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New Approach to Feature Generation by Complex-Valued Econometrics and Sentiment Analysis for Stock-Market Prediction

  • Dmitry BaryevEmail author
  • Igor Konovalov
  • Nikita Voinov
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 95)

Abstract

The theory of complex-valued econometrics makes it possible to generate qualitatively new features that can be used in machine learning algorithms. Our study reveals the task of determining the long-term dependence of future companies’ stock prices from a time-generated feature, i.e., a calculated tonality coefficient gained by methods of semantic analysis of texts from social networks. Data was gathered from the Twitter platform with the use of Big Data ETL-scenarios. The resulting data sets were used to train machine learning algorithms designed to work with Big Data technologies. A semantic coefficient was calculated on the basis of aggregated estimates for each day, with the further application of the methods of complex-valued econometrics. To demonstrate the new approach of feature generation, a complex-valued linear regression model based on the semantic coefficients and stock markets data was constructed. The outcome obtained by the new approach was compared with existing solutions in terms of accuracy. Finally, we demonstrate a possible route for impacting improvements of the existing algorithms for trading strategies using the complex-valued regression.

Keywords

Machine learning Sentiment analysis Complex-valued modeling NLP Big Data ETL Spark Python PySpark Mongo DB Twitter Stock markets 

Notes

Acknowledgements

The study was supported by the Russian Foundation for Basic Research, Grant No. 19-010-00610\19 “Theory, Methods and Techniques for Forecasting Economic Development by Autoregressive Models of Complex Variables.”

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Peter the Great St. Petersburg Polytechnic UniversitySt. PetersburgRussia

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