Kuehne+Nagel: Applying a machine learning approach for evaluating short-term growth potential

研报英文原文证据摘录

Kuehne+Nagel: Applying a machine learning approach for evaluating short-term growth potential

eration in shipping volumes. Still, we back in some

12/27E 8.80 9.01 2 8.68

sequential demand improvements for 2H26E and thus modestly increase our short-term 12/28E 9.79 10.02 2 9.41

revenue and earnings expectations.

Sebastian Vogel, CFA

Details about our quantitative methodology Analyst

sebastian.vogel@ubs.com

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

+41-44 239 90 45

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

macroeconomic environment is modelled using domain expertise, while the link to Tommaso Aquilante

Strategistcompany growth is rigorously tested through cross-validation and backtesting. We

tommaso.aquilante@ubs.com

employ a LASSO (Least Absolute Shrinkage and Selection Operator) model to identify +44-20-7567 1205

the most relevant predictors and enhance forecast accuracy. Macroeconomic variables

are either obtained from external sources or simulated for the forecast period. The Jamie Dorricott

Strategist

outcome is a set of empirically grounded organic growth forecasts that serve as a

jamie.dorricott@ubs.com

benchmark for evaluating short-term company performance. +44-20-7567 0790

Valuation: DCF-derived PT of CHF179/share

We reflect our revised forecasts (FY26-30E) in our DCF valuation for K+N. We maintain

our terminal sales growth rate/WACC of 2.5%/7.3%, respectively. In combination, this

results in a new DCF-derived PT for K+N of CHF179 (previously CHF177).

Highlights (CHFm) 12/23 12/24 12/25 12/26E 12/27E 12/28E 12/29E 12/30E

Revenues 26,649 27,356 28,118 28,203 29,010 30,027 31,151 32,379

EBIT (UBS) 1,861 1,676 1,380 1,375 1,513 1,676 1,809 1,854