Swiss Small and Midcap "A machine learning approach for evaluating short..."

Research evidence excerpt

Swiss Small and Midcap "A machine learning approach for evaluating short..."

Global Research

24 June 2026ab

Swiss Small and Midcap Equities

SwitzerlandA machine learning approach for evaluating

short-term growth potential - a primer Industrial

Sebastian Vogel, CFA

Analyst

sebastian.vogel@ubs.com

Developing an empirical framework to assess growth prospects +41-44 239 90 45

We find that most analysts usually place significant emphasis on medium-term earnings Tommaso Aquilante

forecasts, valuation metrics, and broader macroeconomic developments, when Strategist

assessing the companies they cover. Yet short-term growth is often also the starting tommaso.aquilante@ubs.com

point to assess such earnings prospects. Hence, we worked together with UBS' Empirical +44-20-7567 1205

Scientific Approaches (ESA) team to develop an empirical framework that helps to Jamie Dorricott

validate short-term focussed organic growth prospects for companies for which this Strategist

approach is appropriate. Thus, on the back of high-frequency indicators and alternative jamie.dorricott@ubs.com

data sources, the framework provides an additional lens for assessing such short-term +44-20-7567 0790

growth trends and helps to identify potential deviations from consensus expectations, in Cristian Nedelcu, CFA

our view. While Kuehne+Nagel is used as an initial case study throughout this white Analyst

paper, our framework is designed to be scalable and applicable across a broader range cristian.nedelcu@ubs.com

of companies across the Swiss Small and Mid Cap universe. +44-20-7568 4375

Details on the methodology

Our approach uses time-series and panel-data econometric techniques to estimate and

validate how macroeconomic conditions influence firm-level growth prospects. The