Master Thesis Online
Success and Failure Predictions of FinTech Startups
Hristodoulakis, Axel 
Promoteur(s) :
Hübner, Georges
Date de soutenance : 19-jui-2020/23-jui-2020 • URL permanente : http://hdl.handle.net/2268.2/8945
Détails
| Titre : | Success and Failure Predictions of FinTech Startups |
| Titre traduit : | [fr] Prédiction du succès et de l'échec de FinTech Startups |
| Auteur : | Hristodoulakis, Axel
|
| Date de soutenance : | 19-jui-2020/23-jui-2020 |
| Promoteur(s) : | Hübner, Georges
|
| Membre(s) du jury : | Heuchenne, Cédric
Lamest, Markus Esch, Louis
|
| Langue : | Anglais |
| Nombre de pages : | 119 |
| Mots-clés : | [en] FinTech [en] Startup [en] Failure Prediction [en] Logistic Regression [en] Lasso [en] SMOTE [en] ADASYN |
| Discipline(s) : | Sciences économiques & de gestion > Finance |
| Public cible : | Professionnels du domaine |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Diplôme : | Master en ingénieur de gestion, à finalité spécialisée en Financial Engineering |
| Faculté : | Mémoires de la HEC-Ecole de gestion de l'Université de Liège |
Résumé
[en] This project-thesis aims to find how to predict the future success or failure of a particular type of firms, that is to say the FinTech startups, based on information available at their establishment. To do so, the FinTech industry was inspected in details. Its evolution and last trends are depicted in the first part of the investigation. In addition, a state of the art of corporate failure prediction summarizing this practice from its premises in the 60s until today is displayed. To carry on this analysis, I decided to conduct a binary logistic regression on a sample of FinTech startups with the objective to separate failing and successful ones. Furthermore, I applied a feature selector, specifically the Least Absolute Shrinkage and Selection Operator (Tibshirani, 1996), on the analysis. Moreover, as the sample used is rather small and imbalanced, I decided to apply an over-sampling technique on it.
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Document(s)
S150963Hristodoulakis2020.pdf
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Annexe(s)
Datasetfinal.xlsx
Description: Données
Taille: 181.68 kB
Format: Microsoft Excel XML
EvaluationOfSuccessFinTechStartups.xlsx
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Taille: 11.45 kB
Format: Microsoft Excel XML
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