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Authors: Giacomo Domeniconi ; Gianluca Moro ; Andrea Pagliarani and Roberto Pasolini

Affiliation: Università degli Studi di Bologna, Italy

ISBN: 978-989-758-271-4

Keyword(s): Stock Market Prediction, Dow Jones Trend, Text Mining, Noise Detection, Twitter.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Data Analytics ; Data Engineering ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Mining Text and Semi-Structured Data ; Soft Computing ; Symbolic Systems ; Web Mining

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. (More)

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Paper citation in several formats:
Domeniconi, G.; Moro, G.; Pagliarani, A. and Pasolini, R. (2017). Learning to Predict the Stock Market Dow Jones Index Detecting and Mining Relevant Tweets.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-271-4, pages 165-172. DOI: 10.5220/0006488201650172

@conference{kdir17,
author={Giacomo Domeniconi. and Gianluca Moro. and Andrea Pagliarani. and Roberto Pasolini.},
title={Learning to Predict the Stock Market Dow Jones Index Detecting and Mining Relevant Tweets},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2017},
pages={165-172},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006488201650172},
isbn={978-989-758-271-4},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Learning to Predict the Stock Market Dow Jones Index Detecting and Mining Relevant Tweets
SN - 978-989-758-271-4
AU - Domeniconi, G.
AU - Moro, G.
AU - Pagliarani, A.
AU - Pasolini, R.
PY - 2017
SP - 165
EP - 172
DO - 10.5220/0006488201650172

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