Proceedings of International Conference on Applied Innovation in IT
2018/03/13, Volume 6, Issue 1, pp.11-16

The Use of News Reports to Predict the Values of Macroeconomic Indicators and Indices Represented by Time Series


Artur Mikhailov, Natalia Gergel


Abstract: The use of forecasts and predictive models highly affects the process of making decisions. The use of given forecasts allows to increase economic effectiveness of individual entities as well as the corporations. The aim of the article is the investigation of the influence of the weakly formalized factors on the forecasts' accuracy. The study is based on the problem of classification for determining the trends of changing the indicators and the levels of external factors’ influences on a change of the referencing parameter. The dataset which contains 25 daily news headings gathered during 8 years was used to make the calculations. The chosen news headlines are related to the stock market and were published by the most authoritative sources such as: Russia Today, Reuters, Scientific American, The Guardian. It was demonstrated that the record of the influence of the information in the news reports on the change of the referencing parameter (using the example of the NASDAQ index) allows clarifying the forecasts taken with the use of functional methods. Therefore, it leads to minimizing mistakes and maximizing the forecasts’ reliability.

Keywords: Forecast, Prediction, Model, Text-mining, Machine Learning, Classification, Time Series

DOI: 10.13142/kt10006.12

Download: PDF

References:

  1. A. Khadjeh Nassirtoussi, S. Aghabozorgi, T. Ying Wah, and D. C. L. Ngo, “Text mining for market prediction: A systematic review,” Expert Syst. Appl., vol. 41, no. 16, pp. 7653–7670, Nov. 2014.
  2. F. Jin, N. Self, P. Saraf, P. Butler, W. Wang, and N. Ramakrishnan, “Forex-foreteller: currency trend modeling using news articles,” 2013, p. 1470.
  3. M. Butler and V. Kešelj, “Financial Forecasting Using Character N-Gram Analysis and Readability Scores of Annual Reports,” in Advances in Artificial Intelligence, vol. 5549, Y. Gao and N. Japkowicz, Springer Berlin Heidelberg, 2009, pp. 39–51.
  4. Y. Zhai, A. Hsu, and S. K. Halgamuge, “Combining News and Technical Indicators in Daily Stock Price Trends Prediction,” in Advances in Neural Networks – ISNN 2007, vol. 4493, D. Liu, S. Fei, Z. Hou, H. Zhang, and C. Sun, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 1087–1096.
  5. M.-A. Mittermayer, “Forecasting Intraday stock price trends with text mining techniques,” 2004, p. 10 pp.
  6. R. P. Schumaker and H. Chen, “Textual analysis of stock market prediction using breaking financial news: The AZFin text system,” ACM Trans. Inf. Syst., vol. 27, no. 2, pp. 1–19, Feb. 2009.
  7. X. Zhou and Australasian Database Conference, Eds., Database technologies 2002: proceedings of the Thirteenth Australasian Database Conference ; Monash University, Melbourne, January/February 2002. Sydney: Australian Computer Society, 2002.
  8. T. Joachims, “Text categorization with Support Vector Machines: Learning with many relevant features,” in Machine Learning: ECML-98, vol. 1398, C. Nédellec and C. Rouveirol, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998, pp. 137–142.
  9. S. Henrard, N. Speybroeck, and C. Hermans, “Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia,” Haemophilia, vol. 21, no. 6, pp. 715–722, Nov. 2015.
  10. G. M. Di Nunzio, “Using scatterplots to understand and improve probabilistic models for text categorization and retrieval,” Int. J. Approx. Reason., vol. 50, no. 7, pp. 945–956, Jul. 2009.
  11. T. M. Mitchell, Machine Learning. New York: McGraw-Hill, 1997.
  12. Association for Computing Machinery, W. B. Croft, International Conference on Research and Development in Information Retrieval, and Trinity College Dublin, Eds., SIGIR ’94: proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, 3 - 6 July 1994, Dublin, Ireland. London: Springer, 1994.
  13. L. Mylnikov, B. Krause, M. Kuetz, K. Bade, and I. Shmidt, Intelligent data analysis in the management of production systems (approaches and methods). Moscow: BIBLIO-GLOBUS, 2017.
  14. L. A. Mylnikov, A. V. Seledkova, and B. Krause, “Forecasting characteristics of time series to support managerial decision making process in production-And-economic systems,” Proc. 2017 20th IEEE Int. Conf. Soft Comput. Meas. SCM 2017 6 July 2017, pp. 853–855.


    HOME

       - Call for Papers
       - For authors
       - Important Dates
       - Conference Schedule
       - Conference Committee
       - Editorial Board
       - Reviewers
       - Last Proceedings


    PROCEEDINGS

       - Volume 11, Issue 2 (ICAIIT 2023)
       - Volume 11, Issue 1 (ICAIIT 2023)
       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


    PAST CONFERENCES

       ICAIIT 2023
         - Photos
         - Reports

       ICAIIT 2022
         - Message

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports

    ETHICS IN PUBLICATIONS

    ACCOMODATION

    CONTACT US

 

DOI: http://dx.doi.org/10.25673/112984


        

         Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0


                                                   This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License


           ISSN 2199-8876
           Publisher: Anhalt University of Applied Sciences

        site traffic counter

Creative Commons License
Except where otherwise noted, all works and proceedings on this site is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.