View LSEG's Lipper Fund Research Database, providing independent fund content to benchmark fund performance, manage risk, and more.

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.

Context

Would you like to understand performance of a mutual fund. Would you like to compare performance of various mutual funds. How do I balance between returns and risk? Would you like to apply Data Science in your personal finance? Suppose you invest INR 1000 today what would be the value of your investment in a fund after a year? Can you predict? If so, how confident are you with your predictions?

Content

Dataset has details of Indian Mutual Funds and its daywise NAV from Year 2006 to Year 2023.

Total number of Schemes Covered: 35,350.

Number of Rows: 29 Million

Size of Dataset: 4.5 GB

Blog

Starter Analysis Blog

https://www.analyticsvidhya.com/blog/2021/03/end-to-end-application-of-data-science-in-personal-finance-mutual-funds-ranking/

Acknowledgments

Dataset is sourced through API https://www.mfapi.in/ using Python.

API providing data for top institutional holders, mutual fund holders, and insider transactions.

https://img.shields.io/github/v/release/captn3m0/india-isin-data" alt="GitHub release (latest by date)"> https://img.shields.io/github/v/tag/captn3m0/india-isin-data" alt="GitHub tag (latest SemVer)"> https://img.shields.io/github/repo-size/captn3m0/india-isin-data" alt="GitHub repo size">

ISIN Data from various public securities. You can sort and filter this dataset in your browser at https://flatgithub.com/captn3m0/india-isin-data.

Source: [NSDL Website Detailed ISIN Search][nsdl].

Automatically updated every midnight (IST).

Note: The [NSDL Website][nsdl] returns zero valid results for INA, INB, so those are not tracked.

Alternative Sources

ISINs for India can be found at a few other sources:

• https://nsdl.co.in/downloadables/html/hold-mutual-fund-units.html
• The Kuvera Mutual Fund Details API returns ISIN codes.
• The OpenFIGI API returns results for some (not all) Indian ISINs.
• The National Stock Exchange has a few pages listing Government Security ISINs.
• Similarly, the Reserve Bank of India also lists Government Securities at a few pages: [1], [2], [3], [4], [5].
• Securities admitted for Dematerialisation on the NSDL website
• CSV Download on the NSDL website. This doesn't include Government securities

License

Licensed under the Creative Commons Zero v1.0 Universal license. There are no guarantees made as to the correctness or accuracy of this data.

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.

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).

Investment Fund Of Al Bayt University Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Content

Weighted average of the rates received on the interest-bearing assets included in M2. The interest-bearing assets include size of the other checkable deposits, thrift saving deposits, money market mutual fund holdings, and small time deposits that are weighted using their corresponding rates.

The construction of this series was discontinued as of July 12, 2019. The underlying data can be accessed through the following sources: size of the assets can be obtained from the H.6 release (https://www.federalreserve.gov/releases/h6/current/default.htm) published by the Board of Governors, rate on the money market mutual funds from iMoneyNet (https://financialintelligence.informa.com/products-and-services/data-analysis-and-tools/imoneynet), and the remaining rates from the Weekly National Rates and Rate Caps (https://www.fdic.gov/regulations/resources/rates/index.html) from the FDIC website. Listing of the sources is provided for informational purposes only: the Federal Reserve Bank of St. Louis is not associated with any listed private entities and cannot guarantee that the listed data sources will provide the data in the future.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1959-02-01

• Observation End : 2019-06-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Leon Skibitzki on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Context

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%

Content

The dataset is obtained from the Fintual API and the present files are 5:

1. fonds_info: General Information.

2. fonds_series_A: Features information from the Conservative Streep, Moderate Pitt, and Risky Norris funds for A series.

3. fonds_series_APV: Same as fond_series_A, but for APV series.

4. fintual_risk_lvl_A: Presents information on risk levels 'Muy Conservador', 'Conservador', 'Moderado', 'Arriesgado' and 'Muy arriesgado'.

5. fintual_risk_lvl_APV: Same as fintual_risk_lvl_A, but for A series.

Acknowledgements

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.

Inspiration

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.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

This dataset is refreshed using yahoo finance backend api executed from my analytics application. I use the data to build graphical models for various indicators such as SMA, Stochastics, RSI. The information stored in the dataset is also used to develop buy and sell strategy. The application also shows various interactive graphs using the dataset. All graphs are developed using google graphs.

Chengdu Fund Investment Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Michelle Spollen on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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.

Content

Please note breaks in data: Data prior to 2003-01-01 include adjustment, extended, and seasonal credit. Data from 2003-01-01 to 2007-11-01 include primary, secondary, and seasonal credit. Data from 2007-12-01 to 2008-02-01 include primary, secondary, seasonal, and term auction credit. Data from 2008-03-01 forward include primary, secondary, seasonal credit, primary dealer credit facility, other credit extensions, and term auction credit. Data from 2008-09-01 are loans to depository institutions for primary, secondary, and seasonal credit, primary dealer and other broker-dealer credit. This category also includes the asset-backed commercial paper money market mutual fund liquidity facility, other credit extensions, and term auction credit.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1919-01-01

• Observation End : 2019-11-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Enrapture Captivating Media on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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

This layer is published from the Department of Community Affairs to show Federally designated Opportunity Zones.The U.S. Department of the Treasury and the Internal Revenue Service (IRS) have designated Opportunity Zones in 18 States, including 260 census tracts in the State of Georgia. Economic investment in these areas, which are some of the most distressed communities in the country, may now be eligible for preferential tax treatment. These new Federal Opportunity Zones are intended to facilitate investment in areas where poverty rates are greater than 20 percent.“This designation will enable some of our state’s struggling communities to attract much-needed private sector investment,” said DCA Commissioner Christopher Nunn. “By giving an economic ‘shot in the arm’ to these communities, the goal is to boost investment where it’s most urgently needed.”Georgia’s 260 zones, located in 83 counties, represent some of the most concentrated poverty in the state and are found in both rural and metropolitan areas, with approximately 60% rural and 40% urban. Qualified Opportunity Zones retain this designation for 10 years. Investors can defer tax on any prior gains until no later than December 31, 2026, so long as the gain is reinvested in a Qualified Opportunity Fund, an investment vehicle organized to make investments in Qualified Opportunity Zones. In addition, if the investor holds the investment in the Opportunity Fund for at least ten years, the investor would be eligible for an increase in its basis equal to the fair market value of the investment on the date that it is sold.Treasury and the IRS plan to issue additional information on Qualified Opportunity Funds to address the certification of Opportunity Funds, which are required to have at least 90 percent of fund assets invested in Opportunity Zones. DCA will communicate additional information about the specifics of the program as it is released by Treasury. Interactive map of designated Opportunity Zones.Additional information on Opportunity Zones.View a full list of Georgia’s designated census tracts, by county.Click here for FAQs.About the Georgia Department of Community AffairsThe Georgia Department of Community Affairs (DCA) partners with communities to create a climate of success for Georgia’s families and businesses through community and economic development, local government assistance, and safe and affordable housing. Using state and federal resources, DCA helps communities spur private job creation, implement planning, develop downtowns, generate affordable housing solutions, and promote volunteerism. DCA also helps qualified low- and moderate-income Georgians buy homes, rent housing, and prevent foreclosure and homelessness. For more information, visit www.dca.ga.gov.

Snapshot img

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?

Request Free Sample

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 alternative in

Content

This series is calculated by the Federal Reserve Bank of St. Louis as Total Borrowings of Depository Institutions from the Federal Reserve (BORROW) (https://fred.stlouisfed.org/series/BORROW) minus Term Auction Credit (TERMAUC) (https://fred.stlouisfed.org/series/TERMAUC). The Term Auction Credit (TERMAUC) (https://fred.stlouisfed.org/series/TERMAUC) was discontinued in May 2011.

The values are monthly averages.

Please note breaks in data: Data prior to 2003-01-01 include adjustment, extended, and seasonal credit. From 2003-01-01 to 2008-04-03, the data include primary, secondary, and seasonal credit. As of 2008-04-10, data include primary, secondary, and seasonal credit, primary dealer credit facility, and other credit extensions. Data from 2008-09-01 are loans to depository institutions for primary, secondary, and seasonal credit, primary dealer and other broker-dealer credit. This category also includes the asset-backed commercial paper money market mutual fund liquidity facility, credit extended to American International Group, Inc., and other credit extensions. Data were modified to include term asset-backed securities loan facility with the release on 2009-03-26.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1959-01-01

• Observation End : 2019-11-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Francesco Ungaro on Unsplash
Unsplash Images are distributed under a unique Unsplash License.