Readme.md

Data for Macroeconomic Attention and Narrative Uncertainty

We provide the daily and monthly macroeconomic attention indices (MAI) and narrative uncertainty scores (UNC) for the New York Times (NYT_) and Wall Street Journal (WSJ_) used in the following papers:

• Fisher, Martineau, and Sheng (2021) "Macroeconomic Attention and Announcement Risk Premia" SSRN
• Martineau, Poulos, Wu, Thompson, Haghighi, Yuan, Hull (2023) "Narrative Monetary Policy Uncertainty" SSRN
• Benson, Hull, Nozawa, Poulos, Strela, Wu (2023) "Forecasting Using Text-Based Uncertainty Measures" SSRN

The visualization of the data on daily and monthly frequency can be viewed on HuggingFace.

Data

The MAI data is available from June 1980 to December 2020, and includes the following series:

• CREDIT_RATING_MAI: credit rating
• GDP_MAI: GDP
• HSNG_MAI: housing market
• INFL_MAI: inflation
• MONETARY_MAI: monetary
• OIL_MAI: oil
• UNEMP_MAI: unemployment
• USD_MAI: USD

The UNC data is available from January 1984 to May 2023, we include both uncertainty generated using static Loughran McDonald dictionary (_LM) and uncertainty generated using BERT model (_ML). The data includes the following series:

• NMPU: monetary policy uncertainty
• INFU: inflation uncertainty
• INFU_weighted: the daily average of inflation uncertainty is calculated based on the weighted average of counts of the word "inflation" in the article
• INFU_screened: the daily average of inflation uncertainty is calculated only on articles where the word "inflation" appears once in the title or the first five sentences in the text, or when the word "inflation" appears at least twice in the article
• GSCU: uncertainty about commodity.

We provide the daily series, daily series detrended by 252 trading day average, and monthly trading day average series.

We also include the following existing measures of uncertainty. If you use those data, please cite the corresponding works accordingly.

• EPU: policy uncertainty measure proposed by Baker and Bloom. Official website
• MPU-MKT: Uncertainty from short-term rates based on Eurodollar futures options by Bauer
• VIX: Chicago Board Options Exchange Volatility Index, monthly data is the last valid MOVE of each month
• MOVE: Merrill Lynch Option Volatility Estimate, monthly data is the last valid MOVE of each month

Citation

If you use this data, please cite our studies accordingly:

@article{MAI,
    author = {Fisher, Adlai and Martineau, Charles and Sheng, Jinfei},
    title = "{Macroeconomic Attention and Announcement Risk Premia}",
    journal = {Review of Financial Studies},
    volume = {35},
    number = {11},
    pages = {5057-5093},
    year = {2022},
    month = {02},
    abstract = "{We construct macroeconomic attention indexes (MAI), which are new measures of attention to different macroeconomic risks, including unemployment and monetary policy. Individual MAI tend to increase around related announcements and following changes in related fundamentals. Further, bad news raises attention more than good news. For unemployment and FOMC, attention predicts announcement risk premiums and implied volatility changes with large economic magnitudes. Our findings support theories of endogenous attention and announcement risk premiums, while demonstrating future research directions, including that announcements can raise new concerns. Macroeconomic announcements are important not only for contents and timing but also for attention.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.}",
    issn = {0893-9454},
    doi = {10.1093/rfs/hhac011},
    url = {https://doi.org/10.1093/rfs/hhac011},
    eprint = {https://academic.oup.com/rfs/article-pdf/35/11/5057/46558642/hhac011.pdf},
}

@article{NMPU,
  author={Martineau, Charles and Poulos, Zissis and Wu, Yuntao and Thompson, Cameron and Haghighi, Maryam and Yuan, Jun and Hull, John},
  title={Narrative Monetary Policy Uncertainty},
  year={2023},
  month={9},
  note = {Available at SSRN: \url{https://ssrn.com/abstract=4573829} or \url{http://dx.doi.org/10.2139/ssrn.4573829}}
}

Credit

This project is built on Gradio, Plotly and pandas