Swiss Small and Midcap: A machine learning approach for evaluating short-term growth potential - a primer

Research evidence excerpt

Swiss Small and Midcap: A machine learning approach for evaluating short-term growth potential - a primer

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