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Assessing News Contagion in Finance

Author

Listed:
  • Paola Cerchiello

    (Department of Economics and Management, University of Pavia)

  • Giancarlo Nicola

    (Department of Economics and Management, University of Pavia)

Abstract

The analysis of news data in the financial context has gained a prominent interest in the last years. This because of the possible predictive power of such content especially in terms of associated sentiment/mood. In this paper we focus on a specific aspect of financial news analysis: how the covered topics modify according to space and time dimensions. To this purpose, we employ a modified version of topic model LDA, the so called Structural Topic Model (STM), that takes into account covariates as well. Our aim is to study the possible evolution of topics extracted from two well known news archive - Reuters and Bloomberg - and to investigate a causal effect in the diffusion of the news by means of a Granger causality test. Our results show that both the temporal dynamics and the spatial differentiation matter in the news contagion.

Suggested Citation

  • Paola Cerchiello & Giancarlo Nicola, 2017. "Assessing News Contagion in Finance," DEM Working Papers Series 139, University of Pavia, Department of Economics and Management.
    • Handle: RePEc:pav:demwpp:demwp0139
    as

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    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0139.pdf
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    References listed on IDEAS

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    1. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    2. Paola Cerchiello & Paolo Giudici, 2014. "How to measure the quality of financial tweets," DEM Working Papers Series 069, University of Pavia, Department of Economics and Management.
    3. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    4. Pekka Malo & Ankur Sinha & Pekka Korhonen & Jyrki Wallenius & Pyry Takala, 2014. "Good debt or bad debt: Detecting semantic orientations in economic texts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 782-796, April.
    5. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    6. Sims, Christopher A, 1972. "Money, Income, and Causality," American Economic Review, American Economic Association, vol. 62(4), pages 540-552, September.
    7. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Citations

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    Cited by:

    1. Scaramozzino, Roberta & Cerchiello, Paola & Aste, Tomaso, 2021. "Information theoretic causality detection between financial and sentiment data," LSE Research Online Documents on Economics 110903, London School of Economics and Political Science, LSE Library.
    2. Nicola, Giancarlo & Cerchiello, Paola & Aste, Tomaso, 2020. "Information network modeling for U.S. banking systemic risk," LSE Research Online Documents on Economics 107563, London School of Economics and Political Science, LSE Library.
    3. Sturm, Silke, 2019. "Political Competition: How to Measure Party Strategy in Direct Voter Communication using Social Media Data?," Hamburg Discussion Papers in International Economics 1, University of Hamburg, Department of Economics.

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    More about this item

    Keywords

    behavioural finance; financial news; structural topic model; Granger causality.;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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