IDEAS home Printed from https://ideas.repec.org/a/eee/inteco/v162y2020icp110-124.html
   My bibliography  Save this article

The effects of investor emotions sentiments on crude oil returns: A time and frequency dynamics analysis

Author

Listed:
  • Maghyereh, Aktham
  • Awartani, Basel
  • Abdoh, Hussein

Abstract

In this paper, we use wavelet coherence analysis to find that sentiment has a significant effect on crude oil returns that lasts over various investment horizons. While oil returns are positively associated with the sentiments of optimism and trust, they are negatively linked to fear and anger. These relations are more pronounced over the medium and the long term. Additionally, we find that short-term oil returns are relatively more sentiment-sensitive during turbulent periods than in normal conditions. These results highlight the importance of sentiment and investor psychology in the crude oil market.

Suggested Citation

  • Maghyereh, Aktham & Awartani, Basel & Abdoh, Hussein, 2020. "The effects of investor emotions sentiments on crude oil returns: A time and frequency dynamics analysis," International Economics, Elsevier, vol. 162(C), pages 110-124.
  • Handle: RePEc:eee:inteco:v:162:y:2020:i:c:p:110-124
    DOI: 10.1016/j.inteco.2020.01.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2110701719302458
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.inteco.2020.01.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jiancheng Shen & Mohammad Najand & Feng Dong & Wu He, 2017. "News and social media emotions in the commodity market," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 9(2), pages 148-168, July.
    2. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Ugolini, Andrea, 2017. "Wavelet-based test of co-movement and causality between oil and renewable energy stock prices," Energy Economics, Elsevier, vol. 61(C), pages 241-252.
    3. Gallegati, Marco & Ramsey, James B., 2014. "The forward looking information content of equity and bond markets for aggregate investments," Journal of Economics and Business, Elsevier, vol. 75(C), pages 1-24.
    4. David Hirshleifer & Tyler Shumway, 2003. "Good Day Sunshine: Stock Returns and the Weather," Journal of Finance, American Finance Association, vol. 58(3), pages 1009-1032, June.
    5. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    6. Qadan, Mahmoud & Nama, Hazar, 2018. "Investor sentiment and the price of oil," Energy Economics, Elsevier, vol. 69(C), pages 42-58.
    7. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    8. Baeck, E.G. & Brock, W.A., 1992. "A Nonparametric Test for Independence of a Multivariate Time Series," Working papers 9204, Wisconsin Madison - Social Systems.
    9. Ji, Qiang & Li, Jianping & Sun, Xiaolei, 2019. "Measuring the interdependence between investor sentiment and crude oil returns: New evidence from the CFTC's disaggregated reports," Finance Research Letters, Elsevier, vol. 30(C), pages 420-425.
    10. Deeney, Peter & Cummins, Mark & Dowling, Michael & Bermingham, Adam, 2015. "Sentiment in oil markets," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 179-185.
    11. Manuel Ammann & Roman Frey & Michael Verhofen, 2014. "Do Newspaper Articles Predict Aggregate Stock Returns?," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 15(3), pages 195-213, July.
    12. Wang, Yaw-Huei & Keswani, Aneel & Taylor, Stephen J., 2006. "The relationships between sentiment, returns and volatility," International Journal of Forecasting, Elsevier, vol. 22(1), pages 109-123.
    13. Bekiros, Stelios D. & Diks, Cees G.H., 2008. "The nonlinear dynamic relationship of exchange rates: Parametric and nonparametric causality testing," Journal of Macroeconomics, Elsevier, vol. 30(4), pages 1641-1650, December.
    14. Maitra, Debasish & Dash, Saumya Ranjan, 2017. "Sentiment and stock market volatility revisited: A time–frequency domain approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 15(C), pages 74-91.
    15. Li, Sufang & Zhang, Hu & Yuan, Di, 2019. "Investor attention and crude oil prices: Evidence from nonlinear Granger causality tests," Energy Economics, Elsevier, vol. 84(C).
    16. repec:bla:jfinan:v:53:y:1998:i:6:p:1839-1885 is not listed on IDEAS
    17. Chen, Wei & Huang, Zhuo & Yi, Yanping, 2015. "Is there a structural change in the persistence of WTI–Brent oil price spreads in the post-2010 period?," Economic Modelling, Elsevier, vol. 50(C), pages 64-71.
    18. Reboredo, Juan C. & Rivera-Castro, Miguel A., 2013. "A wavelet decomposition approach to crude oil price and exchange rate dependence," Economic Modelling, Elsevier, vol. 32(C), pages 42-57.
    19. Chakrabarty, Anindya & De, Anupam & Gunasekaran, Angappa & Dubey, Rameshwar, 2015. "Investment horizon heterogeneity and wavelet: Overview and further research directions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 45-61.
    20. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    21. Yang, Cai & Gong, Xu & Zhang, Hongwei, 2019. "Volatility forecasting of crude oil futures: The role of investor sentiment and leverage effect," Resources Policy, Elsevier, vol. 61(C), pages 548-563.
    22. Li, Jun & Yu, Jianfeng, 2012. "Investor attention, psychological anchors, and stock return predictability," Journal of Financial Economics, Elsevier, vol. 104(2), pages 401-419.
    23. Luís Aguiar-Conraria & Maria Joana Soares, 2014. "The Continuous Wavelet Transform: Moving Beyond Uni- And Bivariate Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 344-375, April.
    24. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    25. Li, Xiao-Lin & Chang, Tsangyao & Miller, Stephen M. & Balcilar, Mehmet & Gupta, Rangan, 2015. "The co-movement and causality between the U.S. housing and stock markets in the time and frequency domains," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 220-233.
    26. Maghyereh, Aktham I. & Abdoh, Hussein & Awartani, Basel, 2019. "Connectedness and hedging between gold and Islamic securities: A new evidence from time-frequency domain approaches," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 13-28.
    27. Maghyereh, Aktham I. & Awartani, Basel & Abdoh, Hussein, 2019. "The co-movement between oil and clean energy stocks: A wavelet-based analysis of horizon associations," Energy, Elsevier, vol. 169(C), pages 895-913.
    28. Novy-Marx, Robert, 2014. "Predicting anomaly performance with politics, the weather, global warming, sunspots, and the stars," Journal of Financial Economics, Elsevier, vol. 112(2), pages 137-146.
    29. Yue-Jun Zhang & Shu-Hui Li, 2019. "The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1357-1371, August.
    30. Khalfaoui, R & Boutahar, M, 2012. "Portfolio risk evaluation: An approach based on dynamic conditional correlations models and wavelet multiresolution analysis," MPRA Paper 41624, University Library of Munich, Germany.
    31. Alex Edmans & Diego García & Øyvind Norli, 2007. "Sports Sentiment and Stock Returns," Journal of Finance, American Finance Association, vol. 62(4), pages 1967-1998, August.
    32. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    33. Smales, Lee A., 2014. "News sentiment and the investor fear gauge," Finance Research Letters, Elsevier, vol. 11(2), pages 122-130.
    34. Jessen L. Hobson & William J. Mayew & Mohan Venkatachalam, 2012. "Analyzing Speech to Detect Financial Misreporting," Journal of Accounting Research, Wiley Blackwell, vol. 50(2), pages 349-392, May.
    35. Patrick M. Crowley, 2007. "A Guide To Wavelets For Economists," Journal of Economic Surveys, Wiley Blackwell, vol. 21(2), pages 207-267, April.
    36. Chau, Frankie & Deesomsak, Rataporn & Koutmos, Dimitrios, 2016. "Does investor sentiment really matter?," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 221-232.
    37. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    38. Lepori, Gabriele M., 2016. "Air pollution and stock returns: Evidence from a natural experiment," Journal of Empirical Finance, Elsevier, vol. 35(C), pages 25-42.
    39. Han, Liyan & Lv, Qiuna & Yin, Libo, 2017. "Can investor attention predict oil prices?," Energy Economics, Elsevier, vol. 66(C), pages 547-558.
    40. Dror Y. Kenett & Xuqing Huang & Irena Vodenska & Shlomo Havlin & H. Eugene Stanley, 2015. "Partial correlation analysis: applications for financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 15(4), pages 569-578, April.
    41. Dowling, Michael & Cummins, Mark & Lucey, Brian M., 2016. "Psychological barriers in oil futures markets," Energy Economics, Elsevier, vol. 53(C), pages 293-304.
    42. Wang, Jue & Athanasopoulos, George & Hyndman, Rob J. & Wang, Shouyang, 2018. "Crude oil price forecasting based on internet concern using an extreme learning machine," International Journal of Forecasting, Elsevier, vol. 34(4), pages 665-677.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maghyereh, Aktham & Abdoh, Hussein, 2020. "The tail dependence structure between investor sentiment and commodity markets," Resources Policy, Elsevier, vol. 68(C).
    2. Mohammad Al-Shboul & Aktham Maghyereh, 2023. "Did real economic uncertainty drive risk connectedness in the oil–stock nexus during the COVID-19 outbreak? A partial wavelet coherence analysis," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 12(1), pages 1-23, December.
    3. Zhuhua Jiang & Rim El Khoury & Muneer M. Alshater & Seong‐Min Yoon, 2024. "Impact of global macroeconomic factors on spillovers among Australian sector markets: Fresh findings from a wavelet‐based analysis," Australian Economic Papers, Wiley Blackwell, vol. 63(1), pages 78-105, March.
    4. Wang, Lu & Ma, Feng & Niu, Tianjiao & Liang, Chao, 2021. "The importance of extreme shock: Examining the effect of investor sentiment on the crude oil futures market," Energy Economics, Elsevier, vol. 99(C).
    5. Wang, Xinghua & Lee, Zhengzheng & Wu, Shuang & Qin, Meng, 2023. "Exploring the vital role of geopolitics in the oil market: The case of Russia," Resources Policy, Elsevier, vol. 85(PB).
    6. Qadan, Mahmoud & Idilbi-Bayaa, Yasmeen, 2021. "The day-of-the-week-effect on the volatility of commodities," Resources Policy, Elsevier, vol. 71(C).
    7. Gaies, Brahim & Nakhli, Mohamed Sahbi & Sahut, Jean-Michel & Schweizer, Denis, 2023. "Interactions between investors’ fear and greed sentiment and Bitcoin prices," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Lu & Ma, Feng & Niu, Tianjiao & Liang, Chao, 2021. "The importance of extreme shock: Examining the effect of investor sentiment on the crude oil futures market," Energy Economics, Elsevier, vol. 99(C).
    2. Maghyereh, Aktham & Abdoh, Hussein, 2020. "The tail dependence structure between investor sentiment and commodity markets," Resources Policy, Elsevier, vol. 68(C).
    3. Chen, Qitong & Zhu, Huiming & Yu, Dongwei & Hau, Liya, 2022. "How does investor attention matter for crude oil prices and returns? Evidence from time-frequency quantile causality analysis," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    4. Chu, Xiaojun & Wan, Xinmin & Qiu, Jianying, 2023. "The relative importance of overnight sentiment versus trading-hour sentiment in volatility forecasting," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    5. Gong, Xue & Zhang, Weiguo & Wang, Junbo & Wang, Chao, 2022. "Investor sentiment and stock volatility: New evidence," International Review of Financial Analysis, Elsevier, vol. 80(C).
    6. Zhang, Yue-Jun & Li, Zhao-Chen, 2021. "Forecasting the stock returns of Chinese oil companies: Can investor attention help?," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 531-555.
    7. Obaid, Khaled & Pukthuanthong, Kuntara, 2022. "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, vol. 144(1), pages 273-297.
    8. Wang, Wenzhao & Duxbury, Darren, 2021. "Institutional investor sentiment and the mean-variance relationship: Global evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 415-441.
    9. Yue-Jun Zhang & Shu-Hui Li, 2019. "The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1357-1371, August.
    10. He, Zhifang, 2020. "Dynamic impacts of crude oil price on Chinese investor sentiment: Nonlinear causality and time-varying effect," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 131-153.
    11. Liya Chu & Xue-Zhong He & Kai Li & Jun Tu, 2022. "Investor Sentiment and Paradigm Shifts in Equity Return Forecasting," Management Science, INFORMS, vol. 68(6), pages 4301-4325, June.
    12. Li, Sufang & Xu, Qiufan & Lv, Yixue & Yuan, Di, 2022. "Public attention, oil and gold markets during the COVID-19: Evidence from time-frequency analysis," Resources Policy, Elsevier, vol. 78(C).
    13. Kim, Jun Sik & Ryu, Doojin & Seo, Sung Won, 2014. "Investor sentiment and return predictability of disagreement," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 166-178.
    14. Gong, Xue & Ye, Xin & Zhang, Weiguo & Zhang, Yue, 2023. "Predicting energy futures high-frequency volatility using technical indicators: The role of interaction," Energy Economics, Elsevier, vol. 119(C).
    15. Li, Sufang & Zhang, Hu & Yuan, Di, 2019. "Investor attention and crude oil prices: Evidence from nonlinear Granger causality tests," Energy Economics, Elsevier, vol. 84(C).
    16. Qingxiang Han & Mengxi He & Yaojie Zhang & Muhammad Umar, 2023. "Default return spread: A powerful predictor of crude oil price returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1786-1804, November.
    17. Liang, Chao & Xu, Yongan & Wang, Jianqiong & Yang, Mo, 2022. "Whether dimensionality reduction techniques can improve the ability of sentiment proxies to predict stock market returns," International Review of Financial Analysis, Elsevier, vol. 82(C).
    18. Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).
    19. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
    20. Qadan, Mahmoud & Aharon, David Y. & Cohen, Gil, 2020. "Everybody likes shopping, including the US capital market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).

    More about this item

    Keywords

    Co-movement; Crude oil; Emotions sentiments; Wavelet analysis;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:inteco:v:162:y:2020:i:c:p:110-124. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/21107017 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.