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Deciphering Indian inflationary expectations through text mining: an exploratory approach

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Abstract

Inflationary forecasts tend to play a crucial role in macroeconomic and financial decision/policy making. In particular, in an inflation-targeting framework, it is of paramount importance. While traditionally, model-based and survey-based inflation expectations are being used, in recent times, a literature has emerged to forecast various macro-aggregates using text-based sentiment estimates. Taking a cue from this approach, in this paper we attempt to decipher inflationary sentiments using text mining from two leading financial dailies, viz., the Economic Times and Business Line. We consciously avoid using social media news due to severe challenges and high noise-to-signal ratio. In our algorithm we aggregate CPI basket level (viz., food, fuel, cloth & miscellaneous) sentiment into an overall index of inflation, adapting techniques from natural language processing. Our results from this text-based model indicate significant success in tracking actual inflation.

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Source: Displacy Library (Part of Spacy), available at https://spacy.io/usage/visualizers

Fig. 3

Source: Authors’ Calculations

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Notes

  1. See Dua (2020) for a detailed discussion on monetary policy framework in India.

  2. Interestingly, an analysis of IESH in a recent RBI study revealed, “Ever since the inception of the survey, households’ inflation expectations are found to have an upward bias, when compared with the actual inflation, by about 300 basis points on an average for the three months ahead horizon and 400 basis points for the one year ahead horizon” (RBI, 2019). In fact, IESH does not appear to be efficient (Sharma and Bicchal, 2018).

    We thank an anonymous reviewer and an Associate Editor for their many helpful comments.

  3. Illustratively, RBI Executive Director (in charge of monetary policy) in a speech noted, “The RBI’s inflation expectations survey of households provides a measurement of the sentiment of inflation expectations that is essentially urban” (Mohanty, 2012).

  4. We are indebted to an anonymous referee for pointing this explicitly to us.

  5. We are indebted to an anonymous referee for pointing out this subtle difference.

  6. Incidentally, the RBI also used this CPI-based inflation for its flexible inflation targeting policy.

  7. As is well-known, Python is an interpreted, object-oriented, high-level programming language with dynamic semantics; see https://www.python.org/doc/essays/ for details.

  8. A global news monitoring and search engine, owned by the Dow Jones & Company.

  9. The relevant weights for each of these groups are 45.86%, 2.38%, 6.53%, 10.07%, 6.84%, and 28.32%, respectively.

  10. NLTK stands for Natural Language Toolkit. This toolkit is an NLP library which contains packages to make machines understand human language and reply to it with an appropriate response.

  11. An encouraging development is a recent RBI article which found that the sentiment index (derived through text) has significant predictive ability for retail inflation in India; see RBI (2020).

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Funding

We acknowledge the data support provided by the Financial Research and Trading Laboratory of IIM Calcutta and research grant by IIM Calcutta.

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Authors and Affiliations

  1. Finance and Control Group, IIM Calcutta, Kolkata, India

    Ashok Banerjee

  2. Stanford University, Stanford, USA

    Ayush Kanodia

  3. Economics Group, IIM Calcutta, Kolkata, India

    Partha Ray

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  1. Ashok Banerjee
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  2. Ayush Kanodia
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  3. Partha Ray
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Correspondence to Ashok Banerjee.

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Banerjee, A., Kanodia, A. & Ray, P. Deciphering Indian inflationary expectations through text mining: an exploratory approach. Ind. Econ. Rev. 56, 49–66 (2021). https://doi.org/10.1007/s41775-021-00106-9

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