Stock market analysis is a primary interest for finance and such a challenging task that has always attracted many researchers. Historically, this task was accomplished by means of trend analysis, but in the last years text mining is emerging as a promising way to predict the stock price movements. Indeed, previous works showed not only a strong correlation between financial news and their impacts to the movements of stock prices, but also that the analysis of social network posts can help to predict them. These latest methods are mainly based on complex techniques to extract the semantic content and/or the sentiment of the social network posts. Differently, in this paper we describe a method to predict the Dow Jones Industrial Average (DJIA) price movements based on simpler mining techniques and text similarity measures, in order to detect and characterise relevant tweets that lead to increments and decrements of DJIA. Considering the high level of noise in the social network data, w e also introduce a noise detection method based on a two steps classification. We tested our method on 10 millions twitter posts spanning one year, achieving an accuracy of 88.9% in the Dow Jones daily prediction, that is, to the best our knowledge, the best result in the literature approaches based on social networks.

Learning to Predict the Stock Market Dow Jones Index Detecting and Mining Relevant Tweets / Giacomo Domeniconi; Gianluca Moro; Andrea Pagliarani; Roberto Pasolini. - ELETTRONICO. - 1:(2017), pp. 165-172. (Intervento presentato al convegno 9th International Conference on Knowledge Discovery and Information Retrieval tenutosi a Funchal, Madeira, Portugal nel 1-3 novembre 2017) [10.5220/0006488201650172].

Learning to Predict the Stock Market Dow Jones Index Detecting and Mining Relevant Tweets

Giacomo Domeniconi;Gianluca Moro;Andrea Pagliarani;Roberto Pasolini
2017

Abstract

Stock market analysis is a primary interest for finance and such a challenging task that has always attracted many researchers. Historically, this task was accomplished by means of trend analysis, but in the last years text mining is emerging as a promising way to predict the stock price movements. Indeed, previous works showed not only a strong correlation between financial news and their impacts to the movements of stock prices, but also that the analysis of social network posts can help to predict them. These latest methods are mainly based on complex techniques to extract the semantic content and/or the sentiment of the social network posts. Differently, in this paper we describe a method to predict the Dow Jones Industrial Average (DJIA) price movements based on simpler mining techniques and text similarity measures, in order to detect and characterise relevant tweets that lead to increments and decrements of DJIA. Considering the high level of noise in the social network data, w e also introduce a noise detection method based on a two steps classification. We tested our method on 10 millions twitter posts spanning one year, achieving an accuracy of 88.9% in the Dow Jones daily prediction, that is, to the best our knowledge, the best result in the literature approaches based on social networks.
2017
Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - (Volume 1)
165
172
Learning to Predict the Stock Market Dow Jones Index Detecting and Mining Relevant Tweets / Giacomo Domeniconi; Gianluca Moro; Andrea Pagliarani; Roberto Pasolini. - ELETTRONICO. - 1:(2017), pp. 165-172. (Intervento presentato al convegno 9th International Conference on Knowledge Discovery and Information Retrieval tenutosi a Funchal, Madeira, Portugal nel 1-3 novembre 2017) [10.5220/0006488201650172].
Giacomo Domeniconi; Gianluca Moro; Andrea Pagliarani; Roberto Pasolini
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/611392
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