representing and learning diverse company relations due to its ability to model complex relationships between a large number of entities. By representing companies as nodes and their relationships as edges, we can form a Knowledge Graph (KG). Utilizing this KG allows us to efficiently capture and analyze the network structure of the business landscape. Moreover, KG-based approaches allow us to leverage powerful tools from network science, graph theory, and graph-based machine learning, such as Graph Neural Networks (GNNs), to extract insights and patterns to facilitate similar company analysis. While there are various company datasets (mostly commercial/proprietary and non-relational) and graph datasets available (mostly for single link/node/graph-level predictions), there is a scarcity of datasets and benchmarks that combine both to create a large-scale KG dataset expressing rich pairwise company relations.

Source Code and Tutorial:
https://github.com/llcresearch/CompanyKG2

Paper: to be published