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Measuring Financial Sentiment to Predict Financial Instability: A New Approach based on Text Analysis

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  • Paul Ormerod
  • Rickard Nyman
  • David Tuckett

Abstract

Following the financial crisis of the late 2000s, policy makers have shown considerable interest in monitoring financial stability. Several central banks now publish indices of financial stress, which are essentially based upon market related data. In this paper, we examine the potential for improving the indices by deriving information about emotion shifts in the economy. We report on a new approach, based on the content analysis of very large text databases, and termed directed algorithmic text analysis. The algorithm identifies, very rapidly, shifts through time in the relations between two core emotional groups. The method is robust. The same word-list is used to identify the two emotion groups across different studies. Membership of the words in the lists has been validated in psychological experiments. The words consist of everyday English words with no specific economic meaning. Initial results show promise. An emotion index capturing shifts between the two emotion groups in texts potentially referring to the whole US economy improves the one-quarter ahead consensus forecasts for real GDP growth. More specifically, the same indices are shown to Granger cause both the Cleveland and St Louis Federal Reserve Indices of Financial Stress.

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  • Paul Ormerod & Rickard Nyman & David Tuckett, 2015. "Measuring Financial Sentiment to Predict Financial Instability: A New Approach based on Text Analysis," Papers 1508.05357, arXiv.org.
    • Handle: RePEc:arx:papers:1508.05357
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    Cited by:

    1. Irving Fisher Committee, 2017. "Big Data," IFC Bulletins, Bank for International Settlements, number 44.
    2. Omotosho, Babatunde S. & Tumala, Mohammed M., 2019. "A Text Mining Analysis of Central Bank Monetary Policy Communication in Nigeria," MPRA Paper 98850, University Library of Munich, Germany.
    3. Giuseppe Bruno, 2017. "Central Bank Communications: information extraction and semantic analysis," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Big Data, volume 44, Bank for International Settlements.
    4. Ghufran Ahmad & Muhammad Suhail Rizwan & Dawood Ashraf, 2021. "Systemic risk and macroeconomic forecasting: A globally applicable copula‐based approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1420-1443, December.
    5. María del Pilar Cruz & Hugo Peralta & Bruno Ávila, 2020. "Análisis de Sentimiento Basado en el Informe de Percepciones de Negocios del Banco Central de Chile," Working Papers Central Bank of Chile 862, Central Bank of Chile.
    6. Hamed Mirashk & Amir Albadvi & Mehrdad Kargari & Mohammad Ali Rastegar, 2024. "News Sentiment and Liquidity Risk Forecasting: Insights from Iranian Banks," Risks, MDPI, vol. 12(11), pages 1-32, October.

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