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Access data on ownership by institutions, mutual funds, stakeholders, and float for both stocks and bonds on a global scale. Discover comprehensive details about shareholding patterns for equities and fixed-income instruments across the world, including institutional and mutual fund holdings, stakeholder interests, and floating shares. Obtain insights into the distribution of shares held by institutions, mutual funds, stakeholders, and the float for securities worldwide, encompassing both equities and bonds. Explore global share ownership information, covering institutional and mutual fund investments, stakeholder shares, and float-related holdings for both stocks and fixed-income securities.
API providing data for top institutional holders, mutual fund holders, and insider transactions.
Fintual is a general fund manager that started as a startup that went through the YCombinator accelerator. In recent times, Fintual has become the place where many Chileans have invested to save as an alternative to traditional mutual funds. In parallel to the investment series A, Fintual has an APV series, which is intended as voluntary savings for people's old age, as support for the mandatory savings that exist by law in Chile.
Fintual impone como novedad 4 nuevos fondos, de menos conservadores o más arriesgados son Conservative Streep, Moderate Pitt, Risky Norris. Los niveles de riesgo que Fintual dispone son 5 y son ponderaciones de estos 3 fondos, de más conservador a más arriesgado son:
-Muy Conservador: Conservative Streep 100%
-Conservador: Conservative Streep 57.8% + Moderate Pitt 42.2%
-Moderado: Conservative Streep 20.5%+Moderate Pitt 79.5%
-Arriesgado: Moderate Pitt %80.3 + Risky Norris %19.7
-Muy Arriesgado: Moderate Pitt 38.5% + Risky Norris 61.5%
The dataset is obtained from the Fintual API and the present files are 5:
fonds_info: General Information.
fonds_series_A: Features information from the Conservative Streep, Moderate Pitt, and Risky Norris funds for A series.
fonds_series_APV: Same as fond_series_A, but for APV series.
fintual_risk_lvl_A: Presents information on risk levels 'Muy Conservador', 'Conservador', 'Moderado', 'Arriesgado' and 'Muy arriesgado'.
fintual_risk_lvl_APV: Same as fintual_risk_lvl_A, but for A series.
Thanks to the Fintual team for facilitating the extraction of data from their API as an add-on in Google Sheets. You can find more information about Fintual at www.fintual.com.
In Chile, the political, social and economic environment has lived through very volatile times. At the same time, Fintual has become the choice of ordinary people who do not have much information about investing, but at the same time are bored with rigid banking protocols, which is why Fintual has become a very popular option in the last moment. That is why analyzing how Fintual funds vary can be a different window to analyze the social, political and economic changes in Chile and how this is consistent with important national and international events in the future.
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License information was derived automatically
United Kingdom NFC: Resources: API: PI: OI: Collective Investment Fund Shareholders data was reported at 3.000 GBP mn in Jun 2018. This stayed constant from the previous number of 3.000 GBP mn for Mar 2018. United Kingdom NFC: Resources: API: PI: OI: Collective Investment Fund Shareholders data is updated quarterly, averaging 3.000 GBP mn from Mar 1987 (Median) to Jun 2018, with 126 observations. The data reached an all-time high of 6.000 GBP mn in Dec 2007 and a record low of 0.000 GBP mn in Dec 1989. United Kingdom NFC: Resources: API: PI: OI: Collective Investment Fund Shareholders data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.AB028: ESA10: Resources and Uses: Non Financial Corporations: Primary Income.
Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries
Xverum’s Alternative Data delivers a unique lens into the evolving landscape of global businesses - offering non-traditional insights built from social media signals and public web profiles. With over 750M enriched professional profiles and 50M verified companies, this dataset empowers investors, hedge funds, and analysts to identify hidden trends, benchmark headcount dynamics, and make smarter portfolio decisions.
Our data bridges the gap between surface-level company metrics and internal workforce dynamics - ideal for those seeking high-signal, low-noise intelligence.
🔍 Key Features: ✅ Social Media–Derived Insights: Profiles collected and enriched from open social platforms and web sources. ✅ Workforce Trend Monitoring: Track hiring surges, downsizing, department shifts, and growth by role or region. ✅ Educational Intelligence: Understand degree types, universities, and certifications across a company’s talent base. ✅ 50M Company Profiles: Enriched with org size, industry, location, and growth signals. ✅ Dynamic Dataset: Monthly refresh with 350M+ updates per month to ensure timeliness. ✅ Fully GDPR/CCPA-Compliant: Ethically sourced and privacy-secure.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The dataset contains locations and attributes of Neighborhood Investment Fund Areas (NIF).The Neighborhood Investment Fund is an annual, non-lapsing fund to finance economic development and neighborhood revitalization in 12 targeted areas of the District. The fund is capitalized by an annual contribution of 15 percent of the personal property tax, not to exceed $10 million. The Neighborhood Investment Fund supports neighborhood revitalization through the Target Area Grant Program (TAPG) and Predevelopment and Project Grants (PDG).
http://www.gnu.org/licenses/lgpl-3.0.htmlhttp://www.gnu.org/licenses/lgpl-3.0.html
The National Stock Exchange of India Ltd. (NSE) is an Indian stock exchange located at Mumbai, Maharashtra, India. National Stock Exchange (NSE) was established in 1992 as a demutualized electronic exchange. It was promoted by leading financial institutions on request of the Government of India. It is India’s largest exchange by turnover. In 1994, it launched electronic screen-based trading. Thereafter, it went on to launch index futures and internet trading in 2000, which were the first of its kind in the country.
With the help of NSE, you can trade in the following segments:
Equities
Indices
Mutual Funds
Exchange Traded Funds
Initial Public Offerings
Security Lending and Borrowing Scheme
https://cdn6.newsnation.in/images/2019/06/24/Sharemarket-164616041_6.jpg" alt="Stock image">
Companies on successful IPOs gets their Stocks traded over different Stock Exchnage platforms. NSE is one important platofrm in India. There are thousands of companies trading their stocks in NSE. But, I have chosen two popular and high rated IT service companies of India; TCS and INFOSYS. and the third one is the benchmark for Indian IT companies , i.e. NIFTY_IT_INDEX .
The dataset contains three csv files. Each resembling to INFOSYS, NIFTY_IT_INDEX, and TCS, respectively. One can easily identify that by the name of CSV files.
Timeline of Data recording : 1-1-2015 to 31-12-2015.
Source of Data : Official NSE website.
Method : We have used the NSEpy api to fetch the data from NSE site. I have also mentioned my approach in this Kernel - "**WebScraper to download data for NSE**". Please go though that to better understand the nature of this dataset.
INFOSYS - 248 x 15 || NIFTY_IT_INDEX - 248 x 7 || **TCS - 248 x 15
Colum Descriptors:
Date
: date on which data is recorded
Symbol
: NSE symbol of the stock
Series
: Series of that stock | EQ - Equity
OTHER SERIES' ARE:
EQ: It stands for Equity. In this series intraday trading is possible in addition to delivery.
BE: It stands for Book Entry. Shares falling in the Trade-to-Trade or T-segment are traded in this series and no intraday is allowed. This means trades can only be settled by accepting or giving the delivery of shares.
BL: This series is for facilitating block deals. Block deal is a trade, with a minimum quantity of 5 lakh shares or minimum value of Rs. 5 crore, executed through a single transaction, on the special “Block Deal window”. The window is opened for only 35 minutes in the morning from 9:15 to 9:50AM.
BT: This series provides an exit route to small investors having shares in the physical form with a cap of maximum 500 shares.
GC: This series allows Government Securities and Treasury Bills to be traded under this category.
IL: This series allows only FIIs to trade among themselves. Permissible only in those securities where maximum permissible limit for FIIs is not breached.
Prev Close
: Last day close point
Open
: current day open point
High
: current day highest point
Low
: current day lowest point
Last
: the final quoted trading price for a particular stock, or stock-market index, during the most recent day of trading.
Close
: Closing point for the current day
VWAP
: volume-weighted average price is the ratio of the value traded to total volume traded over a particular time horizon
Volume
: the amount of a security that was traded during a given period of time. For every buyer, there is a seller, and each
transaction contributes to the count of total volume.
Turnover
: Total Turnover of the stock till that day
Trades
: Number of buy or Sell of the stock.
Deliverable
: Volumethe quantity of shares which actually move from one set of people (who had those shares in their demat account before today and are selling today) to another set of people (who have purchased those shares and will get those shares by T+2 days in their demat account).
%Deliverble
: percentage deliverables of that stock
I woul dlike to acknowledge all my sincere thanks to the brains behind NSEpy api, and in particular SWAPNIL JARIWALA , who is also maintaining an amazing open source github repo for this api.
I have also built a starter kernel for this dataset. You can find that right here .
I am so excited to see your magical approaches for the same dataset.
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.
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https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
The results data presented below is based on the portfolio of SREP projects and has been compiled on behalf of the following multilateral development banks: ADB, AFDB, IDB, IFC and IBRD. It follows the principles outlined under the Revised SREP Results Framework and includes the indicators that help determine whether and to what extent the SREP interventions achieve the proposed project outcome objectives involving: (a) Annual electricity output; (b) Improved energy access to people, businesses and community services; (c) GHG emissions reduced/avoided (tons of CO2 equivalent); (d) increased public and private investments in targeted subsectors (co-financing) You can learn more and get further analysis at the 2017 SREP Operational and Results Report: https://www.climateinvestmentfunds.org/sites/default/files/meeting-documents/srep_18_3_orr_1.pdf
This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore World Bank's Financial Data using Kaggle and all of the data sources available through the World Bank organization page!
This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.
This dataset is distributed under Creative Commons Attribution 3.0 IGO
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.
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Artificial Intelligence Market (AI) In Asset Management Size 2025-2029
The AI in asset management size is forecast to increase by USD 25.17 billion at a CAGR of 44.1% between 2024 and 2029.
The Artificial Intelligence (AI) market in asset management is experiencing significant growth, driven by the rapid adoption of AI technologies to enhance asset performance tracking and management capabilities. This trend is further fueled by the increasing popularity of cloud-based AI services, which offer greater flexibility and scalability for asset managers. However, the market also faces challenges related to data privacy and cybersecurity concerns, which require careful attention from industry players. Asset managers must ensure the secure handling of sensitive financial data and maintain compliance with regulatory requirements to mitigate risks and protect client information.
Navigating these challenges while capitalizing on the opportunities presented by AI in asset management requires a strategic approach and a deep understanding of the market landscape. Companies seeking to succeed in this market must prioritize data security, invest in advanced AI technologies, and build robust compliance frameworks to meet the evolving needs of clients and regulators.
What will be the Size of the Artificial Intelligence Market (AI) In Asset Management during the forecast period?
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The artificial intelligence (AI) market in asset management continues to evolve, with various sectors integrating advanced technologies to enhance operations and improve investment strategies. Regulatory reporting and due diligence processes are streamlined through API integration and decision support systems. Virtual advisors and family offices cater to retail investors, while institutional investors, pension funds, and alternative investment managers leverage machine learning for asset allocation and risk management. AI-driven trading and predictive analytics enable quantitative investment management and high-frequency trading. Additionally, computer vision and natural language processing facilitate financial modeling and investment research.
The ongoing integration of AI in asset management ensures continuous optimization and adaptation to market dynamics. Cloud computing enables scalable implementation and deployment of these advanced technologies. Overall, the AI market in asset management remains a dynamic and evolving landscape, with ongoing innovation and application across various sectors.
How is this Artificial Intelligence (AI) In Asset Management Industry segmented?
The artificial intelligence (ai) in asset management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.
Deployment
On-premises
Cloud
Application
BFSI
Retail and e-commerce
Healthcare
Energy and utilities
Others
Technology
Machine learning
Natural language processing
Others
Solution Type
Portfolio Optimization
Risk Management
Predictive Analytics
Robo-Advisors
Geography
North America
US
Mexico
Europe
France
Germany
Italy
Spain
UK
Middle East and Africa
UAE
APAC
Australia
China
India
Japan
South Korea
South America
Brazil
Rest of World (ROW)
By Deployment Insights
The on-premises segment is estimated to witness significant growth during the forecast period.
The on-premises segment of the artificial intelligence (AI) market in asset management is experiencing notable growth. On-premises AI solutions offer organizations greater control and flexibility over their data, as they are installed locally and customized to meet specific business requirements. Deep learning and machine learning algorithms are integrated into these solutions for advanced data analysis, enabling hedge funds, institutional investors, and family offices to make informed investment decisions. AI-driven risk management and fraud detection systems enhance financial technology, ensuring data security and regulatory compliance. Big data and predictive analytics are harnessed for quantitative investment management and portfolio optimization. Furthermore, AI-powered portfolio management and customer relationship management streamline operations, while natural language processing facilitates efficient investment research.
AI assistants and virtual advisors cater to retail investors, offering personalized investment strategies and recommendations. Cloud computing enables seamless API integration and real-time data processing, while algorithmic trading and high-frequency trading leverage AI for enhanced market insights. AI-driven research and sentiment analysis provide valuable alternativ
This table shows the total receipts and outlays and the resulting surplus or deficit (shown on the table as excess) for the current month and the current fiscal year-to-date for all federal trust funds. The table also shows the totals for securities held as investments by the federal trust funds for the beginning of the fiscal year and the beginning and ending of the current accounting month. A trust fund is a type of account, designated by law, for receipts or offsetting receipts dedicated to specific purposes and the expenditure of these receipts. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
NFC:资源:API:PI:OI:集合投资基金股东在06-01-2018达3.000百万英镑,相较于03-01-2018的3.000百万英镑保持不变。NFC:资源:API:PI:OI:集合投资基金股东数据按季更新,03-01-1987至06-01-2018期间平均值为3.000百万英镑,共126份观测结果。该数据的历史最高值出现于12-01-2007,达6.000百万英镑,而历史最低值则出现于12-01-1989,为0.000百万英镑。CEIC提供的NFC:资源:API:PI:OI:集合投资基金股东数据处于定期更新的状态,数据来源于Office for National Statistics,数据归类于全球数据库的英国 – 表 UK.AB028:2010年欧洲账户体系:资源和使用:非金融企业:主要收入。
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
NFC:资源:季节性调整后:API:PI:OI:集合投资基金股东在06-01-2018达3.000百万英镑,相较于03-01-2018的3.000百万英镑保持不变。NFC:资源:季节性调整后:API:PI:OI:集合投资基金股东数据按季更新,03-01-1987至06-01-2018期间平均值为3.000百万英镑,共126份观测结果。该数据的历史最高值出现于12-01-2007,达6.000百万英镑,而历史最低值则出现于12-01-1989,为0.000百万英镑。CEIC提供的NFC:资源:季节性调整后:API:PI:OI:集合投资基金股东数据处于定期更新的状态,数据来源于Office for National Statistics,数据归类于全球数据库的英国 – 表 UK.AB028:2010年欧洲账户体系:资源和使用:非金融企业:主要收入。
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View LSEG's Lipper Fund Research Database, providing independent fund content to benchmark fund performance, manage risk, and more.