Enhancing Portfolio Management with Hedge Fund Grade Equity Risk Models
RiskModels offers sophisticated U.S. equity risk models tailored for traders, hedge funds, mutual funds, registered investment advisors, and other institutional asset managers to optimize their portfolio management strategies. Built on high-quality FactSet data, our models incorporate adjustments for dividends, stock splits, and other corporate events such as spinoffs, institutional grade risk metrics.
U.S. Equity Exposure and Factor Attribution Data
Our U.S. equity risk models provide comprehensive exposure and factor attribution data, enabling you to identify, measure, and mitigate risks effectively. Here's an overview of our approach:
Risk Factor Attribution
To assess each stock's sensitivity to movements in underlying factors—commonly referred to as beta—we run our proprietary regressions that evaluate exposures to market and other risk factors.
Variance Decomposition
Using factor exposures, we decompose risk along the following dimensions: 1. Market Risk - The portion of risk explained by the overall stock market. 2. Sector Risk - The portion of risk driven by sector-specific events. 3. Stock-Specific Risk - Unique risks not accounted for by market or sector movements.
Forecast Horizon Control
Customize your forecast horizon by extending training windows for longer-term predictions, offering more accurate risk projections over time.
Universe Packages
RiskModels provides the essential data for your portfolio optimization and performance reporting needs. Begin with a free trial of our Testing package or select from one of the packages below, customized to fit the size of your U.S. equity universe:
Sandbox
- NVDA, AMZN, GOOG, META, NFLX, JPM, XOM, JNJ, PG, UNH
- 1 year history
- Trial Access
Beta 500
- Top 500 U.S. equities
- 2 year history
- Market & Sector Betas & Factor Returns
Simulation 1500
- Top 1500 U.S equities
- 10 year history
- Betas, Factor Returns, Residual Returns, Universe Mask
- Simulation capabilities
- Nightly bucket delivery
Simulation 3000
- Top 3000 U.S. equities
- 15 year history
- Betas, Factor Returns, Residual Returns, Universe Mask
- Full Simulation capabilities
- Nightly bucket delivery
RiskModels is an offering of Blue Water Macro Corp, a data-driven quant firm founded in 2021. RiskModels empowers finance professionals from traders to institutional asset managers with precise, actionable insights to optimize their portfolio management processes.
We are driven is to make risk modeling more accessible and actionable for investment professionals, empowering them with the tools to make knowledgeable portfolio decisions.
EXPERIENCED LEADERSHIP IN RISK MANAGEMENT
Conrad Gann is a seasoned financial executive with extensive experience in investment management and fintech. As the founder of RiskModels and Blue Water Macro, LLC, Conrad brings over 30 years of industry expertise to help investors make more informed decisions. His background includes leadership roles across asset management firms where he developed innovative approaches to risk modeling and portfolio construction.
Drew Tilley is a quantitative researcher with expertise in machine learning, statistical analysis, and complex systems modeling. He applies data-driven methods to solve challenging problems across finance and analytics. His recent work focused on enhancing the Dollar-Cost Averaging strategy by developing a dynamic model that adjusts portfolio allocations based on market conditions. He is currently expanding into financial risk modeling, applying stochastic processes and portfolio optimization techniques.
Richard is a Data Software Engineer with over 11 years of industry experience, including a background in Mechanical Engineering where he contributed to designing helicopter hardware for NASA. Driven by a passion for investment and finance, he transitioned into data science and software engineering to combine analytical and technical expertise, delivering innovative and unique solutions.
Farid Babayev is a quantitative researcher and CFA charterholder with expertise in machine learning, natural language processing, and credit risk modeling. He is currently pursuing a Ph.D. in Computer Science at the University of Auckland and a Master’s in Financial Engineering at NYU. Farid has led impactful work across financial institutions, from building credit risk frameworks and automating reporting systems to developing sentiment-driven market-risk models using LLMs. With a strong foundation in data science and finance, he brings a research-driven, technical approach to solving complex problems in risk management and quantitative analysis.
Unlock institutional-grade equity risk models built for modern portfolio management.
Gain insight into market, sector, and stock-specific risk using advanced factor attribution and customizable forecast horizons.