This paper investigates if and to what point it is possible to trade on news sentiment and if Deep Learning (DL), given the current hype on the topic, would be a good tool to do so. DL is built explicitly for dealing with significant amounts of data and perform complex tasks where automatic learning is a necessity. Thanks to its promise to detect complex patterns in a dataset, it may be appealing to those investors that are looking to improve the trading process. Moreover, DL and speciftcally LSTM seem a good pick from a linguistic perspective too, given its ability to "remember" previous words in a sentence. After having explained how DL models are built, we will use this tool for forecasting the market sentiment by using news headlines. The prediction is based on the Dow Jones Industrial Average (DJIA) by analyzing 25 daily news headlines available between 2008 and 2016, which will then be extended up to 2020. The result will be the indicator used for developing an algorithmic trading strategy. The analysis will be performed on two speciftc cases that will be pursued over ftve time- steps and the testing will be developed in real-world scenarios.

Mattia Vicari, Mauro Gaspari (2021). Analysis of news sentiments using Natural Language Processing and Deep Learning. AI & SOCIETY, 36(3), 931-937 [10.1007/s00146-020-01111-x].

Analysis of news sentiments using Natural Language Processing and Deep Learning

Mattia Vicari
;
Mauro Gaspari
2021

Abstract

This paper investigates if and to what point it is possible to trade on news sentiment and if Deep Learning (DL), given the current hype on the topic, would be a good tool to do so. DL is built explicitly for dealing with significant amounts of data and perform complex tasks where automatic learning is a necessity. Thanks to its promise to detect complex patterns in a dataset, it may be appealing to those investors that are looking to improve the trading process. Moreover, DL and speciftcally LSTM seem a good pick from a linguistic perspective too, given its ability to "remember" previous words in a sentence. After having explained how DL models are built, we will use this tool for forecasting the market sentiment by using news headlines. The prediction is based on the Dow Jones Industrial Average (DJIA) by analyzing 25 daily news headlines available between 2008 and 2016, which will then be extended up to 2020. The result will be the indicator used for developing an algorithmic trading strategy. The analysis will be performed on two speciftc cases that will be pursued over ftve time- steps and the testing will be developed in real-world scenarios.
2021
Mattia Vicari, Mauro Gaspari (2021). Analysis of news sentiments using Natural Language Processing and Deep Learning. AI & SOCIETY, 36(3), 931-937 [10.1007/s00146-020-01111-x].
Mattia Vicari; Mauro Gaspari
File in questo prodotto:
File Dimensione Formato  
AIsociety.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per accesso libero gratuito
Dimensione 683.56 kB
Formato Adobe PDF
683.56 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/778448
Citazioni
  • ???jsp.display-item.citation.pmc??? 2
  • Scopus 26
  • ???jsp.display-item.citation.isi??? 17
social impact