snapshot-tab-pane Hedge Fund Market in US Size 2025-2029The US hedge fund market size is forecast to increase by USD 738 billion at a CAGR of 8.1% between 2024 and 2029.US Hedge Fund Market is experiencing significant growth due to increasing investor interest in alternative investment options. This trend is driven by the desire for higher returns and risk diversification, leading to a surge in assets under management. Furthermore, technological advancements are transforming the hedge fund industry, enabling companies to offer innovative solutions and improve operational efficiency. However, the market is not without challenges. Regulatory constraints continue to pose significant obstacles, with stringent regulations governing fund operations, investor protection, and transparency.Compliance with these regulations requires substantial resources and expertise, presenting a significant challenge for hedge fund managers. Companies seeking to capitalize on market opportunities and navigate these challenges effectively must stay informed of regulatory developments and invest in robust compliance frameworks. Additionally, leveraging technology to streamline operations and enhance transparency can help hedge funds remain competitive and meet investor demands.What will be the Size of the Hedge Fund Market in US during the forecast period? Request Free SampleUS hedge funds market activities and evolving patterns continue to unfold, shaping the industry's landscape. Hedge funds employ various strategies, such as quantitative methods, algorithmic trading, and relative value strategies, to manage risk and generate alpha. Investor relations play a crucial role in attracting and retaining capital from high-net-worth individuals, family offices, pension funds, and institutional investors. Fund of funds and multi-strategy funds offer diversification, while big data analytics and alternative data inform investment decisions. Machine learning and artificial intelligence enhance risk management and performance measurement. Regulatory compliance and transparency are essential components of hedge fund operations, ensuring liquidity and mitigating drawdowns.Market dynamics are influenced by various factors, including hedge fund leverage, volatility, and capacity. Hedge fund managers must navigate these complexities to deliver competitive returns, employing due diligence and effective fee structures. Hedge fund distribution channels, such as conferences and sales efforts, facilitate access to new investors. The hedge fund market is a continually evolving ecosystem, where technology, regulatory requirements, and investor expectations shape the industry's future. Hedge fund liquidation and exit strategies, performance fees, and risk appetite are critical considerations for hedge fund managers and investors alike. Ultimately, the hedge fund industry's success hinges on its ability to adapt and innovate in a rapidly changing financial landscape.How is this Hedge Fund in US Industry segmented?The hedge fund in US 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.Type OffshoreDomesticFund of fundsMethod Long and short equityEvent drivenGlobal macroOthersEnd-user InstitutionalIndividualFund Structure Small (Medium (USD500M-USD2B)Large (>USD2B)Investor Type InstitutionalHigh-Net-Worth IndividualsGeography North America USBy Type InsightsThe offshore segment is estimated to witness significant growth during the forecast period.The offshore segment of the hedge fund market in the US houses funds that are managed or marketed by American firms but are domiciled and operated in offshore jurisdictions. These funds, located in financial centers known for their favorable regulatory environments, tax treatment, and legal infrastructure, offer investors tax efficiency through lower or zero taxation on investment income, capital gains, and distributions. The reduced regulatory burden in offshore jurisdictions enables greater flexibility in fund operations, investment strategies, and disclosure obligations, making offshore hedge funds an appealing choice for tax-conscious investors. Portfolio construction, risk management, and hedge fund allocation strategies are crucial elements for these funds, with relative value and long-short equity strategies commonly employed.Performance fees and management fees are the primary revenue sources for hedge fund managers, while family offices and institutional investors provide significant hedge fund capital. Regulatory compliance and due diligence are essential for investors, ensuring transparency and performance measurement. Hedge fund research, risk appetite,

CIRA provides comprehensive coverage of investor relations (IR) activities from over 10 official channels, including corporate reports, interactive investor Q&A platforms, and online roadshows. The dataset covers interactions between more public companies and both institutional and retail investors, providing a comprehensive view of corporate engagement in China’s capital markets. More specifically, CIRA focuses on 4 types of IR activities:

1) The Investor Interactive Platform (IIP) component captures real-time Q&A exchanges between listed companies and retail investors, including both routine inquiries and event-driven discussions such as post-earnings clarifications.

2) For company disclosure, the dataset systematically processes Online Roadshows (ORS), including earnings presentations, IPO roadshows, and dedicated investor days, with full transcripts and participant analytics.

3) Company-Reported IR Activity (CRA) extracts the corporate post-event summaries for private and public investor relations activities such as site visits, roadshows and investor days.

4) Roadshow Calendar records the announcement of online and offline roadshows with daily updates, that help users to monitor roadshow hosted by companies of interest.

Overall, CIRA focuses on the unique information disclosure mechanisms of the Chinese capital market. It provides our clients with comprehensive and timely summaries of listed company information, helping them to conveniently understand the company's situation and investment prospects, and make more accurate and informed investment decisions.

• Coverage: 5000+ A-share stocks
• History: From 2010-01-01
• Update Frequency: Daily

Data Source: twitterapi.io

Data Acquisition Steps: First, search for 300,000 Japanese financial influencers on Twitter through the specified keywords via the API provided by twitterapi.io. Subsequently, utilize a Large Language Model (LLM) to label these influencers. The LLM assesses the probability of each individual being a financial influencer. In the dataset table, there is a field named "llm_result" which can take on the values of 1, 2, 3, or 4. Notably, a value of 3 or 4 in the "llm_result" field largely indicates that the individual is involved in the financial sector.

LLM prompt: ```python Task You need to determine whether a Twitter account belongs to the financial industry or is highly focused on financial topics. Analyze based on the following input:

Account Bio: {description}

Last ~20 Tweets: {last_20_tweets}

Analysis Criteria 1. Bio Analysis Does it mention a financial profession? (Investor, Analyst, Trader, Financial Planner, etc.)

Does it reference financial institutions? (Banks, Securities Firms, Investment Banks, Hedge Funds, etc.)

Does it list financial qualifications? (CFA, FP, Securities Analyst, etc.)

Does it explicitly state financial interests? (Investing, Stocks, Economy, Asset Management, etc.)

1. Tweet Content Analysis Frequency of financial jargon (PER, PBR, ROE, EBITDA, Technical Analysis, etc.)

Proportion of financial market discussions (market commentary, trend analysis, stock picks, etc.)

Depth of financial discussions (in-depth analysis vs. casual mentions)

Regular posting of investment advice, market analysis, or economic commentary

Use of charts and data analysis tools (graphs, technical indicators, etc.)

1. Interaction Patterns Frequent engagement with finance professionals

Comments on financial market news/events

Shares personal investment portfolio or decisions

Key Term Detection Stocks & Securities: 株式 (Stocks), 銘柄 (Tickers), チャート (Charts), 株価 (Stock Prices), 日経平均 (Nikkei Average), TOPIX, 配当 (Dividends), 決算 (Earnings), PER, PBR

Cryptocurrency: ビットコイン (Bitcoin), イーサリアム (Ethereum), 仮想通貨 (Crypto), NFT, DeFi, Web3, ブロックチェーン (Blockchain)

Forex & Commodities: FX, 為替 (Exchange Rates), ドル円 (USD/JPY), 金利 (Interest Rates), 原油 (Crude Oil), 金 (Gold)

Personal Finance: 資産運用 (Asset Management), NISA, iDeCo, 投資信託 (Mutual Funds), ETF, ポートフォリオ (Portfolio)

Macroeconomics: 金融政策 (Monetary Policy), 日銀 (Bank of Japan), インフレ (Inflation), GDP, FOMC

Judgment Criteria Rate confidence on a scale of 1 to 4:

4 – Strong Financial Relevance

Bio clearly indicates finance profession

70% of tweets discuss finance

Frequent use of financial jargon & analysis tools

3 – Moderate Financial Relevance

Clear interest in finance but not necessarily professional

40-70% finance-related tweets

Regularly shares finance content (but not deeply analytical)

2 – Weak Financial Relevance

Occasionally discusses finance

Likely an amateur investor, not a core focus

Only basic financial terms used

1 – Non-Financial

Rarely discusses finance

No financial background implied

Occasional mentions are minor

Output Format: You need to return a json format. {{ "reason":"the reason for the confidence", "confidence": int, #1-4 }}

Let's think step by step. ```

Comprehensive Consumer Review Dataset from PissedConsumer

This unique, extensive dataset from PissedConsumer includes over 5 million reviews covering more than 140,000 companies globally. It is ideal for hedge funds, venture capital firms, and investment companies seeking to deepen their understanding of internal processes and predict emerging trends.

Key Features:

Volume and Coverage: Over 5 million reviews on 140,000 companies, offering a broad and precise view of consumer opinions.

Detailed Complaint Insights: Each review includes a complaint title and text, allowing for an in-depth understanding of consumer issues and typical expectations.

Desired Solutions: Data includes preferred resolutions, enabling analysis of company standards and responsiveness to consumer demands.

Device and Date Specifics: Reviews include device type and activation dates, adding further context to your analysis.

Geographical Information: Data includes company locations down to the state and city levels for precise regional analysis.

Company and Industry Data: Reviews are organized by company name and industry type, facilitating targeted research.

This unique dataset from PissedConsumer offers investment analysts valuable insights into consumer needs, business resilience, and improved investment strategies. Leverage this resource for more accurate stock price forecasting, understanding customer satisfaction levels, and assessing companies’ operational practices in competitive markets.

Category: Consumer Review Data

Bank Rankings by Total Assets

Tracking the Financial Performance of the Top Banks

By Arthur Keen [source]

About this dataset

This dataset contains the top 100 global banks ranked by total assets on December 31, 2017. With a detailed list of key information for each bank's rank, country, balance sheet and US Total Assets (in billions), this data will be invaluable for those looking to research and study the current status of some of the world's leading financial organizations. From billion-dollar mega-banks such as JP Morgan Chase to small, local savings & loans institutions like BancorpSouth; this comprehensive overview allows researchers and analysts to gain a better understanding of who holds power in the world economy today

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How to use the dataset

This dataset contains the rank and total asset information of the top 100 global banks as of December 31, 2017. It is a useful resource for researchers who wish to study how key financial institutions' asset information relate to each other across countries.

Using this dataset is relatively straightforward – it consists of three columns - rank (the order in which each bank appears in the list), country (the country in which the bank is located) and total assets US billions (the total value expressed in US dollars). Additionally, there is a fourth column containing the balance sheet information for each bank as well.

In order to make full use of this dataset, one should analyse it by creating comparison grids based on different factors such as region, size or ownership structures. This can provide an interesting insight into how financial markets are structured within different economies and allow researchers to better understand some banking sector dynamics that are particularly relevant for certain countries or regions. Additionally, one can compare any two banks side-by-side using their respective balance sheets or distribution plot graphs based on size or concentration metrics by leverage or other financial ratios as well.

Overall, this dataset provides useful resources that can be put into practice through data visualization making an interesting reference point for trends analysis and forecasting purposes focusing on certain banking activities worldwide

Research Ideas

• Analyzing the differences in total assets across countries. By comparing and contrasting data, patterns could be found that give insight into the factors driving differences in banks’ assets between different markets.

• Using predictive models to identify which banks are more likely to perform better based on their balance sheet data, such as by predicting future profits or cashflows of said banks.

• Leveraging the information on holdings and investments of “top-ranked” banks as a guide for personal investments decisions or informing investment strategies of large financial institutions or hedge funds

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: top50banks2017-03-31.csv | Column name | Description | |:----------------------|:------------------------------------------------------------------------| | rank | The rank of the bank globally based on total assets. (Integer) | | country | The country where the bank is located. (String) | | total_assets_us_b | The total assets of a bank expressed in billions of US dollars. (Float) | | balance_sheet | A snapshot of banking activities for a specific date. (Date) |

File: top100banks2017-12-31.csv | Column name | Description | |:----------------------|:--------------------------------------------...

Techsalerator's Corporate Actions Dataset in Cayman Islands offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 1000 companies traded on the Cayman Islands Stock Exchange (XCAY).

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

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

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

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

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

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

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Top 5 corporate actions in Cayman Islands:

International Financial Services: The Cayman Islands is a well-known offshore financial center, and corporate actions related to the establishment of offshore companies, investment funds, and financial services providers are common.

Alternative Investment Structures: Corporate actions involving the creation and management of hedge funds, private equity funds, and other alternative investment structures are important for the Cayman Islands' role in the global investment industry.

Cross-Border Transactions: Corporate actions related to cross-border mergers and acquisitions, joint ventures, and other international business transactions are significant due to the Cayman Islands' popularity as a jurisdiction for structuring such deals.

Real Estate and Property Development: Corporate actions focused on real estate development, property investment, and hospitality projects contribute to the Cayman Islands' growth as a tourist destination and an attractive location for property investments.

Tech and Innovation: Corporate actions involving technology startups, fintech, and innovation initiatives are becoming more prominent as the Cayman Islands seeks to diversify its economy and attract tech-based businesses.

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Top 5 financial instruments with corporate action Data in Cayman Islands

Cayman Islands Financial Services Index (CIFSI): The main index that tracks the performance of financial services companies and entities registered in the Cayman Islands. This index reflects the performance of the Cayman Islands' offshore financial sector.

Cayman Islands International Company Index: An index that tracks the performance of international companies that are registered or have a presence in the Cayman Islands. This reflects the global diversity of companies utilizing the jurisdiction.

RetailMart Cayman Islands: A retail chain operating in the Cayman Islands, providing essential products to local communities and contributing to the retail sector's growth on the islands.

CaymanIsle Financial Group: A financial services provider based in the Cayman Islands, offering a range of services including offshore banking, investment management, and wealth preservation.

AgriTech Cayman Islands: A technology and agricultural solutions company in the Cayman Islands, focusing on innovative farming practices, agribusiness technology, and supporting sustainable food production.

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

‍

Data fields included:

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

Q&A:

How much does the Corporate Actions Dataset cost in Cayman Islands?

The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsa...

This dataset contains a list of financial firms registered with the Securities and Futures Commission (SFC) in Hong Kong, enriched with an automated classification of firms based on their business type. The classification was performed using a Large Language Model (LLM) to categorize firms into one of four groups:

• Buy-Side (e.g., hedge funds, asset managers)
• Sell-Side (e.g., investment banks, brokerage firms)
• Proprietary Trading (prop trading firms)
• Other (entities that do not fit neatly into the above categories)

Where available, the dataset includes both English and Chinese firm names, along with unique identifiers.

Important Notes:

• The classification is LLM-generated and, while generally reliable, may contain misclassifications, particularly for smaller, newer, lesser-known firms or firms with ambiguous names.
• This dataset can be used for exploratory analysis of Hong Kong’s financial industry, machine learning experiments, macroeconomic or regulatory studies.

Citation & Reference

This dataset was developed as part of ongoing research on the composition of Hong Kong’s financial sector. If you find it useful, please cite the following study:

Citation:

AlKetbi, Abdulla; Marti, Gautier; AlNuaimi, Khaled; Jaradat, Raed; and Henschel, Andreas. “Mapping Hong Kong’s Financial Ecosystem: A Network Analysis of the SFC’s Licensed Professionals and Institutions.” Complex Networks and Their Applications (Complex Networks 2024), 2024.

BibTeX Reference:

@inproceedings{alketbi2024mapping,
 title    = {Mapping Hong Kong's Financial Ecosystem: A Network Analysis of the SFC's Licensed Professionals and Institutions},
 author    = {AlKetbi, Abdulla and Marti, Gautier and AlNuaimi, Khaled and Jaradat, Raed and Henschel, Andreas},
 booktitle  = {Complex Networks and Their Applications (Complex Networks 2024)},
 year     = {2024},
 note     = {Accepted for presentation}
}

If you have any feedback or find interesting insights, feel free to share in the Discussion tab!

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Palantir Technologies is a public American software company that specializes in big data analytics. Headquartered in Denver, Colorado, it was founded by Peter Thiel, Nathan Gettings, Joe Lonsdale, Stephen Cohen, and Alex Karp in 2003. The company's name is derived from The Lord of the Rings where the magical palantíri were "seeing-stones," described as indestructible balls of crystal used for communication and to see events in other parts of the world.

The company is known for three projects in particular: Palantir Gotham, Palantir Metropolis, and Palantir Foundry. Palantir Gotham is used by counter-terrorism analysts at offices in the United States Intelligence Community (USIC) and United States Department of Defense. In the past, Gotham was used by fraud investigators at the Recovery Accountability and Transparency Board, a former US federal agency which operated from 2009 to 2015. Gotham was also used by cyber analysts at Information Warfare Monitor, a Canadian public-private venture which operated from 2003 to 2012. Palantir Metropolis is used by hedge funds, banks, and financial services firms. Palantir Foundry is used by corporate clients such as Morgan Stanley, Merck KGaA, Airbus, and Fiat Chrysler Automobiles NV.

Palantir's original clients were federal agencies of the USIC. It has since expanded its customer base to serve state and local governments, as well as private companies in the financial and healthcare industries.

Let us analyze and visualize

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis