Experts in Vega’s data science and statistics expert network apply empirical methods to complex datasets. The Vega team has vast experience managing large-scale datasets consisting of both structured and unstructured data. By developing rigorous, client-focused solutions, the Vega team helps our clients achieve superior results.

Statistical Analysis & Sampling

Vega develops and implements empirical analyses to answer our client’s economic and financial questions. We rely on flexible, robust, and defensible statistical techniques to address a wide range of issues including sampling, simulation, survival analysis, and other statistical techniques.

Big Data Management

Vega regularly works with large volumes of data. Our staff and in-house infrastructure have the capabilities to build, maintain, update, and most importantly, leverage extensive data to meet client needs. Our expertise enables us to conduct efficient and informative analytics. Our team is skilled at processing, normalizing, and enriching data. We also have experience maintaining datasets with billions of observations spanning many years. 

Web Scraping and Data Collection

Vega researches and collects large amounts of publicly available data. Drawing on our experience and creativity, we perform focused searches to identify and gather relevant and informative data unique to each engagement. Our team uses proprietary tools to scrape and compile custom datasets that address the needs of every client.

Text & Sentiment Analysis

Our team has experience analyzing and interpreting extracted text using sentiment analyses. We are able to build algorithms to classify millions of pages of documents and use proprietary tools to identify, categorize, index, and compile relevant documents based on content. In several engagements, Vega economists have also employed modern sentiment analysis.

Data Visualization

The Vega team creates intuitive, aesthetic, and compelling visualizations. Utilizing our programmatic dexterity, we make complex concepts accessible to all audiences. Our team is also able to streamline revisions and ensure consistency in visualizations by automating the generation process.

Artificial Intelligence (AI) & Machine Learning (ML)

Vega uses cutting edge AI and ML technology to guide data-driven analyses. By using complex state-of-the art data science methods, we are able to process and analyze large volumes of complex information while reducing subjectivity and error. Our approach allows us to automate complex tasks and customize analysis of large datasets.

Example Engagements

  • Health Insurance Pricing Models: Vega supported an expert who performed an analysis using terabytes of sensitive medical data to investigate and recreate health insurance pricing models.

  • Statistical Sampling Strategies in Billing Dispute: A Vega expert created potential sampling strategies for a case related to billing disputes, allowing extrapolation of chart review results from the sample to assess liability and damages issues.

  • Statistical Sampling in Technology Insurance Matter: The Vega team supported an expert in creating a statistically valid sample of claims to be audited in an arbitration regarding claim payments on extended service plans for mobile phones and other devices. After the audit was completed, the results were extrapolated to the overall population of claims. 

  • Loan-Level Risk Profile Analysis In RMBS Repurchase Actions: A Vega expert was retained on more than half dozen cases in which trustees alleged that defendant originator failed to cure or repurchase loans that allegedly failed to conform to the representations and warranties. Vega supported the expert's empirical analysis to determine whether or not the alleged loan-level defects, if true, would have resulted in a statistically significant impact on the value of the loan. In one of these cases, Morgan Stanley Mortgage Loan Trust 2006-14SL, et al. v. Morgan Stanley Mortgage Capital Holdings LLC (N.Y. Sup. No. 652761/2012), this analysis withstood a motion to exclude. 

  • Loss Causation Analysis: In National Credit Union Administration Board v. RBS Securities Inc., et al. (D. Kan. No. 11-cv-02340), a Vega expert was retained to analyze whether macroeconomic events were the key drivers of the unexpected defaults of loans underlying the RMBS. To do so, the expert, supported by the Vega team, performed a series of regressions as well as a sentiment analysis.

  • Production Data Management: Vega was the designated consulting firm to receive and verify data produced by a large group of insurance companies. Our team assisted counsel by bridging the gap between the data produced in discovery and the expert analysis, ensuring the data was sufficient for the expert analysis anticipated in subsequent phases of the litigation.

Experts

Daniel Bauer

Hickman-Larson Chair in Actuarial Science and Associate Professor of Risk and Insurance at Wisconsin School of Business, University of Wisconsin-Madison

  • Insurance & Risk
  • Data Science & Statistics
Rajeev Bhattacharya

Professor of Finance at R.H. Smith School of Business at the University of Maryland

  • Antitrust & Competition
  • Data Science & Statistics
  • Valuation
  • Securities & Finance
Mark J. Browne

Robert Clements Distinguished Chair in Risk Management and Insurance, Tobin College of Business at St. John's University

  • Insurance & Risk
  • Data Science & Statistics
Daphne Chen

Managing Director

  • Data Science & Statistics
Ethan Cohen-Cole

Senior Advisor at Vega Economics

  • Data Science & Statistics
  • Securities & Finance
  • Healthcare & Health Economics
  • Valuation
  • Consumer Finance
  • Financial Institutions
Frederico Finan

Associate Professor at the University of California, Berkeley

  • Data Science & Statistics
Paul Hanouna

Associate Professor, Department of Finance, Villanova School of Business, Villanova University

  • Real Estate
  • Data Science & Statistics
  • Financial Institutions
  • Corporate Finance
  • Securities & Finance
Richard Libby

Founding Director, Perihelion Capital Advisors

  • Data Science & Statistics
  • Corporate Finance
  • Financial Institutions
Xiaodong Liu

Associate Professor in the Department of Economics, University of Colorado, Boulder

  • Data Science & Statistics
Cecilia Mo

Assistant Professor of Political Science at University of California, Berkeley

  • Data Science & Statistics
Michael A. Sadler

Senior lecturer in the Department of Finance and the Department of Economics at the McCombs School of Business at University of Texas at Austin

  • Data Science & Statistics
  • Valuation
  • Energy & Environmental Economics
Shu Shen

Associate Professor in the Department of Economics, University of California, Davis

  • Data Science & Statistics
  • Labor & Employment
Wei Tan

Managing Director at Mingde Economic Research Inc. and an Adjunct Professor at Johns Hopkins University

  • Antitrust & Competition
  • Data Science & Statistics
  • Valuation
Jay Vadiveloo

Professor at the University of Connecticut and Director of the Janet & Mark L Goldenson Center for Actuarial Research at the University of Connecticut

  • Insurance & Risk
  • Data Science & Statistics