Learning Interval-Valued Fuzzy Cognitive Maps with PSO Algorithm for Abnormal Stock Return Prediction

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F17%3A39911545" target="_blank" >RIV/00216275:25410/17:39911545 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-319-71069-3_9" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-71069-3_9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-71069-3_9" target="_blank" >10.1007/978-3-319-71069-3_9</a>

Result language
angličtina
Original language name
Learning Interval-Valued Fuzzy Cognitive Maps with PSO Algorithm for Abnormal Stock Return Prediction
Original language description
Stock return prediction is considered a challenging task in financial domain. The existence of inherent noise and volatility in daily stock price returns requires a highly complex prediction system. Generalizations of fuzzy systems have shown promising results for this task owing to their ability to handle strong uncertainty in dynamic financial markets. Moreover, financial variables are usually in difficult to interpret causal relationships. To overcome these problems, here we propose an interval-valued fuzzy cognitive map with PSO algorithm learning. This system is suitable for modelling complex nonlinear problems through causal reasoning. As the inputs of the system, we combine causally connected financial indicators and linguistic variables extracted from management discussion in annual reports. Here we show that the proposed method is effective for predicting abnormal stock return. In addition, we demonstrate that this method outperforms fuzzy cognitive maps and adaptive neuro-fuzzy rule-based systems with PSO learning.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GA16-19590S" target="_blank" >GA16-19590S: Topic and sentiment analysis of multiple textual sources for corporate financial decision-making</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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