{"@context":{"aiiso":"http:\/\/purl.org\/vocab\/aiiso\/schema#","arm":"https:\/\/ld4p.github.io\/arm\/core\/ontology\/0.1\/","bf":"http:\/\/id.loc.gov\/ontologies\/bibframe\/","bib":"https:\/\/bibliotek-o.org\/","bibo":"http:\/\/purl.org\/ontology\/bibo\/","cito":"http:\/\/purl.org\/spar\/cito\/","classSchemes":"http:\/\/id.loc.gov\/vocabulary\/classSchemes","dbo":"http:\/\/dbpedia.org\/ontology\/","dce":"http:\/\/purl.org\/dc\/elements\/1.1\/","dcmitype":"http:\/\/dublincore.org\/documents\/2000\/07\/11\/dcmi-type-vocabulary\/#","dcterms":"http:\/\/purl.org\/dc\/terms\/","ebucore":"http:\/\/www.ebu.ch\/metadata\/ontologies\/ebucore\/ebucore","edm":"http:\/\/www.europeana.eu\/schemas\/edm\/","foaf":"http:\/\/xmlns.com\/foaf\/spec\/#","frap":"http:\/\/purl.org\/cerif\/frapo","identifiers":"http:\/\/id.loc.gov\/vocabulary\/identifiers","ids":"http:\/\/id.loc.gov\/vocabulary\/identifiers\/","opaque":"http:\/\/opaquenamespace.org\/","pcdm":"http:\/\/pcdm.org\/models#","phaidra":"https:\/\/phaidra.org\/ontology\/","rdam":"http:\/\/rdaregistry.info\/Elements\/m\/","rdau":"http:\/\/rdaregistry.info\/Elements\/u\/","rdax":"http:\/\/rdaregistry.info\/Elements\/x\/","rdf":"http:\/\/www.w3.org\/1999\/02\/22-rdf-syntax-ns#","rdfs":"https:\/\/www.w3.org\/TR\/rdf-schema\/","relators":"http:\/\/id.loc.gov\/vocabulary\/relators","role":{"@context":{"advisor":{"@container":"@list","@id":"http:\/\/id.loc.gov\/vocabulary\/relators\/advisor"},"aut":{"@container":"@list","@id":"http:\/\/id.loc.gov\/vocabulary\/relators\/aut"}}},"schema":"http:\/\/schema.org\/","skos":"http:\/\/www.w3.org\/2004\/02\/skos\/core#","skosxl":"http:\/\/www.w3.org\/2008\/05\/skos-xl"},"@id":"https:\/\/door.donau-uni.ac.at\/o:5729","bf:note":[{"@type":"bf:Note","skos:prefLabel":[{"@language":"eng","@value":"This study investigates the effi cacy of the Tabular Prior-informed Bayesian Neural Network (TabPFN) for predicting Merger and Acquisition (M&A) outcomes, positioning it as a transparent alternative to opaque \"black-box\" models within regulated fi nancial domains. The performance of TabPFN is benchmarked against fi ve established models: Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) on a curated dataset of 4,400 historical U.S. M&A transactions. The central evaluation assesses predictive discrimination (AUC-ROC) and probability calibration (Expected Calibration Error). Results demonstrate that TabPFN achieves superior discriminative performance (AUC-ROC: 0.9168) and strong calibration (ECE: 0.0676). Furthermore, SHAP-based explainability analysis of the TabPFN model reveals crucial non-linear feature eff ects, such as the interplay between deal attitude and completion time, that are not apparent in linear models. The fi ndings establish TabPFN as a robust and interpretable framework for M&A risk assessment, aligning with the principles of trustworthy AI for high-stakes fi nancial applications.\nThe primary aim was to assess diff erences in predictive discrimination (AUC-ROC), probability calibration (ECE), and explanatory insights. Empirical fi ndings revealed TabPFN achieved the highest discriminative performance (AUC-ROC: 0.9168), outperforming LR (0.7171) and other baselines. TabPFN also demonstrated strong probability calibration (ECE: 0.0676), signifi cantly better than LR (ECE: 0.1372), although RF exhibited the best ECE (0.0299). SHAP analysis of TabPFN identifi ed 'realized_return' as a key predictor for the tested instances and elucidated crucial non-linear feature eff ects not apparent from linear models; the initial feature pruning, guided by Random Forest, highlighted the general importance of factors including 'attitude_Friendly' and 'completion_time' for the prediction task.\nThis study concludes that TabPFN's superior discriminative performance and strong calibration, synergistically combined with XAI techniques, provide a more valuable and actionable tool for M&A strategic decision-making. The enhanced probabilistic reliability and transparent, granular feature-level explanations off er a robust foundation for risk assessment within the TISE framework, aligning with demands for trustworthy AI in high-stakes fi nancial applications"}]}],"dce:subject":[{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"Mergers and Acquisitions (M&A) Outcome Prediction"}]},{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"Explainable Artifi cial Intelligence (XAI)"}]},{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"Tabular Prior-informed Bayesian Neural Network (TabPFN)"}]},{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"Probability Calibration"}]},{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"SHapley Additive exPlanations (SHAP)"}]}],"dce:title":[{"@type":"bf:Title","bf:mainTitle":[{"@language":"eng","@value":"Beyond linear models: Explainable AI for merger & acquisition outcome prediction"}]}],"dcterms:created":["2025"],"dcterms:language":["eng"],"dcterms:type":[{"@type":"skos:Concept","skos:exactMatch":["https:\/\/pid.phaidra.org\/vocabulary\/69ZZ-2KGX"],"skos:prefLabel":[{"@language":"eng","@value":"Text"},{"@language":"deu","@value":"Text"},{"@language":"ita","@value":"Testo"}]}],"ebucore:filename":["Alexander Ryusandi Pratama_Master Thesis.pdf"],"ebucore:hasMimeType":["application\/pdf"],"edm:hasType":[{"@type":"skos:Concept","skos:exactMatch":["https:\/\/pid.phaidra.org\/vocabulary\/P2YP-BMND"],"skos:prefLabel":[{"@language":"eng","@value":"Master theses"},{"@language":"deu","@value":"Masterarbeit"},{"@language":"ita","@value":"Tesi di master"}]}],"edm:rights":["http:\/\/rightsstatements.org\/vocab\/InC\/1.0\/"],"role:advisor":[{"@type":"schema:Person","schema:affiliation":[{"@type":"schema:Organization","schema:name":[{"@value":"Poznań University of Economics and Business"}],"skos:exactMatch":["https:\/\/ror.org\/0532c1x92"]}],"schema:familyName":[{"@value":"Zaremba"}],"schema:givenName":[{"@value":"Adam"}],"skos:exactMatch":[{"@type":"ids:orcid","@value":"https:\/\/orcid.org\/0000-0001-5879-9431"}]},{"@type":"schema:Person","schema:affiliation":[{"@type":"schema:Organization","schema:name":[{"@value":"University College Dublin"}],"skos:exactMatch":["https:\/\/ror.org\/05m7pjf47"]}],"schema:familyName":[{"@value":"Dong"}],"schema:givenName":[{"@value":"Ruihai"}],"skos:exactMatch":[{"@type":"ids:orcid","@value":"https:\/\/orcid.org\/0000-0002-2509-1370"}]}],"role:aut":[{"@type":"schema:Person","schema:familyName":[{"@value":"Pratama"}],"schema:givenName":[{"@value":"Alexander Ryusandi"}],"skos:exactMatch":[{"@type":"ids:orcid","@value":"https:\/\/orcid.org\/0000-0003-4205-4288"}]}]}