# name
A web scraping framework for stock price modelling using deep learning methods

# metadata.dc.description[].value
Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2018-2019, Tutor: Salvador Torra Porras

# metadata.dc.description.abstract[].value
(eng) This work aims to shed light to the process of webs craping,emphasizing its im-
portance in th enew ’BigData’ era with an illustrative application of such methods
in financial markets. The work essentially focuses on differents craping methodolo-
gies that can be used to obtain large quantities of heterogenous data in realtime.
Automatization of data extraction systems is one of the main objectives pursuedin
this work, immediately followed by the development of a framework for predic-
tive modelling. Applying neural networks and deep learning methods to the data
obtained through webscraping. The goal pursued is toprovide the reader with
some remarkable notes on how these models work while allowing room for further
research and improvements on the models presented.

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Estadística UB-UPC

# metadata.dc.title[].value
A web scraping framework for stock price modelling using deep learning methods