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Investors’ attention and network spillover for commodity market forecasting

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  • Cerqueti, Roy
  • Ficcadenti, Valerio
  • Mattera, Raffaele

Abstract

This paper explores the role of network spillovers in commodity market forecasting and proposes a novel factor-augmented dynamic network model. We focus on a novel network definition based on investors’ attention to commodities, positing that commodities exhibit spillovers if they share a similar level of interest. To this aim, we employ Google Trends search data as an instrumental measure for attention. The results reveal that including attention-driven spillovers significantly enhances the forecasting accuracy of commodity returns.

Suggested Citation

  • Cerqueti, Roy & Ficcadenti, Valerio & Mattera, Raffaele, 2024. "Investors’ attention and network spillover for commodity market forecasting," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:soceps:v:95:y:2024:i:c:s0038012124002222
    DOI: 10.1016/j.seps.2024.102023
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    as
    1. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Adewuyi, Adeolu, 2020. "Google trends and the predictability of precious metals," Resources Policy, Elsevier, vol. 65(C).
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. Anna Gloria Billé & Alessio Tomelleri & Francesco Ravazzolo, 2023. "Forecasting regional GDPs: a comparison with spatial dynamic panel data models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 18(4), pages 530-551, October.
    4. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra & Agca Aktunc, Esra, 2021. "Forecasting models for daily natural gas consumption considering periodic variations and demand segregation," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    5. Li, Liyao & Yang, Zhenlin, 2021. "Spatial dynamic panel data models with correlated random effects," Journal of Econometrics, Elsevier, vol. 221(2), pages 424-454.
    6. Li, Xuerong & Shang, Wei & Wang, Shouyang, 2019. "Text-based crude oil price forecasting: A deep learning approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1548-1560.
    7. Marek Kwas & Michał Rubaszek, 2021. "Forecasting Commodity Prices: Looking for a Benchmark," Forecasting, MDPI, vol. 3(2), pages 1-13, June.
    8. Tao Chen, 2017. "Investor Attention and Global Stock Returns," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 18(3), pages 358-372, July.
    9. Mattera, Raffaele & Otto, Philipp, 2024. "Network log-ARCH models for forecasting stock market volatility," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1539-1555.
    10. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    11. Cedric Mbanga & Ali F. Darrat & Jung Chul Park, 2019. "Investor sentiment and aggregate stock returns: the role of investor attention," Review of Quantitative Finance and Accounting, Springer, vol. 53(2), pages 397-428, August.
    12. Elzaki, Raga M. & Elrasheed, Mutasim.M.M. & Elmulthum, Nagat A., 2022. "Optimal crop combination under soaring oil and energy prices in the kingdom of Saudi Arabia," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    13. Pesaran, M. Hashem & Tosetti, Elisa, 2011. "Large panels with common factors and spatial correlation," Journal of Econometrics, Elsevier, vol. 161(2), pages 182-202, April.
    14. Daskalaki, Charoula & Kostakis, Alexandros & Skiadopoulos, George, 2014. "Are there common factors in individual commodity futures returns?," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 346-363.
    15. Ahmed, Shamim & Tsvetanov, Daniel, 2016. "The predictive performance of commodity futures risk factors," Journal of Banking & Finance, Elsevier, vol. 71(C), pages 20-36.
    16. Dick van Dijk & Philip Hans Franses, 2003. "Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 727-744, December.
    17. Massimo Guidolin & Manuela Pedio, 2021. "Forecasting commodity futures returns with stepwise regressions: Do commodity-specific factors help?," Annals of Operations Research, Springer, vol. 299(1), pages 1317-1356, April.
    18. Zeng Tian & Swanson Norman R., 1998. "Predictive Evaluation of Econometric Forecasting Models in Commodity Futures Markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(4), pages 1-21, January.
    19. Melody Y. Huang & Randall R. Rojas & Patrick D. Convery, 2020. "Forecasting stock market movements using Google Trend searches," Empirical Economics, Springer, vol. 59(6), pages 2821-2839, December.
    20. Alquist, Ron & Bhattarai, Saroj & Coibion, Olivier, 2020. "Commodity-price comovement and global economic activity," Journal of Monetary Economics, Elsevier, vol. 112(C), pages 41-56.
    21. Leif Anders Thorsrud, 2020. "Words are the New Numbers: A Newsy Coincident Index of the Business Cycle," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 393-409, April.
    22. Gary B. Gorton & Fumio Hayashi & K. Geert Rouwenhorst, 2013. "The Fundamentals of Commodity Futures Returns," Review of Finance, European Finance Association, vol. 17(1), pages 35-105.
    23. George Athanasopoulos & Rob J Hyndman & Raffaele Mattera, 2023. "Improving out-of-sample Forecasts of Stock Price Indexes with Forecast Reconciliation and Clustering," Monash Econometrics and Business Statistics Working Papers 17/23, Monash University, Department of Econometrics and Business Statistics.
    24. Wang, Wenzhao & Su, Chen & Duxbury, Darren, 2021. "Investor sentiment and stock returns: Global evidence," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 365-391.
    25. Rettinger, Moritz & Mandl, Christian & Minner, Stefan, 2024. "A data-driven approach for optimal operational and financial commodity hedging," European Journal of Operational Research, Elsevier, vol. 316(1), pages 341-360.
    26. Pagnottoni, Paolo & Spelta, Alessandro, 2023. "The motifs of risk transmission in multivariate time series: Application to commodity prices," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    27. Abreu, Paulo & Santos, Daniel & Barbosa-Povoa, Ana, 2023. "Data-driven forecasting for operational planning of emergency medical services," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    28. Lee, Lung-fei & Yu, Jihai, 2010. "A Spatial Dynamic Panel Data Model With Both Time And Individual Fixed Effects," Econometric Theory, Cambridge University Press, vol. 26(2), pages 564-597, April.
    29. Foucault, Thierry & Pagano, Marco & Roell, Ailsa, 2013. "Market Liquidity: Theory, Evidence, and Policy," OUP Catalogue, Oxford University Press, number 9780199936243.
    30. Ardia, David & Bluteau, Keven & Boudt, Kris, 2019. "Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1370-1386.
    31. Fang, Hao & Chung, Chien-Ping & Lu, Yang-Cheng & Lee, Yen-Hsien & Wang, Wen-Hao, 2021. "The impacts of investors' sentiments on stock returns using fintech approaches," International Review of Financial Analysis, Elsevier, vol. 77(C).
    32. Edwards, Dominiqueca R. & Idoko, Faith O. & Vogiatzis, Chrysafis & Davis, Lauren B. & Mirchandani, Pitu, 2023. "Determining optimal fuel delivery strategies under uncertainty," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    33. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Oxley, Les, 2021. "An analysis of investor behaviour and information flows surrounding the negative WTI oil price futures event," Energy Economics, Elsevier, vol. 104(C).
    34. Gao, Lin & Süss, Stephan, 2015. "Market sentiment in commodity futures returns," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 84-103.
    35. B. M. Tabak & T. R. Serra & D. O. Cajueiro, 2010. "Topological properties of commodities networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 74(2), pages 243-249, March.
    36. Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
    37. Levent Bulut, 2018. "Google Trends and the forecasting performance of exchange rate models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 303-315, April.
    38. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    39. Vecchiato, Riccardo, 2012. "Environmental uncertainty, foresight and strategic decision making: An integrated study," Technological Forecasting and Social Change, Elsevier, vol. 79(3), pages 436-447.
    40. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    41. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    42. Daniel Andrei & Michael Hasler, 2015. "Investor Attention and Stock Market Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 33-72.
    43. Yu, Lean & Zhao, Yaqing & Tang, Ling & Yang, Zebin, 2019. "Online big data-driven oil consumption forecasting with Google trends," International Journal of Forecasting, Elsevier, vol. 35(1), pages 213-223.
    44. J. Paul Elhorst, 2022. "The dynamic general nesting spatial econometric model for spatial panels with common factors: Further raising the bar," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 42(3), pages 249-267, December.
    45. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
    46. Christoph Bergmeir, 2023. "Common Pitfalls and Better Practices in Forecast Evaluation for Data Scientists," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 70, pages 5-12, Q3.
    47. Afees A. Salisu & Ahamuefula E. Ogbonna & Idris Adediran, 2021. "Stock‐induced Google trends and the predictability of sectoral stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 327-345, March.
    48. Bai, Jushan & Li, Kunpeng, 2021. "Dynamic spatial panel data models with common shocks," Journal of Econometrics, Elsevier, vol. 224(1), pages 134-160.
    49. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    50. Zhao, Lu-Tao & Zheng, Zhi-Yi & Wei, Yi-Ming, 2023. "Forecasting oil inventory changes with Google trends: A hybrid wavelet decomposer and ARDL-SVR ensemble model," Energy Economics, Elsevier, vol. 120(C).
    51. Chung, San-Lin & Hung, Chi-Hsiou & Yeh, Chung-Ying, 2012. "When does investor sentiment predict stock returns?," Journal of Empirical Finance, Elsevier, vol. 19(2), pages 217-240.
    52. Shi, Wei & Lee, Lung-fei, 2017. "Spatial dynamic panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 197(2), pages 323-347.
    53. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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