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Authors: Michael Hagenau ; Michael Liebmann and Dirk Neumann

Affiliation: University of Freiburg, Germany

Keyword(s): Text mining, Machine learning, Financial news, Stock price effect.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Mining Text and Semi-Structured Data ; Soft Computing ; Symbolic Systems

Abstract: In this paper, we examine whether stock price effects can be automatically predicted analyzing unstructured textual information in financial news. Accordingly, we enhance existing text mining methods to evaluate the information content of financial news as an instrument for investment decisions. The main contribution of this paper is the usage of more expressive features to represent text through the employment of market feedback as part of our word selection process. In a comprehensive benchmarking, we show that a robust Feature Selection allows lifting classification accuracies significantly above previous approaches when combined with complex feature types. That is because our approach allows selecting only semantically relevant features and thus, reduces the problem of over-fitting when applying a machine learning approach. The methodology can be transferred to any other application area providing textual information and corresponding effect data.

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Paper citation in several formats:
Hagenau, M.; Liebmann, M. and Neumann, D. (2011). IMPACT OF FEATURE SELECTION AND FEATURE TYPES ON FINANCIAL STOCK PRICE PREDICTION. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2011) - KDIR; ISBN 978-989-8425-79-9; ISSN 2184-3228, SciTePress, pages 295-300. DOI: 10.5220/0003665603030308

@conference{kdir11,
author={Michael Hagenau. and Michael Liebmann. and Dirk Neumann.},
title={IMPACT OF FEATURE SELECTION AND FEATURE TYPES ON FINANCIAL STOCK PRICE PREDICTION},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2011) - KDIR},
year={2011},
pages={295-300},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003665603030308},
isbn={978-989-8425-79-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2011) - KDIR
TI - IMPACT OF FEATURE SELECTION AND FEATURE TYPES ON FINANCIAL STOCK PRICE PREDICTION
SN - 978-989-8425-79-9
IS - 2184-3228
AU - Hagenau, M.
AU - Liebmann, M.
AU - Neumann, D.
PY - 2011
SP - 295
EP - 300
DO - 10.5220/0003665603030308
PB - SciTePress

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