Synergy signal engine
Directional screening signal, not a forecast, valuation, or guarantee of realized synergy.
What the model actually shows.
Out-of-sample metrics from the extension-suite handoff CSVs. R² values sit on different target scales and are never read as cross-target improvements.
The owner-company overlay.
A theory-backed decision-support overlay that re-weights the listed-firm channel emphasis for private-company deal features. It is not trained on SME outcomes.
Built at the intersection of M&A, machine learning, and decision support.
A public portfolio artifact showing how thesis research can become an interactive, bounded analytical product.
Yanick Annema
I hold an MSc in Financial Engineering & Management from the University of Twente and work in M&A and Corporate Finance at Moore MKW. My work sits where applied machine learning meets finance: interpretable models, honest validation, and tools that make quantitative evidence usable in real advisory conversations.
This dashboard is based on my MSc thesis on whether pre-deal information can predict post-merger operating performance. It turns the research into a compact screening studio for exploring candidate deals, model evidence, attribution, and SME transfer limits.
A screening workbench that ranks M&A candidates with a transparent operating-performance rule, shown alongside separate out-of-sample evidence from a model learned on listed-firm deals, with a feature-computability gate for SME transfer.
Acquiror and target fundamentals you already hold early-stage (revenue, EBIT, assets, balance-sheet items), plus deal context.
Target-family indicators with transparent contribution, out-of-sample model evidence per target, and an SME transfer overlay.
Not a valuation, not a synergy guarantee, not a forecast value, and not an empirically validated SME prediction model.
Headline: asset turnover and EBIT/assets are computable for revenue-reporting SMEs; CFROA is proxy-only; retained earnings, cash, CAPEX and all deal-level controls are unavailable in ordinary ORBIS accounts.
- Ranking deal candidates by a transparent operating-performance screening rule.
- Asset-turnover change is the most learnable target in the family.
- Reporting each target's out-of-sample model evidence (R², ρ, n).
- Explaining model attribution via SHAP on listed-firm test data.
- Mapping which SME features are computable from ORBIS.
- A forecast value, valuation, or guaranteed realized synergy.
- Causal attribution: SHAP and contribution bars are explanations, not mechanisms.
- Cross-target R² improvement claims (target scales differ).
- Empirically validated SME prediction.
- Synergy creation from asset-turnover drift (it partly reflects asset-productivity reversion).