Skip to main content
Log in

The thermal optimal path model: Does Google search queries help to predict dynamic relationship between investor’s sentiment and indexes returns?

  • Original Article
  • Published:
Journal of Asset Management Aims and scope Submit manuscript

Abstract

This paper sheds lights on the predictive power of googling investor sentiment on MENA market indexes returns from 2004 to 2018 by using a novel approach based on the thermal optimal path model, Diebold–Yilmaz Spillover Indexes and the wavelet coherence model. Thermal optimal path reveals that googling investor sentiment exhibits a lead effect for Islamic and conventional indexes returns which is influenced by political, social and economic conditions. Using Diebold–Yilmaz Spillover Indexes, we find that googling investor’s sentiment is the main net transmitter of shocks to Saudi Arabia, Egypt, Qatar, Bahrain, Oman and Jordan Islamic market indexes and the majority of conventional indexes. Further, the wavelet coherence model confirms the thermal optimal path results of the leading effect of googling investor sentiment, especially during crises periods. These results depict the robust and significant predictive power of googling investor’s sentiment to detect the behavior of investor, especially during instability periods and to predict market returns in MENA financial markets. Consequently, investor can exploit googling investor sentiment measure in portfolio investments and trading strategy in MENA financial markets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Abdelhedi, M., and M. Boujelbène-Abbes. 2019. Transmission of shocks between Chinese financial market and oil market. International Journal of Emerging Markets 15 (2): 262–286. https://doi.org/10.1108/IJOEM-07-2017-0244.

    Article  Google Scholar 

  • Abdelhédi-Zouch, M., Abbes, M.B. and Boujelbène, Y. (2015) Volatility spillover and investor sentiment: Subprime crisis. Asian Academy of Management Journal of Accounting and Finance 11(2):83–101.

    Google Scholar 

  • Aloui, C., Hkiri, B., Lau, C.K.M. and Yarovaya, L. (2016) Investors’ sentiment and US Islamic and conventional indexes nexus: A time–frequency analysis. Finance Research Letters 19:54–59.

    Google Scholar 

  • Antweiler, W. and Frank, M.Z. (2004). Is all that talk just noise? The information content of internet stock message boards. Journal of Finance 59(3):1259–1294.

    Google Scholar 

  • Anusakumar, S.V., Ali, R. and Hooy, C.W. (2017) The effect of investor sentiment on stock returns: Insight from emerging asian markets. Asian Academy of Management Journal of Accounting and Finance 13(1):159–178.

    Google Scholar 

  • Baker, M., and Wurgler, J. (2006) Investor’s sentiment and the cross-section of stock returns. The Journal of Finance 61:1645–1660.

    Google Scholar 

  • Banholzer, N., Heiden, S. and Schneller, D. (2018) Exploiting investor sentiment for portfolio optimization. Business Research. pp 1–32| Cite as.

  • Bathia, D., Bredin, D. and Nitzsche, D. (2016) International sentiment spillovers in equity returns. International Journal of Finance & Economics 21(4):332–359.

    Google Scholar 

  • Beran, R. 1988. Prepivoting test statistics: A bootstrap view of asymptotic refinements. Journal of the American Statistical Association 83: 687–697.

    Google Scholar 

  • Bijl, L., Kringhaug, G., Molnár, P. and Sandvik, E. (2016) Google searches and stock returns. International Review of Financial Analysis 45:150–156.

    Google Scholar 

  • Brown, G., and M. Cliff. 2004. Investor sentiment and the near-term stock market. Journal of Empirical Finance 11: 1–27.

    Google Scholar 

  • Brown, G.W. and Cliff, M.T. (2005) Investor sentiment and asset valuation. Journal of Business 78(2):405–440.

    Google Scholar 

  • Bu, H., and L. Pi. 2014. Does investor sentiment predict stock returns? The evidence from Chinese stock market. Journal of Systems Science and Complexity 27 (1): 130–143.

    Google Scholar 

  • Cheema, M.A., Y. Man, K.R. Szulczyk. 2018. Does investor sentiment predict the near-term returns of the chinese stock market? International Review of Finance. https://doi.org/10.2139/ssrn.3078449

    Article  Google Scholar 

  • Chen, M.-P., Chen, P.-F. and Lee, C.-C. (2014) Asymmetric effects of investor sentiment on industry stock returns: Panel data evidence. Emerging Markets Review 14:35–54.

    Google Scholar 

  • Da, Z., Joseph, E. and Pengjie, G. (2011) In search of attention. The Journal of Finance 66(5):1461–1499.

    Google Scholar 

  • Da, Z., Joseph, E. and Pengjie, G. (2015) The sum of all fears investor sentiment and asset prices. Review of Financial Studies 28:1–32.

    Google Scholar 

  • Diebold, F.X. and Yilmaz, K. (2012) Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting 28(1):57–66.

    Google Scholar 

  • Fisher, Kenneth L. and Statman. Meir (2003) Consumer confidence and stock returns. Journal of Portfolio Management 30:115–127.

    Google Scholar 

  • Fu, Chengbo, Gady, J. and Yan, W. (2015) Investor sentiment and portfolio selection. Finance Research Letters 15(issue C):266–273.

    Google Scholar 

  • Ghorbel, A., Zouch, M. Abdelhédi and Boujelbène, Y. (2014) Assessing the impact of crude oil price and investor sentiment on Islamic Indices: Subprime crisis. Journal of African Business 15(1):13–24.

    Google Scholar 

  • Gong, C.-C., Ji, S.-D., Su, L.-L., Li, S.-P. and Ren, F. (2016) The lead-lag relationship between stock index and stock index futures: A thermal optimal path method. Physica A 444:63–72.

    Google Scholar 

  • Guo, K., Sun, Y. and Qian, X. (2017) Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market. Physica A: Statistical Mechanics and its Applications 469:390–396.

    Google Scholar 

  • Huang, D., F. Jiang, J. Tu, and G. Zhou. 2014. Investor sentiment aligned: A powerful predictor. Review of Financial Studies 28: 791–837.

    Google Scholar 

  • Hudson, Y. and Green, C.J. (2015) Is investor sentiment contagious? International sentiment and UK equity returns. Journal of Behavioral and Experimental Finance 5:46–59.

    Google Scholar 

  • Hui, P.U. and Li P.I. (2014) Does investor sentiment predict stock returns? The evidence from Chinese Stock Market. The Journal of Systems Science and Complexity 27:130–143.

    Google Scholar 

  • Inagaki, K. 2007. Testing for volatility spillover between the British pound and the euro. Research in International Business and Finance 21 (2): 161–174.

    Google Scholar 

  • Jaziri, M. and Abdelhedi, M. (2018) Islamic occasions and investor sentiment. International Journal of Islamic and Middle Eastern Finance and Management 11(2):194–212.

    Google Scholar 

  • Karabulut, Y. (2013) Can Facebook Predict Stock Market Activity? AFA 2013 San Diego Meetings Paper.

  • Kling, G. and Gao, L. (2008) Chinese institutional investors’ sentiment. Journal of International Financial Market and Money 18:374–387.

    Google Scholar 

  • Li, H., Guo, Y., Park, S.Y. (2017) Asymmetric relationship between investors’ sentiment and stock returns: Evidence from a quantile non-causality test. International Review of Finance 17(4):617–626.

    Google Scholar 

  • Li, Xin, Bing, Lidong, Lam, Wai and Shi, Bei (2018) Transformation networks for target oriented sentiment classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics.

  • Liston-Perez, D., Torres-Palacio, P. and Gulfem Bayram, S. (2018) Does investor sentiment predict Mexican equity returns? International Journal of Managerial Finance 14(4):484–502. https://doi.org/10.1108/IJMF-05-2017-0088.

    Article  Google Scholar 

  • Ma, C., S. Xiao, and Z. Ma. 2018. Investor sentiment and the prediction of stock returns: a quantile regression approach. Applied Economics 50 (50): 5401–5415.

    Google Scholar 

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

  • Mao, Y., Wang, B., Wei, W. and Liu, B. (2012) Correlating S&P 500 stocks with twitter data. In Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research, New York. 12–16, 69–72

  • Mehwish, A.K. and Eatzaz, A. (2018) Measurement of investor sentiment and its bi-directional contemporaneous and lead–lag relationship with returns: Evidence from Pakistan. Sustainability 11(1):94.

    Google Scholar 

  • Naifar, N. (2016) Do global risk factors and macroeconomic conditions affect global Islamic index dynamics? A quantile regression approach. The Quarterly Review of Economics and Finance 61:29–39.

    Google Scholar 

  • Namouri, H., Jawadi, F., Ftiti, Z. and Hachicha, N. (2017) Threshold effect in the relationship between investor sentiment and stock market returns: A PSTR specification. Applied Economics 50(5):559–573.

    Google Scholar 

  • Nikolakakis, K., S. Amber, J.S. Wilbur, E.J. Diner, S.K. Aoki, S.J. Poole, and D.A. Low. 2012. The toxin/immunity network of Burkholderia pseudomallei contact-dependent growth inhibition (CDI) systems. Molecular Microbiology 84 (3): 516–529.

    Google Scholar 

  • Perez-Liston, D., Huerta, D. and Haq, S. (2014) Does investor sentiment impact the returns and volatility of Islamic equities? Journal of Economics and Finance 40(3):421–437.

    Google Scholar 

  • Pesaran, M.H. and Shin, Y. (1998) Generalized impulse response analysis in linear multivariate models. Economics Letters 58(1):17–29.

    Google Scholar 

  • Qiu, L.X. and Welch, I. (2006) Investor sentiment measures. NBER Working Paper.

  • Rupande, L., M. Hilary Tintenda, H.T. Muguto, and P.F. Muzindutsi. 2019. Investor sentiment and stock return volatility: Evidence from the Johannesburg Stock Exchange. Cogent Economics and Finance 7 (1): 1600233.

    Google Scholar 

  • Schmeling, M. (2009) Investor sentiment and stock returns: Some international evidence. Journal of Empirical Finance 16:394–408.

    Google Scholar 

  • Siganos, A., Vagenas-Nanos, E. and Verwijmeren, P. (2014) Facebook’s daily sentiment and international stock markets. Journal of Economic Behavior & Organization 107:730–743.

    Google Scholar 

  • Solt, Michael E. and Statman. Meir (1988) How useful is the Sentiment Index? Financial Analysts Journal 44:45–55.

    Google Scholar 

  • Sornette, D. and Zhou, W.-X. (2005) Non-parametric determination of real-time lag structure between two time series: The “optimal thermal causal path” method. Quantitative Finance 5:577–591.

    Google Scholar 

  • Souza, T.T.P., Kolchyna, O., Treleaven, P.C. and Aste, T. (2015) Twitter Sentiment Analysis Applied to Finance: A Case Study in the Retail Industry. arXiv preprint.

  • Tetlock, P.C. (2007) Giving content to investor sentiment: The role of media in the stock market. Journal of Finance 62:1139–1168.

    Google Scholar 

  • Tiwari, A., Dar, A. and Bhanja, N. (2013) Oil price and exchange rates: A wavelet based analysis for India. Economic Modelling 31:414–416.

    Google Scholar 

  • Torrence, C., and Webster, P.J. 1999. Interdecadal changes in the ensomonsoon system. Journal of Climate 12: 2679–2690.

    Google Scholar 

  • Wilder, W.J. (1978) New Concepts in Technical Trading Systems. Greensboro, NC: Trend Research.

    Google Scholar 

  • Xu, H., Zhou, W. and Sornette, D. (2017) Time-dependent lead-lag relationship between the onshore and offshore Renminbi exchange rates. Journal of International Financial Markets, Institutions and Money 49:173–183.

    Google Scholar 

  • Yao, C.Z., Kuang, P.C. and Lin, J.N. (2018a) The optimal thermal causal path analysis on the relationship between international crude oil price and stock market. Kybernetes. https://doi.org/10.1108/K-04-2017-0139.

    Article  Google Scholar 

  • Yao, J., Zhang, Q., Ye, X., Zhang, D. and Bai, P. (2018b) Quantifying the impact of bathymetric changes on the hydrological regimes in a large floodplain lake: Poyang Lake. Journal of Hydrology 561:711–723.

    Google Scholar 

  • Yu, J. and Y. Yuan. 2011. Investor sentiment and the mean–variance relation. Journal of Financial Economics 100 (2): 367–381.

    Google Scholar 

  • Zhang, Q. and S. Yang. 2009. Noise trading, investor sentiment volatility, and stock returns. Systems Engineering-Theory & Practice 29 (3): 40–47.

    Google Scholar 

  • Zhou, W.-X. and D. Sornette. 2007. Lead-lag cross-sectional structure and detection of correlated-anticorrelated regime shifts: Application to the volatilities of inflation and economic growth rates. Physica A 380: 287–296.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Yousra Trichilli or Mouna Abdelhédi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Trichilli, Y., Abdelhédi, M. & Boujelbène Abbes, M. The thermal optimal path model: Does Google search queries help to predict dynamic relationship between investor’s sentiment and indexes returns?. J Asset Manag 21, 261–279 (2020). https://doi.org/10.1057/s41260-020-00159-0

Download citation

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/s41260-020-00159-0

Keywords

Navigation