Publicação
Using Consumer Online Reviews to Predict Stock Price Returns in the Ridesharing Industry
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Henriques, Roberto André Pereira | |
| dc.contributor.author | Fernandes, Pedro Ricardo Félix | |
| dc.date.accessioned | 2025-02-19T11:57:51Z | |
| dc.date.available | 2025-02-19T11:57:51Z | |
| dc.date.issued | 2025-02-13 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science | pt_PT |
| dc.description.abstract | This research explores the predictive power of online consumer reviews on weekly stock price returns within the ridesharing industry, focusing on Uber and Grab, two companies with very distinct characteristics. The user-generated reviews were obtained from the Google Play Store and leveraged to determine how their features (such as sentiment, rating, and textual content) correlate with stock performance. This data is joined with Stock data from Yahoo Finance and aggregated daily and weekly to be inputted into machine learning models, specifically Recurrent Neural Networks with Long Short-Term Memory (RNN-LSTM) and XGBoost. After identifying the optimal time lag for the predictions, the results reveal high predictive capacity from some sets of features, mainly regarding the review content itself. These findings confirm the benefits of considering user feedback in financial forecasting by showing that including features such as the most common words and the rating given by users increases performance for stock price prediction, especially in terms of the price return direction (positive or negative). By demonstrating the value added in integrating qualitative online review data into quantitative stock prediction, this work contributes to financial analytics, offering relevant insights for investors and industry stakeholders to understand how their customers influence their economic outcomes. | pt_PT |
| dc.identifier.tid | 203921577 | |
| dc.identifier.uri | http://hdl.handle.net/10362/179320 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | User Generated Content | pt_PT |
| dc.subject | Stock Price Returns | pt_PT |
| dc.subject | Prediction | pt_PT |
| dc.subject | Deep Learning | pt_PT |
| dc.subject | Ridesharing | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.title | Using Consumer Online Reviews to Predict Stock Price Returns in the Ridesharing Industry | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Ciência de Dados e Métodos Analíticos Avançados, especialização em Ciência de Dados | pt_PT |