Publication
Riskit: investment risk assessment platform
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Monte, Ana Paula | |
| dc.contributor.advisor | Lopes, Rui Pedro | |
| dc.contributor.author | Pereira, Miguel de Lacerda | |
| dc.date.accessioned | 2024-01-22T15:51:31Z | |
| dc.date.available | 2024-01-22T15:51:31Z | |
| dc.date.issued | 2023 | |
| dc.description | Mestrado em IPB-ESTG e ASSOCIAÇÃO DE POLITÉCNICOS DO NORTE (APNOR): Instituto Politécnico do Cávado e do Ave, P. Porto, Instituto Politécnico de Viana do Castelo | pt_PT |
| dc.description.abstract | The current society is volatile, influenced by macro social, economic, geopolitical, and natural phenomena that have a global and deeply interconnected impact. As a result, as unpredictability increases, access to information and decision-support tools becomes increasingly vital in all aspects of social life. The capital market (and companies) is at the forefront of these phenomena, given its volatility and extreme exposure to these macro events. In this scenario, the objective was to develop a platform that predicts insolvencies. The Riskit: Insolvency Predictor is a web-based platform aimed at assisting the scientific community and investors in predicting the possibility of companies becoming insolvent based on specific financial indicators. Methodologically, a dataset of 15,000 Portuguese companies was randomly extracted from the Iberian Balance Sheet Analysis System (SABI) database1. An analysis was conducted, resulting in the selection of 11 financial indicators used for predictions. To make predictions, the authors conducted a comprehensive study of models commonly used for this type of forecasting and also experimented with some machine-learning models that are not frequently mentioned in the literature. The evaluation of the application’s performance in predicting insolvencies is measured by a series of performance benchmarks calculated with the help of a confusion matrix. It was found that models frequently mentioned in the literature do not always have better performance. The main objectives of this project were achieved, providing both the scientific community and investors with a tool that predicts insolvency using a set of financial indicators and demonstrating the value of machine-learning models for making these predictions. The application can be visited at https://riskit.ipb.pt/. | pt_PT |
| dc.identifier.tid | 203476182 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10198/29285 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | pt_PT |
| dc.subject | Insolvency prediction | pt_PT |
| dc.subject | Risk management | pt_PT |
| dc.subject | Financial indicators | pt_PT |
| dc.subject | Machine learning models | pt_PT |
| dc.subject | Web-based application | pt_PT |
| dc.title | Riskit: investment risk assessment platform | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Gestão das Organizações | pt_PT |