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...

PowerMap U.S. is an innovative trading solutions, specializing in order flow analytics on U.S. Stock market. With its AI-inferred proprietary algorithm trained on market data, TradePulse predicts stock flow on using trade volume and buy intensity providing an additional key metric for decision-making while providing catalogue of alternative dataset on its platform.

Key Features: 💠 AI-driven order flow prediction based on trade volume and buy-side intensity 💠 Proprietary algorithms trained on historical and real-time U.S. equity data 💠 Real-time analytics across major U.S. exchanges (NYSE, NASDAQ, etc.) 💠 Integrated dashboard with visual flow indicators and trend detection 💠 Access to alternative datasets curated for quantitative and discretionary strategies 💠 Customizable signals aligned with trading styles (momentum, mean-reversion, etc.) 💠 Scalable infrastructure suitable for institutional-grade workflows

Primary Use Cases: 🔹 U.S.-focused hedge funds leveraging inferred flow data for intraday alpha 🔹 Quantitative traders integrating buy-side pressure metrics into models 🔹 Execution teams identifying optimal entry/exit points through real-time flow signals 🔹 Asset managers enhancing conviction through AI-derived trade behavior insights 🔹 Research analysts and PMs utilizing alternative datasets for cross-validation of ideas

Contact us for a real time order flow data in different markets. Stay ahead with TradePulse's order flow insights.

Global foreign exchange (net - net), for options, total (all currencies), total (all currencies), total (all maturities), hedge funds and proprietary trading firms, All countries (total), All countries (total), total (all ratings), total (all sectors), total (all methods), turnover - notional amounts (daily average)

The Board, the Office of the Comptroller of the Currency (OCC), the Federal Deposit Insurance Corporation (FDIC), the Commodity Futures Trading Commission (CFTC), and the Securities and Exchange Commission (SEC) (collectively, the agencies) adopted a final rule that implemented section 13 of the Bank Holding Company Act of 1956 (BHC Act), which was added by section 619 of the Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank Act). Section 13 contains certain prohibitions and restrictions on the ability of a banking entity supervised by the agencies to engage in proprietary trading or to have certain interests in, or relationships with, a hedge fund or private equity fund. Section 248.20 and Appendix A of Regulation VV - Proprietary Trading and Certain Interests in and Relationships with Covered Funds require certain of the largest banking entities engaged in significant trading activities to collect, evaluate, and furnish data regarding covered trading activities as an indicator of areas meriting additional attention by the banking entity and the Board. The new FR VV-1 report must be filed by firms with 'significant' trading assets and liabilities beginning with the quarterly report for the first quarter of 2021, due April 30, 2020.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Replication files to ``Machine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha'' %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Each folder contains files to replicate one table or figure of the paper. The folders also contain a readme file to help users to run the codes. The replication_file.tex file generates a PDF file with the replicated tables and figures.

Notice that the replicated tables and figures are not always identical to those reported in the paper. The reason is that in order to protect the proprietary nature of the data, in some cases we have added noise to mutual fund characteristics and returns; see the README file for details.

The data files that are required to run the codes are stored in the /data_sets/ folder. Some tables and figures require the output from the codes stored in folders /code_for_ML_methods/ , /code_for_AW_method/ and /code_for_EW_method/. It is recommended to run the codes in those folders before running the codes that generate tables and figures.

We include a file in both tex and pdf formats containing the full set of results.

The data files represent the 26 estimated life-cycle-based indicators for a sample of companies and funds, obtained using the methodology described in the linked journal article. The files SD1 and SD2 contain the individual values estimated for the fund and company samples. These estimates are based on the methodology described in the linked article. The data herein is the source for producing all figures of the paper. All companies and funds have been anonymized, as the data is sourced from proprietary databases. At the same link, supplementary file SD3 contains the summary statistics and comparison of sustainable funds versus conventional funds sample. The file SD4 contains the data used to create Figure 4. The file SD5 contains sample data to create Figure 5. The file SD6 contains sample data to create Figure 6. Additional more detailed data can be provided upon reasonable request, but cannot be publicly disclosed as it contains data from licenced databases.

Global foreign exchange (net - gross), for total (all instruments), total (all currencies), total (all currencies), total (all maturities), hedge funds and proprietary trading firms, Denmark, All countries (total), total (all ratings), total (all sectors), total (all methods), turnover - notional amounts (daily average)

Global foreign exchange (net - net), for outright forwards, total (all currencies), total (all currencies), total (all maturities), hedge funds and proprietary trading firms, All countries (total), All countries (total), total (all ratings), total (all sectors), total (all methods), turnover - notional amounts (daily average)

The Algorithmic Trading Server market is experiencing robust growth, driven by the increasing adoption of algorithmic trading strategies by financial institutions and the need for high-performance computing solutions. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7.8 billion by 2033. This growth is fueled by several key factors. The rising demand for speed and efficiency in executing trades is a primary driver, as algorithmic trading requires extremely low latency and high throughput. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the sophistication of trading algorithms, necessitating more powerful and specialized servers to handle the increased computational complexity. Regulatory changes and increasing market volatility also contribute to the demand for reliable and robust algorithmic trading infrastructure. Competition is intense, with established players like HP Enterprise and Super Micro Computer competing against specialized firms such as ASA Computers and Blackcore Technologies. The market is segmented by server type (e.g., blade servers, rack servers), deployment model (on-premise, cloud), and end-user (hedge funds, investment banks, proprietary trading firms). Geographic growth is expected to be strong across North America and Europe, followed by Asia-Pacific. Despite its considerable growth potential, the market faces some challenges. High initial investment costs for sophisticated server infrastructure can be a barrier to entry for smaller firms. Additionally, the complexity of managing and maintaining these high-performance systems requires specialized expertise, increasing operational costs. Cybersecurity threats are also a significant concern, given the sensitive nature of financial data processed by algorithmic trading servers. However, these challenges are likely to be outweighed by the increasing benefits of algorithmic trading, leading to sustained market expansion throughout the forecast period. Continued innovation in server technology and software, coupled with the expanding adoption of cloud-based solutions, is poised to further accelerate market growth.

Estimate income and evaluate stocks and ETFs based on accurate two year forward dividend forecasts across 25k+ securities globally.

Use our forward dividend prediction data feed to obtain up-to-date information on the dates and payments of thousands of securities across 1000+ indices / 100+ countries.

Our single stock and ETF dividend forecast data elements include:

-Predicted ex, record and pay dates -Amount and currency -Dividend type / frequency -Unique Dividend Forecast Data Methodology

In these times of accelerating change our tech-driven approach gives us a powerful and disruptive edge over the older, more traditional forecasting methodologies Working from EDI’s global corporate actions database (dating back to 2012) our dividend projections are based on a combination of stated dividend policies and predictable patterns. The estimate data is generated by a dedicated London team working with our proprietary algorithm and enhanced with manual analyst input where required. This algorithm+analyst approach gives estimates with both huge scale and strong accuracy – our forward-looking data runs two full fiscal years ahead for well over 25,000 securities including equity, ADR and ETF future projections.

Our Woodseer single stock forecast data-set went live in January 2017, and the ETF dividend forecast product launched in July 2019 with detailed forward projections (dates and amounts) for over 1400 ETFs including 700+ US-listed. Working closely with a specialist ETF data provider we combine their compositional ETF data with our own underlying security estimates to produce accurate ‘bottom-up’ forecasts.

Clients include asset managers and custodians, index providers, options market makers, hedge funds, single stock and index traders.