UBS Quant Answers
Platform of choice. Delivering solutions. Managing risk.
Managing risks
Managing risks
A powerful and broad platform which helps investors interpret and manage their exposures and risks.
Innovation
Innovation
Allows our clients to tap directly into the innovation that is being developed by the Quant team at UBS.
Delivering solutions
Delivering solutions
Delivers portfolio risk forecasting, portfolio style and fundamental analysis and several other data and analysis modules.
Expert opinions and discussions you want to hear
Speak to the QA team about how we can help you.
Our global series of Quant, Evidence Lab & HOLT Conferences brings together experts in technology, AI, finance, academia, and business to discuss the latest advancements and help you navigate today’s key trends.
Global Head of Quantitive Research, UBS
Our 2025 conferences
Our 2025 conferences
UBS Quant, Evidence Lab and HOLT Conference
London, 19-20 March
New York, 20-22 May
Singapore, 4 November
Hong Kong, 6 November
UBS Quant and Data Intelligence Conference
Zurich, 12 May
Paris, 13 May
Imperial College and UBS Hedge Fund Conference
London, 4 June
UMD and UBS Market Microstructure Conference
Washington, 18-20 September
CFM and Imperial College Microstructure Conference
London, 8-9 December
Explore some of the insights from our Quant, Evidence Lab and HOLT conference in London.
Explore some of the insights from our Quant, Evidence Lab and HOLT conference in London.
Tear sheets
Tear sheets
If you are looking for detail on each of our data sets and models, please access our tear sheets.
Whitepaper
Whitepaper
For a deeper dive into our approach in the UBS Hybrid Risk Model.
UBS Research
UBS Research
Discover the breadth, depth and originality from our award-winning economists, strategists and analysts.
UBS Evidence Lab
UBS Evidence Lab
UBS Evidence Lab is a sell-side team of experts that work across more than 55 specialized areas creating insight-ready datasets. The experts turn data into evidence by applying a combination of tools and techniques to harvest, cleanse, and connect billions of data items each month. The library of assets, covering more than 5,000 companies, across all sectors and regions, is designed to help answer the questions that matter to investment decisions.
See more from UBS Evidence LabSee more from UBS Evidence Lab
UBS Data Solutions
UBS Data Solutions
Our goal is to provide our institutional clients with unique data to help them optimize their business objectives from model input and alpha generation to risk management and operational support. Our dedicated global Data Solutions team is designed to ensure our clients are given the right tools and knowledge to build and maintain their data business in the most efficient manner possible.
Get in touch with our team
HOLT is a business of Credit Suisse AG and its affiliates which are UBS Group Companies. The HOLT methodology does not assign ratings or a target price to a security. HOLT is an analytical tool that involves use of publicly available, fact-based data and applies a set of proprietary quantitative algorithms and warranted value calculations against such data, collectively called the HOLT valuation model. The HOLT methodology is consistently applied to all the companies included in its database. The HOLT valuation model is a discounted cash flow model (DCF). The % upside/downside is the difference between HOLT’s default warranted price (as determined by an objective, systematic DCF framework) and a security’s most recent closing price, expressed as a percentage. Third-party data (including consensus earnings estimates) are systematically translated into a number of default variables and incorporated into the algorithms available in the HOLT valuation model. The source financial statement, pricing, and earnings data provided by outside data vendors are subject to quality control and may also be adjusted to more closely measure the underlying economics of firm performance. These adjustments provide consistency when analyzing a single company across time,or analyzing multiple companies across industries or national borders.
The default scenario that is produced by the HOLT valuation model establishes a warranted price that represents the expected mean value for a security based upon various factors, including the use of third-party data and empirically derived fade algorithms that forecast a firm’s future cash return on capital and growth rates over an extended period of time. A default set of algorithms apply to all the securities. As the data are updated, the warranted price updates automatically. A company’s future achieved return on capital or growth rate may differ from the result generated from the default scenario that is produced by the HOLT valuation model. Additional information about the HOLT methodology is available upon request.
