The share of systematic hedge fund launches that uses artificial intelligence or machine learning grew overall during the last decade. One percent of fund launches in 2010 used artificial intelligence or machine learning, and the proportion peaked at 24 percent of fund launches in 2018.
This table shows the aggregate assets and liabilities of hedge funds that file Form PF with the Securities and Exchange Commission. Unlike table B.101.f in the regular Financial Accounts publication, which reports assets and liabilities of domestic hedge funds only, this table presents data on all hedge funds that file Form PF, both domestic and foreign.
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The Investment Fund Services market is experiencing robust growth, projected to reach a market size of $150 billion by 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is driven by several key factors. The increasing complexity of global financial regulations necessitates sophisticated fund administration and management solutions, fueling demand for specialized services. Furthermore, the rise of alternative investment vehicles, such as private equity and hedge funds, is contributing significantly to market growth. Technological advancements, including the adoption of AI and machine learning for portfolio optimization and risk management, are streamlining operations and improving efficiency. The expanding global wealth pool and rising institutional investment in funds further underpin the market's trajectory. Increased regulatory scrutiny is also impacting the market, encouraging players to invest in compliance and risk management solutions. The market is segmented by type (Software, Service) and application (Enterprise, Individual), catering to diverse client needs. Key players like DTCC, Clearstream, and several major global banks are actively competing, driving innovation and service enhancement. Geographic distribution shows a strong concentration in North America and Europe, which currently hold the largest market share. However, the Asia-Pacific region, particularly China and India, demonstrates significant growth potential due to burgeoning domestic wealth and increased foreign investment. While market expansion is robust, potential restraints include cybersecurity threats, data privacy concerns, and the complexity of integrating new technologies into existing infrastructures. Successful players will need to navigate these challenges effectively to maintain their market position and capitalize on the significant opportunities ahead. The forecast period of 2025-2033 indicates a continued strong performance, fueled by technological innovation and growing global demand.
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High-Flyer hedge fund boldly redirects its $13.79 billion assets towards AGI, emblematic of its technological foresight and ambition.
In the third quarter of 2020, funding for artificial intelligence (AI)-focused companies reached 4.7 billion U.S. dollars in the United States. This quarter’s funding was the second largest over this four-year time period. Overall, artificial intelligence funding investment in the U.S. has significantly grown over the past few years.
AI investment in the U.S.
The United States leads in private investments in AI when compared with other regions around the world, especially considering that the top three investors for AI investments in the world are U.S. companies, namely Intel Capital, 500 Startups, and Y Combinator. The U.S.’s dominance in AI investment contributes to its increased role in the global AI industry. When compared with other countries, it is deemed as the most AI-ready government, which means that the U.S. is considered the country best situated in the world to implement AI within public services, from healthcare to education to transportation. Moreover, North America currently makes up the region with the most AI patent publications, which may also contribute to the U.S.’s dominant role in AI.
AI adoption in U.S. companies
AI has already been implemented in many organizations within the United States, with many of those who do not currently use AI planning to use it in some capacity in the future. This is not overly surprising given that companies who have fully embraced AI see more of a positive impact on their organizations than those who have not done so. The leading AI and analytic application for U.S. organizations is managing risk, fraud, and cybersecurity threats.
In 2022, the global total corporate investment in artificial intelligence (AI) reached almost 92 billion U.S. dollars, a slight decrease from the previous year. In 2018, the yearly investment in AI saw a slight downturn, but that was only temporary. Private investments account for a bulk of total AI corporate investment. AI investment has increased more than sixfold since 2016, a staggering growth in any market. It is a testament to the importance of the development of AI around the world.
What is Artificial Intelligence (AI)?
Artificial intelligence, once the subject of people’s imaginations and the main plot of science fiction movies for decades, is no longer a piece of fiction, but rather commonplace in people’s daily lives whether they realize it or not. AI refers to the ability of a computer or machine to imitate the capacities of the human brain, which often learns from previous experiences to understand and respond to language, decisions, and problems. These AI capabilities, such as computer vision and conversational interfaces, have become embedded throughout various industries’ standard business processes.
AI investment and startups
The global AI market, valued at 142.3 billion U.S. dollars as of 2023, continues to grow driven by the influx of investments it receives. This is a rapidly growing market, looking to expand from billions to trillions of U.S. dollars in market size in the coming years. From 2020 to 2022, investment in startups globally, and in particular AI startups, increased by five billion U.S. dollars, nearly double its previous investments, with much of it coming from private capital from U.S. companies. The most recent top-funded AI businesses are all machine learning and chatbot companies, focusing on human interface with machines.
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The High-frequency Trading Market is projected to be valued at 5.6 billion USD in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 7.5%, reaching approximately 10.2 billion USD by 2034.
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The Global Fintech-as-a-Service Platform Market Size Was Worth USD 232.17 Billion in 2021 and Is Expected To Reach USD 949 Billion by 2028, CAGR of 17%.
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Analysis of ‘Ratio of non-state investment leveraged to MHT administered funds awarded’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/233a4303-4a0b-45ac-b8b2-75c542f97b21 on 26 January 2022.
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This data shows how much private investment is generated with awards of state funds.
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Middle Office Outsourcing Market size was valued at USD 8087.59 Million in 2023 and is projected to reach USD 14844.38 Million by 2031, growing at a CAGR of 8.70% from 2024 to 2031.
Key Market Drivers:
Cost Efficiency and Scalability: One of the key reasons for middle office outsourcing is the possibility of cost savings. Outsourcing middle office operations such as risk management, compliance, and trade processing allows businesses to drastically cut operational expenses associated with keeping in-house staff. Outsourcing providers frequently have specialized knowledge and economies of scale allowing them to provide certain services more efficiently.
Access to Advanced Technology and Expertise: Another important factor is having access to cutting-edge technology and specialized knowledge. Middle office operations necessitate complex tools and systems for data management, analytics, and compliance monitoring. Outsourcing providers invest extensively in these technologies allowing their clients to access cutting-edge solutions that would be prohibitively expensive to develop in-house.
Regulatory Compliance and Risk Management: The growing complexity of regulatory regulations is another major driver of middle office outsourcing. Financial organizations face severe rules that necessitate strong compliance and risk management systems. Companies that outsource these services can reduce the risk of non-compliance and the resulting penalties. Outsourcing firms specialize in keeping up with changing rules and have the means to keep their clients compliant.
AIML hedge funds, which utilize artificial intelligence or machine learning, performed better than other hedge funds in the long run. AIML hedge funds had the lowest average return during 2019, but higher returns than the other over a three-year and five-year perspective.
AIML hedge funds, which are hedge funds using artificial intelligence or machine learning, performed better than systematic hedge funds and all hedge funds during the second quarter 2019, but performed the worst during the other quarters of that year. During the third quarter 2019, AIML hedge funds had negative net returns of 2.82 percent, while systematic hedge funds had negative net returns of -0.11 percent. However, AIML hedge funds outperform other hedge funds in the long run.
Lucror Analytics: Proprietary Hedge Funds Data for Credit Quality & Bond Valuation
At Lucror Analytics, we provide cutting-edge corporate data solutions tailored to fixed income professionals and organizations in the financial sector. Our datasets encompass issuer and issue-level credit quality, bond fair value metrics, and proprietary scores designed to offer nuanced, actionable insights into global bond markets that help you stay ahead of the curve. Covering over 3,300 global issuers and over 80,000 bonds, we empower our clients to make data-driven decisions with confidence and precision.
By leveraging our proprietary C-Score, V-Score , and V-Score I models, which utilize CDS and OAS data, we provide unparalleled granularity in credit analysis and valuation. Whether you are a portfolio manager, credit analyst, or institutional investor, Lucror’s data solutions deliver actionable insights to enhance strategies, identify mispricing opportunities, and assess market trends.
What Makes Lucror’s Hedge Funds Data Unique?
Proprietary Credit and Valuation Models Our proprietary C-Score, V-Score, and V-Score I are designed to provide a deeper understanding of credit quality and bond valuation:
C-Score: A composite score (0-100) reflecting an issuer's credit quality based on market pricing signals such as CDS spreads. Responsive to near-real-time market changes, the C-Score offers granular differentiation within and across credit rating categories, helping investors identify mispricing opportunities.
V-Score: Measures the deviation of an issue’s option-adjusted spread (OAS) from the market fair value, indicating whether a bond is overvalued or undervalued relative to the market.
V-Score I: Similar to the V-Score but benchmarked against industry-specific fair value OAS, offering insights into relative valuation within an industry context.
Comprehensive Global Coverage Our datasets cover over 3,300 issuers and 80,000 bonds across global markets, ensuring 90%+ overlap with prominent IG and HY benchmark indices. This extensive coverage provides valuable insights into issuers across sectors and geographies, enabling users to analyze issuer and market dynamics comprehensively.
Data Customization and Flexibility We recognize that different users have unique requirements. Lucror Analytics offers tailored datasets delivered in customizable formats, frequencies, and levels of granularity, ensuring that our data integrates seamlessly into your workflows.
High-Frequency, High-Quality Data Our C-Score, V-Score, and V-Score I models and metrics are updated daily using end-of-day (EOD) data from S&P. This ensures that users have access to current and accurate information, empowering timely and informed decision-making.
How Is the Data Sourced? Lucror Analytics employs a rigorous methodology to source, structure, transform and process data, ensuring reliability and actionable insights:
Proprietary Models: Our scores are derived from proprietary quant algorithms based on CDS spreads, OAS, and other issuer and bond data.
Global Data Partnerships: Our collaborations with S&P and other reputable data providers ensure comprehensive and accurate datasets.
Data Cleaning and Structuring: Advanced processes ensure data integrity, transforming raw inputs into actionable insights.
Primary Use Cases
Portfolio Construction & Rebalancing Lucror’s C-Score provides a granular view of issuer credit quality, allowing portfolio managers to evaluate risks and identify mispricing opportunities. With CDS-driven insights and daily updates, clients can incorporate near-real-time issuer/bond movements into their credit assessments.
Portfolio Optimization The V-Score and V-Score I allow portfolio managers to identify undervalued or overvalued bonds, supporting strategies that optimize returns relative to credit risk. By benchmarking valuations against market and industry standards, users can uncover potential mean-reversion opportunities and enhance portfolio performance.
Risk Management With data updated daily, Lucror’s models provide dynamic insights into market risks. Organizations can use this data to monitor shifts in credit quality, assess valuation anomalies, and adjust exposure proactively.
Strategic Decision-Making Our comprehensive datasets enable financial institutions to make informed strategic decisions. Whether it’s assessing the fair value of bonds, analyzing industry-specific credit spreads, or understanding broader market trends, Lucror’s data delivers the depth and accuracy required for success.
Why Choose Lucror Analytics for Hedge Funds Data? Lucror Analytics is committed to providing high-quality, actionable data solutions tailored to the evolving needs of the financial sector. Our unique combination of proprietary models, rigorous sourcing of high-quality data, and customizable delivery ensures that users have the insights they need to make smarter dec...
The largest private investment in artificial intelligence (AI) companies was in the medical and healthcare field. The investment there was over six billion U.S. dollars. Other substantial investments were found in data management, fintech, and cybersecurity, with around or over five point five billion U.S. dollars in investment within each of those fields. The lowest amount of investment was in AI companies in the venture capital (VC) field, with barely 20 million U.S. dollars invested. The only other industry with such low investment numbers was facial recognition, with barely 70 million U.S. dollars invested.
Comprehensive dataset of insider trading activities for Golden Gate Capital Investment Fund II-A (AI), L.P., including Form 4 filings and transaction visualizations across multiple 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?
Comprehensive Contact Information
Global Reach Across Private Equity Markets
Continuously Updated Datasets
Ethical and Compliant
Data Highlights:
Key Features of the Dataset:
Investment Decision-Maker Profiles
Advanced Filters for Precision Targeting
AI-Driven Enrichment
Strategic Use Cases:
Deal Origination and Pipeline Building
Advisory and Professional Services
Fundraising and Investor Relations
Market Research and Competitive Intelligence
Why Choose Success.ai?
Best Price Guarantee
Seamless Integration
Data Accuracy with AI Validation
Customizable and Scalable Solutions
...
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This list contains the following details for all funds registered with the program: * name * address * contact information * registration date * registration number * status About the labour sponsored investment fund program
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BASE YEAR | 2024 |
HISTORICAL DATA | 2019 - 2024 |
REPORT COVERAGE | Revenue Forecast, Competitive Landscape, Growth Factors, and Trends |
MARKET SIZE 2023 | 2.2(USD Billion) |
MARKET SIZE 2024 | 2.42(USD Billion) |
MARKET SIZE 2032 | 5.2(USD Billion) |
SEGMENTS COVERED | Deployment Mode ,End User Industry ,Functionality ,Asset Class ,Organization Size ,Regional |
COUNTRIES COVERED | North America, Europe, APAC, South America, MEA |
KEY MARKET DYNAMICS | 1 Rising demand for personalized wealth management solutions 2 Technological advancements in data analytics and artificial intelligence 3 Growing need for efficient portfolio management and risk assessment 4 Increasing adoption of cloudbased wealth management platforms 5 Regulatory changes and compliance requirements |
MARKET FORECAST UNITS | USD Billion |
KEY COMPANIES PROFILED | Addepar ,Orion Advisor Services ,Morningstar ,BlackRock ,Fidelity Investments ,DST Systems ,Morgan Stanley ,JPMorgan Chase ,Vestmark ,Schwab Advisor Services ,Envestnet ,SS&C Technologies ,UBS ,Fiserv |
MARKET FORECAST PERIOD | 2025 - 2032 |
KEY MARKET OPPORTUNITIES | Digitalization of financial services Increased demand for personalized wealth management Growing adoption of artificial intelligence AI and machine learning ML in wealth management Expansion into emerging markets Rising demand for sustainable and impact investing |
COMPOUND ANNUAL GROWTH RATE (CAGR) | 10.04% (2025 - 2032) |
The financial sector's spending on artificial intelligence (AI) is projected to experience substantial growth, with an estimated increase from 35 billion U.S. dollars in 2023 to 126.4 billion U.S. dollars in 2028. This represents a compound annual growth rate (CAGR) of 29 percent, indicating a significant upward trajectory in AI investment within the financial industry. AI investment across industries In 2023, the banking and retail sectors led in AI investments, with the banking sector accounting for 20.6 billion U.S. dollars and the retail sector investing 19.7 billion U.S. dollars. This demonstrates the varying degrees of AI adoption across different industries, with the financial sector poised for substantial growth over the coming years. These findings highlight the competitive landscape of AI investment and the potential for the financial sector to capitalize on AI technologies. Global corporate AI investment trends The global corporate investment in AI reached nearly 92 billion U.S. dollars in 2022, marking a significant increase from previous years. Private investments played a substantial role in driving this growth, underscoring the increasing importance of AI development worldwide. This trend signifies a strong foundation for the expansion of AI technologies, with implications for the financial sector's investment landscape as it navigates the evolving AI market.
Success.ai provides unparalleled access to Venture Capital Funding Data, meticulously curated to support organizations in identifying, connecting with, and analyzing global venture capital professionals. Our robust database includes verified profiles of VC analysts, fund managers, investment partners, and other key decision-makers. With AI-driven validation, continuously updated data, and extensive global coverage, our solutions empower businesses to excel in fundraising, partnership development, and strategic investment decisions.
Key Features of Success.ai's Venture Capital Funding Data:
Global Reach: Access profiles of venture capital professionals across 250+ countries, representing the world’s leading VC firms and emerging funds.
Comprehensive Profiles: Gain insights into the professional histories, investment focuses, and contact details of fund managers, analysts, and partners. Each profile includes work emails, phone numbers, and firmographic data.
Industry-Specific Data: Tailored to industries such as technology, healthcare, renewable energy, e-commerce, and more, ensuring highly relevant data for targeted outreach.
Real-Time Accuracy: Our AI-driven systems continuously update datasets to ensure a 99% accuracy rate, delivering the most reliable and actionable insights for your needs.
GDPR-Compliant Data: Fully compliant with global data privacy standards, ensuring ethical and legal usage across all business practices.
Why Choose Success.ai for Venture Capital Funding Data?
Best Price Guarantee: Our pricing is the most competitive in the market, ensuring you get maximum value for comprehensive VC funding data.
AI-Validated Accuracy: Advanced AI technology verifies every data point, reducing errors and improving efficiency in your outreach and analysis.
Tailored Solutions: Whether you need industry-specific data or a broader view of the VC landscape, our datasets are customized to meet your exact requirements.
Scalable Access: From startups seeking funding to established firms analyzing VC markets, our platform scales to meet the needs of all users.
Comprehensive Use Cases for VC Funding Data:
Identify and connect with venture capitalists whose investment priorities align with your business goals. Use verified profiles to build meaningful relationships and secure funding.
Understand the VC landscape, including trends, emerging sectors, and regional investment hotspots. Leverage this data to stay ahead in competitive markets.
Access detailed profiles to identify potential partners for co-investments, joint ventures, or syndications. Build strong networks with the right contacts.
Enhance your lead generation efforts with verified contact details for decision-makers at leading VC firms. Use accurate email and phone data to improve conversion rates.
Plan targeted events or outreach campaigns by accessing detailed data on VC professionals in your target sectors and regions.
APIs to Supercharge Your Outreach:
Enrichment API: Keep your systems up-to-date with real-time data enrichment, ensuring your VC contact lists remain accurate and actionable.
Lead Generation API: Access verified profiles and key contact details to maximize the efficiency of your lead generation campaigns. Perfect for connecting with top-tier venture capitalists and fund managers.
Tailored Solutions for Diverse Needs:
Startup Founders: Identify potential investors aligned with your funding stage and sector.
Investment Banks: Gain insights into VC funding trends and key players to support advisory services.
Corporate Development Teams: Discover VCs for partnership opportunities or to fund internal innovation projects.
Consulting Firms: Provide data-driven recommendations to clients by leveraging detailed VC funding data.
What Sets Success.ai Apart?
Extensive Database: Access verified profiles of thousands of venture capital professionals, from analysts to managing partners, across diverse industries and regions.
Ethical and Legal Compliance: All data is ethically sourced and complies with global standards, including GDPR, giving you confidence in every interaction.
Customizable Delivery: Receive data tailored to your specifications, whether you need a comprehensive dataset or niche industry insights.
Expert Support: Our team of experts is always available to help you maximize the value of our data solutions.
Transform Your Outreach with Success.ai:
With Venture Capital Funding Data, Success.ai empowers you to connect with the right investors, streamline your fundraising efforts, and gain unparalleled insights into the global VC ecosystem. Whether you’re a startup founder, corporate executive, or investment professional, our data solutions provide the tools you need to succeed.
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The share of systematic hedge fund launches that uses artificial intelligence or machine learning grew overall during the last decade. One percent of fund launches in 2010 used artificial intelligence or machine learning, and the proportion peaked at 24 percent of fund launches in 2018.