Financial Forecasts Based on Analysis of Textual News Sources: Some Empirical Evidence

A. Barazzetti (), F. Cecconi () and R. Mastronardi ()
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A. Barazzetti: QBT Sagl
F. Cecconi: LABSS-ISTC-CNR
R. Mastronardi: QBT Sagl

A chapter in Artificial Economics and Self Organization, 2014, pp 133-145 from Springer

Abstract: Abstract The explosive growth of online news and the need to find the right news article quickly and efficiently cause people to adapt on events happening. The readers task is to filter out the desired information from headlines and teasers by scanning various sources formats (text, broadcasting transmission and video storage) of news articles. What people need are entities, relationships and events, which can be extracted from text by using event extraction techniques. Considering the granularity of event extraction, we present a novel approach that extracts correlation between news with a human/machine interaction. Our scope is to answer this question more efficiently: “How might stocks (e.g. Eni) react if a news is created and launched again across web news network?”. This research examines a predictive machine learning approach for financial news articles analysis using a News Index Map (NIM) based on a web news decision support system for event forecasting and trading decision. Empirical evaluation on real online news data sets firstly show that only a small number of news ends up having a real impact on the security and secondly, human coding is able to extract knowledge from large amounts of data to build predictive models to provide investment decision suggestions.

Keywords: Stock Price; Natural Language Processing; Stock Prex; News Article; Sentiment Analysis (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-319-00912-4_11

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DOI: 10.1007/978-3-319-00912-4_11

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