IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1705.08545.html
   My bibliography  Save this paper

Financial Time Series Forecasting: Semantic Analysis Of Economic News

Author

Listed:
  • Kateryna Kononova
  • Anton Dek

Abstract

The paper proposes a method of financial time series forecasting taking into account the semantics of news. For the semantic analysis of financial news the sampling of negative and positive words in economic sense was formed based on Loughran McDonald Master Dictionary. The sampling included the words with high frequency of occurrence in the news of financial markets. For single-root words it has been left only common part that allows covering few words for one request. Neural networks were chosen for modeling and forecasting. To automate the process of extracting information from the economic news a script was developed in the MATLAB Simulink programming environment, which is based on the generated sampling of positive and negative words. Experimental studies with different architectures of neural networks showed a high adequacy of constructed models and confirmed the feasibility of using information from news feeds to predict the stock prices.

Suggested Citation

  • Kateryna Kononova & Anton Dek, 2017. "Financial Time Series Forecasting: Semantic Analysis Of Economic News," Papers 1705.08545, arXiv.org.
    • Handle: RePEc:arx:papers:1705.08545
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1705.08545
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    2. Leonid Kogan & Stephen A. Ross & Jiang Wang & Mark M. Westerfield, 2006. "The Price Impact and Survival of Irrational Traders," Journal of Finance, American Finance Association, vol. 61(1), pages 195-229, February.
    3. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fu, Chengbo & Jacoby, Gady & Wang, Yan, 2015. "Investor sentiment and portfolio selection," Finance Research Letters, Elsevier, vol. 15(C), pages 266-273.
    2. Sayim, Mustafa & Rahman, Hamid, 2015. "An examination of U.S. institutional and individual investor sentiment effect on the Turkish stock market," Global Finance Journal, Elsevier, vol. 26(C), pages 1-17.
    3. Akbas, Ferhat & Boehmer, Ekkehart & Jiang, Chao & Koch, Paul D., 2022. "Overnight returns, daytime reversals, and future stock returns," Journal of Financial Economics, Elsevier, vol. 145(3), pages 850-875.
    4. Fu, Junhui & Wu, Xiang & Liu, Yufang & Chen, Rongda, 2021. "Firm-specific investor sentiment and stock price crash risk," Finance Research Letters, Elsevier, vol. 38(C).
    5. Zhi Da & Borja Larrain & Clemens Sialm & José Tessada, 2016. "Coordinated Noise Trading: Evidence from Pension Fund Reallocations," NBER Working Papers 22161, National Bureau of Economic Research, Inc.
    6. Hu, Wei & Zheng, Zhenlong, 2020. "Expectile CAPM," Economic Modelling, Elsevier, vol. 88(C), pages 386-397.
    7. Jördis Hengelbrock & Erik Theissen & Christian Westheide, 2013. "Market Response to Investor Sentiment," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 40(7-8), pages 901-917, September.
    8. Bahloul, Walid & Bouri, Abdelfettah, 2016. "The impact of investor sentiment on returns and conditional volatility in U.S. futures markets," Journal of Multinational Financial Management, Elsevier, vol. 36(C), pages 89-102.
    9. Dash, Saumya Ranjan & Maitra, Debasish, 2018. "Does sentiment matter for stock returns? Evidence from Indian stock market using wavelet approach," Finance Research Letters, Elsevier, vol. 26(C), pages 32-39.
    10. Ben-Rephael, Azi & Kandel, Shmuel & Wohl, Avi, 2012. "Measuring investor sentiment with mutual fund flows," Journal of Financial Economics, Elsevier, vol. 104(2), pages 363-382.
    11. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).
    12. Kumari, Jyoti, 2019. "Investor sentiment and stock market liquidity: Evidence from an emerging economy," Journal of Behavioral and Experimental Finance, Elsevier, vol. 23(C), pages 166-180.
    13. Shahzad, Syed Jawad Hussain & Raza, Naveed & Balcilar, Mehmet & Ali, Sajid & Shahbaz, Muhammad, 2017. "Can economic policy uncertainty and investors sentiment predict commodities returns and volatility?," Resources Policy, Elsevier, vol. 53(C), pages 208-218.
    14. Ding, Wenjie & Mazouz, Khelifa & Wang, Qingwei, 2021. "Volatility timing, sentiment, and the short-term profitability of VIX-based cross-sectional trading strategies," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 42-56.
    15. Hela Namouri & Fredj Jawadi & Zied Ftiti & Néjib Hachicha, 2018. "Threshold effect in the relationship between investor sentiment and stock market returns: a PSTR specification," Applied Economics, Taylor & Francis Journals, vol. 50(5), pages 559-573, January.
    16. Irresberger, Felix & Mühlnickel, Janina & Weiß, Gregor N.F., 2015. "Explaining bank stock performance with crisis sentiment," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 311-329.
    17. Hou, Yang & Meng, Jiayin, 2018. "The momentum effect in the Chinese market and its relationship with the simultaneous and the lagged investor sentiment," MPRA Paper 94838, University Library of Munich, Germany.
    18. Riso, Luigi & Vacca, Gianmarco, 2024. "Sentiment dynamics and volatility: A study based on GARCH-MIDAS and machine learning," Finance Research Letters, Elsevier, vol. 62(PB).
    19. Wei Xiong, 2013. "Bubbles, Crises, and Heterogeneous Beliefs," NBER Working Papers 18905, National Bureau of Economic Research, Inc.
    20. Shen, Yiran & Liu, Chang & Sun, Xiaolei & Guo, Kun, 2023. "Investor sentiment and the Chinese new energy stock market: A risk–return perspective," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 395-408.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1705.08545. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.