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Impact of News on the Commodity Market: Dataset and Results

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1364)


Over the last few years, machine learning based methods have been applied to extract information from news flow in the financial domain. However, this information has mostly been in the form of the financial sentiments contained in the news headlines, primarily for the stock prices. In our current work, we propose that various other dimensions of information can be extracted from news headlines, which will be of interest to investors, policy-makers and other practitioners. We propose a framework that extracts information such as past movements and expected directionality in prices, asset comparison and other general information that the news is referring to. We apply this framework to the commodity “Gold” and train the machine learning models using a dataset of 11,412 human-annotated news headlines (released with this study), collected from the period 2000–2019. We experiment to validate the causal effect of news flow on gold prices and observe that the information produced from our framework significantly impacts the future gold price.


  • Machine learning
  • Natural Language Processing
  • Text mining

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  1. Araci, D.: FinBERT: financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063 (2019)

  2. Barnhart, S.W.: The effects of macroeconomic announcements on commodity prices. Am. J. Agric. Econ. 71(2), 389–403 (1989)

    CrossRef  Google Scholar 

  3. Cai, J., Cheung, Y.L., Wong, M.C.: What moves the gold market? J. Futures Mark.: Futures, Opt. Deriv. Prod. 21(3), 257–278 (2001)

    CrossRef  Google Scholar 

  4. Caporale, G.M., Spagnolo, F., Spagnolo, N.: Macro news and commodity returns. Int. J. Financ. Econ. 22(1), 68–80 (2017)

    CrossRef  Google Scholar 

  5. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

  6. Christie-David, R., Chaudhry, M., Koch, T.W.: Do macroeconomics news releases affect gold and silver prices? J. Econ. Bus. 52(5), 405–421 (2000)

    CrossRef  Google Scholar 

  7. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  8. Ederington, L.H., Lee, J.H.: How markets process information: news releases and volatility. J. Financ. 48(4), 1161–1191 (1993)

    CrossRef  Google Scholar 

  9. Elder, J., Miao, H., Ramchander, S.: Impact of macroeconomic news on metal futures. J. Bank. Financ. 36(1), 51–65 (2012)

    CrossRef  Google Scholar 

  10. Feuerriegel, S., Neumann, D.: News or noise? How news drives commodity prices. In: ICIS 2013 Proceedings. AIS Electronic Library (2013), 34th International Conference on Information Systems (ICIS 2013); Conference Location: Milan, Italy; Conference Date: December 15–18, 2013

    Google Scholar 

  11. Frankel, J.A., Hardouvelis, G.A.: Commodity prices, money surprises and fed credibilit. J. Money, Credit, Bank. 17(4), 425–438 (1985)

    CrossRef  Google Scholar 

  12. Ghosal, D., Bhatnagar, S., Akhtar, M.S., Ekbal, A., Bhattacharyya, P.: IITP at semeval-2017 task 5: an ensemble of deep learning and feature based models for financial sentiment analysis. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 899–903 (2017)

    Google Scholar 

  13. Hautsch, N., Groß-Klußmann, A.: When machines read the news: using automated text analytics to quantify high frequency news-implied market reactions. J. Empirical Financ. 18, 321–340 (2011)

    CrossRef  Google Scholar 

  14. Hess, D., Huang, H., Niessen, A.: How do commodity futures respond to macroeconomic news? Financ. Mark. Portfolio Manage. 22(2), 127–146 (2008)

    CrossRef  Google Scholar 

  15. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    CrossRef  Google Scholar 

  16. MacroTrends: Gold prices - 100 year historical chart. Accessed 15 June 2019

  17. Malkiel, B.G.: Efficient Market Hypothesis, pp. 127–134. Palgrave Macmillan UK, London (1989).

  18. Malo, P., Sinha, A., Korhonen, P., Wallenius, J., Takala, P.: Good debt or bad debt: detecting semantic orientations in economic texts. J. Assoc. Inf. Sci. Technol. 65(4), 782–796 (2014)

    CrossRef  Google Scholar 

  19. Malo, P., Sinha, A., Takala, P., Ahlgren, O., Lappalainen, I.: Learning the roles of directional expressions and domain concepts in financial news analysis. In: 2013 IEEE 13th International Conference on Data Mining Workshops, pp. 945–954. IEEE (2013)

    Google Scholar 

  20. Mao, H., Counts, S., Bollen, J.: Predicting financial markets: Comparing survey, news, twitter and search engine data. arXiv preprint arXiv:1112.1051 (2011)

  21. Moore, A., Rayson, P.: Lancaster a at semeval-2017 task 5: evaluation metrics matter: predicting sentiment from financial news headlines. arXiv preprint arXiv:1705.00571 (2017)

  22. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014).

  23. Rao, T., Srivastava, S.: Modeling movements in oil, gold, forex and market indices using search volume index and twitter sentiments. In: Proceedings of the 5th Annual ACM Web Science Conference, pp. 336–345. ACM (2013)

    Google Scholar 

  24. Roache, S.K.: The Effects of Economic News on Commodity Prices: Is Gold Just Another Commodity? No. 9–140, International Monetary Fund (2009)

    Google Scholar 

  25. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)

    CrossRef  Google Scholar 

  26. Shen, J., Najand, M., Dong, F., He, W.: News and social media emotions in the commodity market. Rev. Behav. Financ. 9(2), 148–168 (2017)

    CrossRef  Google Scholar 

  27. Sinha, A., Kedas, S., Kumar, R., Malo, P.: Buy, sell or hold: entity-aware classification of business news (2019)

    Google Scholar 

  28. Smales, L.A.: News sentiment in the gold futures market. J. Bank. Financ. 49, 275–286 (2014)

    CrossRef  Google Scholar 

  29. Takala, P., Malo, P., Sinha, A., Ahlgren, O.: Gold-standard for topic-specific sentiment analysis of economic texts. Citeseer

    Google Scholar 

  30. Tetlock, P.C.: Giving content to investor sentiment: the role of media in the stock market. J. Financ. 62(3), 1139–1168 (2007)

    CrossRef  Google Scholar 

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Ankur Sinha would like to acknowledge India Gold Policy Centre (IGPC) for supporting this study under grant number 1815012.

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Correspondence to Tanmay Khandait .

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Sinha, A., Khandait, T. (2021). Impact of News on the Commodity Market: Dataset and Results. In: Arai, K. (eds) Advances in Information and Communication. FICC 2021. Advances in Intelligent Systems and Computing, vol 1364. Springer, Cham.

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