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“Watch your tone!”: Forecasting mining industry commodity prices with financial report tone

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  • Hardy, Nicolás
  • Ferreira, Tiago
  • Quinteros, Maria J.
  • Magner, Nicolás S.

Abstract

This paper presents robust evidence indicating that the tone of financial reports from the US mining industry firms can predict certain mining commodity returns. We assess this predictive ability with different tests, including evaluations of mean squared prediction errors, correlations, mean directional accuracy, and trading strategies. We compared our results with several benchmarks proposed in the literature, including the random walk, outperforming most statistically and economically. Our findings are related to the literature on firms' strategic disclosure choices, suggesting that firms may strategically tailor their disclosure decisions to influence investor expectations regarding future performance. Additionally, we conduct a series of placebo tests, revealing significant outcomes: (1) the tone of financial reports from other industries does not exhibit predictive power for mining industry commodity returns, (2) this predictive ability is not overshadowed when controlling for uncertainty measures, such as VIX, OVX, realized volatility and Google Trends commodity price searches, and (3) the tone of the mining industry's financial report does not predict returns for unrelated mining commodities. To our knowledge, our work is the first paper to employ financial report tone through text mining of nearly 60,000 financial reports to forecast commodity returns. Our findings substantially contribute to the commodity forecasting literature, offering valuable insights for portfolio managers and professionals seeking to enhance their forecasting capabilities.

Suggested Citation

  • Hardy, Nicolás & Ferreira, Tiago & Quinteros, Maria J. & Magner, Nicolás S., 2023. "“Watch your tone!”: Forecasting mining industry commodity prices with financial report tone," Resources Policy, Elsevier, vol. 86(PA).
  • Handle: RePEc:eee:jrpoli:v:86:y:2023:i:pa:s0301420723009625
    DOI: 10.1016/j.resourpol.2023.104251
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    More about this item

    Keywords

    Forecasting; Text-mining; Sentiment analysis; Commodities; Base metals; Time-series models; Out-of-sample comparison; Base metal equity securities; Qualitative disclosure; Linguistic tone;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • L74 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Construction
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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