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Annals of Data Science
Erschienen in: Annals of Data Science 3/2021

07.05.2020

Predicting Indian Stock Market Using the Psycho-Linguistic Features of Financial News

verfasst von: B. Shravan Kumar, Vadlamani Ravi, Rishabh Miglani

Erschienen in: Annals of Data Science | Ausgabe 3/2021

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Abstract

Financial forecasting using news articles is an emerging field. In this paper, we proposed hybrid intelligent models for stock market prediction using the psycholinguistic variables (LIWC and TAALES) extracted from news articles as predictor variables. For prediction purpose, we employed various intelligent techniques such as Multilayer Perceptron, Group Method of Data Handling (GMDH), General Regression Neural Network (GRNN), Random Forest, Quantile Regression Random Forest, Classification and regression tree and Support Vector Regression. We experimented on the data of 12 companies’ stocks, which are listed in Bombay Stock Exchange. We employed Chi squared and maximum relevance and minimum redundancy feature selection techniques on the psycho-linguistic features obtained from the news articles etc. After extensive experimentation, using Diebold-Mariano test, we conclude that GMDH and GRNN are statistically the best techniques in that order with respect to the MAPE and NRMSE values.
Vorheriger Artikel A Clustering Algorithm Based on Document Embedding to Identify Clinical Note Templates
Nächster Artikel On Discrimination Between the Lindley and xgamma Distributions

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Metadaten
Titel
Predicting Indian Stock Market Using the Psycho-Linguistic Features of Financial News
verfasst von
B. Shravan Kumar
Vadlamani Ravi
Rishabh Miglani
Publikationsdatum
07.05.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Annals of Data Science / Ausgabe 3/2021
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-020-00272-2

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