ABSTRACT This paper investigates if opacity (as measured by derivatives usage) creates value for investors and the managers of hedge funds that charge performance fees. Since we do not identify a positive relation between opacity and managers’ revenue, it is not possible to state that opacity is a source of manager’s value creation for hedge fund investors and managers. However, considering that opacity is positively associated with risk-taking and negatively related with investors’ adjusted returns, we suggest policies aiming at protecting investors, especially those less qualified. We examine a unique and comprehensive database related to the positions in derivatives taken by managers, which was enabled due to specific disclosure regulatory demands of the Brazilian Securities Exchange Commission, where detailed information on hedge funds’ portfolio allocation should be provided on a monthly basis.

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

1. 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.

2. 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.

3. 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.

4. 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...

We propose a dynamic measure of extremal connectedness tailored to the short reporting period and unbalanced nature of hedge funds data. Using multivariate extreme value regression techniques, we estimate this measure conditional on factors reflecting the economic uncertainty and the state of the financial markets, and derive risk indicators reflecting the likelihood of extreme spillovers. Empirically, we study the dynamics of tail dependencies between hedge funds grouped per investment strategies, as well as with the banking sector. We show that during crisis periods, some pairs of strategies display an increase in their extremal connectedness, revealing a higher likelihood of simultaneous extreme losses. We also find a sizable tail dependence between hedge funds and banks, indicating that banks are more likely to suffer extreme losses when the hedge fund sector does. Our results highlight that a proactive regulatory framework should account for the dynamic nature of the tail dependence and its link with financial stress.

Mutual Fund Dataset

Table of Contents

1. Introduction
2. Dataset Overview
3. Data Fields Description
4. Usage Guidelines
5. Handling Missing or Anomalous Data
6. Conclusion

Introduction

The Mutual Fund Dataset provides key information about various mutual fund schemes managed by multiple Asset Management Companies (AMCs). The dataset includes critical details such as fund ratings, returns across different time periods, NAV (Net Asset Value), and minimum investment requirements. Additionally, the dataset also contains information about the fund managers responsible for managing the portfolios.

This data can be particularly valuable for financial analysts, individual investors, and researchers seeking to evaluate the performance and characteristics of different mutual funds.

Dataset Overview

The dataset comprises the following major categories:

• AMC (Asset Management Company): The institution managing the mutual fund.
• Fund Ratings: Ratings from reputable organizations like Morningstar and Value Research.
• Returns: Historical performance of mutual funds over 1 month, 1 year, and 3 years.
• NAV (Net Asset Value): The per-unit price of the mutual fund.
• Investment Requirements: Information on the minimum investment needed to participate in a fund.
• Fund Manager: Details of the person responsible for managing the fund’s investment strategy.

Data Fields Description

Usage Guidelines

This dataset can be used for:

1. Comparative Analysis: Investors and analysts can compare mutual funds based on their returns, minimum investment requirements, and ratings from reputed agencies like Morningstar and Value Research.

2. Investment Strategy: The dataset can help in identifying high-performing funds based on past returns and other key factors, assisting in portfolio diversification or fund selection.

3. Financial Research: Researchers can analyze trends across the mutual fund industry, assess risk versus reward, or develop prediction models based on fund performance data.

Handling Missing or Anomalous Data

• Missing Data: Some columns, such as Fund Name, may have missing or incomplete data. Consider filtering out rows with insufficient data based on your use case.

• NA Values: Fields such as Morning Star Rating, Value Research Rating, and Fund Manager may contain 'NA' values, indicating unavailability or lack of a rating for certain funds.

• Other Category: Some columns include data points marked as "Other" to represent a collective...

Alternative Data Provider Market Outlook

The global alternative data provider market size was valued at approximately USD 2.5 billion in 2023 and is expected to reach around USD 11 billion by 2032, growing at a robust CAGR of 18% during the forecast period. The surge in market size is primarily driven by the increasing demand for unique insights that alternative data provides to investment firms, hedge funds, and other financial institutions.

One of the prominent growth factors fueling the alternative data provider market is the escalating number of data sources. With the digital footprint expanding across social media, web scraping, credit card transactions, and satellite data, firms are constantly seeking new ways to gain a competitive edge. Social media platforms alone generate an immense volume of data daily, enabling businesses to derive real-time insights into consumer behavior, market trends, and sentiment analysis. This vast pool of unstructured data, when properly processed and analyzed, provides a goldmine of information for investment strategies and risk management.

Another significant growth driver is the increasing adoption of advanced analytical tools and artificial intelligence (AI). These technologies enable the efficient processing and analysis of large datasets, thus enhancing the accuracy and reliability of the insights derived. AI algorithms, in particular, are adept at identifying patterns and trends that may not be immediately apparent to human analysts. Moreover, the integration of machine learning techniques allows for continuous improvement in data analysis capabilities, making alternative data an indispensable tool for financial institutions aiming to stay ahead of the market.

Furthermore, the growing regulatory emphasis on transparency and accountability in financial markets is driving the adoption of alternative data. Regulatory bodies across the globe are increasingly scrutinizing traditional data sources to ensure fair trading practices and risk mitigation. In response, financial institutions are turning to alternative data providers to gain a more comprehensive view of market dynamics and to comply with stringent regulatory requirements. This shift toward greater transparency is expected to further bolster market growth.

Regionally, North America dominates the alternative data provider market, owing to the early adoption of advanced technologies and the presence of major financial hubs. However, other regions such as Asia Pacific and Europe are rapidly catching up. In Asia Pacific, the burgeoning fintech sector and the increasing number of start-ups are contributing significantly to market growth. Europe, on the other hand, is witnessing a surge in demand due to stringent regulatory frameworks and a growing emphasis on sustainable investing practices.

Data Type Analysis

The alternative data provider market can be segmented by data type into social media data, web scraped data, credit card transactions, satellite data, and others. Social media data is a significant segment that impacts the market due to the sheer volume and variety of data generated through various platforms like Facebook, Twitter, and LinkedIn. This data includes user posts, comments, likes, shares, and other forms of engagement that can be analyzed to gauge market sentiment and predict consumer behavior. Social media data is invaluable for real-time analysis and immediate insights, making it a crucial component for investment and marketing strategies.

Web scraped data is another vital segment, offering an extensive array of information collected from various online sources like e-commerce websites, news sites, blogs, and forums. This data type provides insights into market trends, product popularity, pricing strategies, and consumer preferences. Web scraping tools extract relevant information efficiently, which can then be analyzed to provide actionable insights for businesses looking to optimize their operations and investment strategies.

Credit card transaction data is a high-value segment, offering precise insights into consumer spending patterns and financial behaviors. This data can be used to track economic trends, monitor the performance of specific sectors, and forecast future spending habits. Financial institutions and hedge funds rely heavily on this type of data to make informed investment decisions and to develop targeted marketing campaigns. The granularity and accuracy of credit card transaction data make it a powerful tool for financial analysis.

Satellite data is an e

Envestnet®| Yodlee®'s Bank Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

These tables provide additional detail on the investment holdings of U.S. money market funds, based on a monthly dataset of security-level holdings for all U.S. money market funds. Table 1 reports the aggregate dollar amount of investments of U.S. money market funds since 2010, by the world region and country of the security issuer. Table 2 provides a finer level of detail by month, showing, for each country of issuer, the aggregate dollar amount of investments of U.S. money market funds by type of money fund (i.e., prime, government, and municipal bond funds), type of security (i.e., direct debt and deposits, repurchase agreement, asset-backed commercial paper, and other), and by maturity of the security. Table 3 depicts the asset allocation of U.S. money market fund portfolios over time. Tables 4, 5, and 6 show the asset allocation of prime, government, and tax-exempt money market funds, respectively, over time. The sum of the values in these three tables equals the total value of Table 3. Tables 7 and 8 report additional detail on the repurchase agreement holdings and the commercial paper holdings, respectively, for U.S. money market funds.

Quant Fund Market Outlook

As of 2023, the global quant fund market size is estimated to be USD 1.2 trillion, with a projected CAGR of 8.5% leading to an anticipated market size of approximately USD 2.47 trillion by 2032. The rising adoption of algorithmic trading and advanced analytics stands out as a key growth factor driving this remarkable proliferation. The integration of artificial intelligence (AI) and machine learning (ML) to enhance trading strategies has been transforming the landscape, providing unprecedented opportunities for growth and efficiency gains.

One of the primary growth factors for the quant fund market is the increasing reliance on data-driven decision-making in financial markets. Institutional investors are progressively leveraging quantitative models to optimize their investment strategies, minimize risks, and capitalize on high-frequency trading opportunities. These sophisticated models, powered by AI and ML, allow for the processing of vast amounts of market data to uncover patterns and insights that would be nearly impossible to detect manually. This trend is expected to continue, further pushing the market's expansion.

Another significant factor contributing to the growth of the quant fund market is the technological advancements in computing power and data storage. The development of high-performance computing systems and the advent of cloud computing have enabled quantitative funds to process and analyze massive datasets in real-time. These technological innovations have not only enhanced the accuracy and efficiency of trading algorithms but also reduced the operational costs associated with running complex quantitative models. This evolution in technology is likely to sustain the market's growth trajectory in the coming years.

Furthermore, the increasing demand for diversification and risk management among investors is also driving the market's growth. Quantitative funds are designed to employ sophisticated strategies that aim to provide consistent returns while mitigating market risks. The ability to implement market-neutral strategies, statistical arbitrage, and trend-following techniques allows these funds to perform well even in volatile market conditions. This appeal of stable and diversified returns is attracting a broader range of investors, from institutional to retail, thereby expanding the market size.

The regional outlook for the quant fund market indicates that North America currently holds the largest market share, driven by the presence of numerous established quant funds and a mature financial ecosystem. However, the Asia Pacific region is anticipated to witness the highest growth rate over the forecast period, fueled by rapid economic development, increased adoption of advanced financial technologies, and a growing number of high-net-worth individuals seeking sophisticated investment solutions. Europe and Latin America are also expected to contribute significantly to the market growth, albeit at a slower pace compared to Asia Pacific.

Fund Type Analysis

The quant fund market can be segmented by fund type into equity funds, fixed income funds, multi-asset funds, and alternative funds. Within the equity funds segment, quantitative strategies have been particularly advantageous in identifying undervalued stocks and arbitrage opportunities, leading to a steady influx of investments. The application of machine learning algorithms to analyze stock performance and predict future trends has allowed equity-focused quant funds to generate consistent returns, attracting both institutional and retail investors.

Fixed income funds, on the other hand, have gained traction due to their ability to navigate the complexities of bond markets. Quantitative models in this segment are often employed to analyze interest rate movements, credit spreads, and economic indicators. The precision offered by these algorithms in predicting bond price movements has made fixed income quant funds a preferred choice for investors seeking stable returns with lower volatility compared to equity markets. Moreover, the inclusion of government and corporate bonds in their portfolios adds an additional layer of security for risk-averse investors.

Multi-asset funds, which combine equities, bonds, and other asset classes, have also seen significant growth. These funds leverage quantitative techniques to allocate assets dynamically based on market conditions. The ability to diversify across multiple asset classes while employing sophisticated risk management strategies makes multi-asset funds attractive to

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 1000 companies listed on the Cayman Islands Stock Exchange (XCAY) in Cayman Islands. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Cayman Islands :

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Cayman Islands:

Cayman Islands Stock Exchange (CSX) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Cayman Islands Stock Exchange. This index provides insights into the overall market performance of companies based in the Cayman Islands.

Cayman Islands Stock Exchange (CSX) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Cayman Islands Stock Exchange. This index reflects the performance of international companies that are listed and traded on the CSX.

Financial Services Corporation Cayman Trust Bank: A major financial institution based in the Cayman Islands, offering banking, investment, and wealth management services. This company's securities are listed and traded on the CSX.

Real Estate Development Group Cayman Properties: A prominent real estate development company operating in the Cayman Islands, involved in the construction of residential and commercial properties. This company's securities are listed on the CSX.

Offshore Investment Fund Cayman Capital: An offshore investment fund registered in the Cayman Islands, offering investment opportunities to both local and international investors. Units of this fund are traded on the CSX.

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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.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Cayman Islands ?

The cost of this 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 Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Cayman Islands?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Cayman Islands exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botsw...

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.

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

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

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

1. Comprehensive Contact Information

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

2. Global Reach Across Private Equity Markets

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

3. Continuously Updated Datasets

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

4. Ethical and Compliant

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

Data Highlights:

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

Key Features of the Dataset:

1. Investment Decision-Maker Profiles

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

2. Advanced Filters for Precision Targeting

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

3. AI-Driven Enrichment

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

Strategic Use Cases:

1. Deal Origination and Pipeline Building

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

2. Advisory and Professional Services

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

3. Fundraising and Investor Relations

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

4. Market Research and Competitive Intelligence

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

Why Choose Success.ai?

1. Best Price Guarantee

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

2. Seamless Integration

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

3. Data Accuracy with AI Validation

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

4. Customizable and Scalable Solutions

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

...

Free-Cash-Flow-To-The-Firm Time Series for Ameriprise Financial Inc. Ameriprise Financial, Inc., together with its subsidiaries, operates as a diversified financial services company in the United States and internationally. The company offers financial planning and advice services to individual and institutional clients. It operates through Advice & Wealth Management, Asset Management, and Retirement & Protection Solutions segments. The Advice & Wealth Management segment provides financial planning and advice; brokerage products and services for retail and institutional clients; discretionary and non-discretionary investment advisory accounts; mutual funds; insurance and annuities products; cash management and banking products; and face-amount certificates. The Asset Management segment offers investment management, advice, and products to retail, high net worth, and institutional clients through third-party financial institutions, advisor networks, direct retail, and its institutional sales force under the Columbia Threadneedle Investments brand name. Its products include U.S. mutual funds and their non-U.S. equivalents, exchange-traded funds, variable product funds underlying insurance, and annuity separate accounts; and institutional asset management products, such as traditional asset classes, separately managed accounts, individually managed accounts, collateralized loan obligations, hedge funds, collective funds, and property and infrastructure funds. The Retirement & Protection Solutions segment provides variable annuity products, as well as life and disability income insurance products to retail clients. The company was formerly known as American Express Financial Corporation and changed its name to Ameriprise Financial, Inc. in September 2005. Ameriprise Financial, Inc. was founded in 1894 and is based in Minneapolis, Minnesota.

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

Context

Gold, the yellow shiny metal, has been the fancy of mankind since ages. From making jewelry to being used as an investment, gold covers a huge spectrum of use cases. Gold, like other metals, is also traded on the commodities indexes across the world. For better understanding time series in a real-world scenario, we will work with gold prices collected historically and predict its future value.

Content

Metals such as gold have been traded for years across the world. Prices of gold are determined and used for trading the metal on commodity exchanges on a daily basis using a variety of factors. Using this daily price-level information only, our task is to predict future price of gold.

Data

For the purpose of implementing time series forecasting technique , i will utilize gold pricing from Quandl. Quandl is a platform for financial, economic, and alternative datasets. To access publicly shared datasets on Quandl, we can use the pandas-datareader library as well as quandl (library from Quandl itself). The following snippet shows a quick one-liner to get your hands on gold pricing information since 1970s.

import quandl gold_df = quandl.get("BUNDESBANK/BBK01_WT5511")

The time series is univariate with date and time feature

Starter Kernel(s)

-Start with Fundamentals: TSA & Box-Jenkins Methods

This notebook is an overview of TSA and traditional methods

Acknowledgements

For this dataset and tasks, i will depend upon Quandl. The premier source for financial, economic, and alternative datasets, serving investment professionals. Quandl’s platform is used by over 400,000 people, including analysts from the world’s top hedge funds, asset managers and investment banks.

Inspiration

• Forecast gold price

*If you find the data useful your upvote is an explicit feedback for future works, Have fun exploring data!*

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Change-To-Inventory Time Series for Ameriprise Financial Inc. Ameriprise Financial, Inc., together with its subsidiaries, operates as a diversified financial services company in the United States and internationally. The company offers financial planning and advice services to individual and institutional clients. It operates through Advice & Wealth Management, Asset Management, and Retirement & Protection Solutions segments. The Advice & Wealth Management segment provides financial planning and advice; brokerage products and services for retail and institutional clients; discretionary and non-discretionary investment advisory accounts; mutual funds; insurance and annuities products; cash management and banking products; and face-amount certificates. The Asset Management segment offers investment management, advice, and products to retail, high net worth, and institutional clients through third-party financial institutions, advisor networks, direct retail, and its institutional sales force under the Columbia Threadneedle Investments brand name. Its products include U.S. mutual funds and their non-U.S. equivalents, exchange-traded funds, variable product funds underlying insurance, and annuity separate accounts; and institutional asset management products, such as traditional asset classes, separately managed accounts, individually managed accounts, collateralized loan obligations, hedge funds, collective funds, and property and infrastructure funds. The Retirement & Protection Solutions segment provides variable annuity products, as well as life and disability income insurance products to retail clients. The company was formerly known as American Express Financial Corporation and changed its name to Ameriprise Financial, Inc. in September 2005. Ameriprise Financial, Inc. was founded in 1894 and is based in Minneapolis, Minnesota.

Information about Barnet Council's Pension Alternative and Private Funds - names and vintage years of private equity, venture capital, mezzanine, distressed, real estate/REIT, debt, infrastructure and hedge funds/partnerships in the Fund's portfolio. The names of the funds are: IFM Global Infrastructure (UK) B, L.P (2017) Alcentra European Direct Lending Fund II (2016) IIFIG Secured Finance Fund (2017) Partners Group Private Market Credit Strategies – Multi-Asset Credit 2015 Partners Group Private Market Credit Strategies – Multi-Asset Credit 2017 Partners Group Multi Asset Credit V S.C.A., SICAV-RAIF (2019) CBRE Global Alpha Property Fund Aberdeen Standard Long Lease Property Fund Adams Street 2019 Global Fund LCIV Private Debt (2021) LCIV Renewables Infrastructure (2021) Disclosures here are made for all relevant funds except for the below, please see Exemptions Notice. Partners Group Private Markets Credit Strategies S.A. - Compartment Multi Asset Credit 2015 (II) GBP Partners Group Private Markets Credit Strategies 2 S.A. - Compartment Multi Asset Credit 2017 (IV) GBP Partners Group Multi Asset Credit V S.C.A., SICAV-RAIF Dataset includes commitments made to each partnership, contributions drawn down since inception, distributions made to the Fund to date by each partnership, net asset value, internal rates of return (IRRs) for each partnership with and without the use of credit facility, investment multiple (TV/PI) for each individual partnership, the dollar amount of 'total management fees and costs paid' for each individual partnership, date as of which all the above data was calculated, names of all alternative asset partnerships partially and fully sold by The London Borough of Barnet Pension Found. This Dataset also includes the Quarterly Investment Monitoring Report.

Denmark Foreign Direct Investment Income: Inward: Total: Jordan data was reported at 0.000 DKK mn in 2023. This stayed constant from the previous number of 0.000 DKK mn for 2022. Denmark Foreign Direct Investment Income: Inward: Total: Jordan data is updated yearly, averaging 0.000 DKK mn from Dec 2020 (Median) to 2023, with 4 observations. The data reached an all-time high of 0.000 DKK mn in 2023 and a record low of 0.000 DKK mn in 2023. Denmark Foreign Direct Investment Income: Inward: Total: Jordan data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Denmark – Table DK.OECD.FDI: Foreign Direct Investment Income: by Region and Country: OECD Member: Annual. Reverse investment:Reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) is treated as portfolio investment. Netting of reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series excluding resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value, Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market value, Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the immediate counterpart country. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Direct Influence/Indirect Control (DIIC) method. Debt between fellow enterprises are completely covered. Collective investment institutions are not covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the resident direct investment enterprise. Statistical unit:Enterprise and Local Enterprise Group combined. Respondents have the opportunity to choose between reporting for one enterprise only or reporting for several enterprises within the same group

Denmark Foreign Direct Investment Income: Inward: Total: Benin data was reported at 0.000 DKK mn in 2023. This stayed constant from the previous number of 0.000 DKK mn for 2022. Denmark Foreign Direct Investment Income: Inward: Total: Benin data is updated yearly, averaging 0.000 DKK mn from Dec 2020 (Median) to 2023, with 4 observations. The data reached an all-time high of 0.000 DKK mn in 2023 and a record low of 0.000 DKK mn in 2023. Denmark Foreign Direct Investment Income: Inward: Total: Benin data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Denmark – Table DK.OECD.FDI: Foreign Direct Investment Income: by Region and Country: OECD Member: Annual. Reverse investment:Reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) is treated as portfolio investment. Netting of reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series excluding resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value, Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market value, Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the immediate counterpart country. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Direct Influence/Indirect Control (DIIC) method. Debt between fellow enterprises are completely covered. Collective investment institutions are not covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the resident direct investment enterprise. Statistical unit:Enterprise and Local Enterprise Group combined. Respondents have the opportunity to choose between reporting for one enterprise only or reporting for several enterprises within the same group

Denmark Foreign Direct Investment Income: Inward: USD: Total: Greenland data was reported at -11.177 USD mn in 2023. This records a decrease from the previous number of -2.226 USD mn for 2022. Denmark Foreign Direct Investment Income: Inward: USD: Total: Greenland data is updated yearly, averaging -6.701 USD mn from Dec 2022 (Median) to 2023, with 2 observations. The data reached an all-time high of -2.226 USD mn in 2022 and a record low of -11.177 USD mn in 2023. Denmark Foreign Direct Investment Income: Inward: USD: Total: Greenland data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Denmark – Table DK.OECD.FDI: Foreign Direct Investment Income: USD: by Region and Country: OECD Member: Annual. Reverse investment:Reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) is treated as portfolio investment. Netting of reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series excluding resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value, Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market value, Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the immediate counterpart country. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Direct Influence/Indirect Control (DIIC) method. Debt between fellow enterprises are completely covered. Collective investment institutions are not covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the resident direct investment enterprise. Statistical unit:Enterprise and Local Enterprise Group combined. Respondents have the opportunity to choose between reporting for one enterprise only or reporting for several enterprises within the same group