The Fama-French multifactor model with market and Pandemic news fear sentimentsa test in the Mexican stock markets

  1. Oscar Valdemar De la Torre-Torres 1
  2. Evaristo Galeana Figueroa 1
  3. Mª. De la Cruz Del Río-Rama 2
  1. 1 Universidad Michoacana de San Nicolás de Hidalgo Morelia, México
  2. 2 Universidad de Vigo, España
Revista:
Contaduría y administración

ISSN: 0186-1042,  2448-8410

Ano de publicación: 2021

Título do exemplar: lecciones de la pandemia de Covid-2019

Volume: 66

Número: 5

Tipo: Artigo

DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Outras publicacións en: Contaduría y administración

Resumo

In the present paper, we test the extension of the Fama-French (FF) three-factor model by including Economic, stock market, and Pandemic news uncertainty. For this purpose, we used either Global news or social media (Twitter) sentiment indexes, along with Mexican and U.S. implied volatility (VIX) ones. Using robust panel data regression models in the 72 most traded and biggest companies in the Mexican stock markets from 2017 to 2021, we found that only the Mexican VIX index is helpful to extend the FF model. Contrary to our expectations, the social media and news sentiment indexes have a negligible impact on stock price formation. These results suggest that developing more appropriate sentiment indexes is an essential need in the Mexican stock markets.

Referencias bibliográficas

  • Ahir, H., Bloom, N., & Furceri, D. (2018). The World Uncertainty Index. https://ssrn.com/abstract=3275033
  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1016/J.CUB.2017.09.001
  • Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross-section of volatility and expected returns. Journal of Finance, 61(1), 259–299. https://doi.org/10.1111/J.1540- 6261.2006.00836.X
  • Armendáriz, T., & Ramírez, C. (2017). Estimación de un índice de condiciones financieras para México. El Trimestre Económico, 4(336), 899–946.
  • Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645–1680. https://doi.org/10.1111/J.1540-6261.2006.00885.X
  • Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129–151. https://doi.org/10.1257/jep.21.2.129
  • Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring Economic Policy Uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024
  • Baker, S. R., Bloom, N., Davis, S. J., & Renault, T. (2021). Twitter-Derived Measures of Economic Uncertainty. http://www.policyuncertainty.com/media/Twitter_Uncertainty_5_13_2021.pdf
  • Balcilar, M., Demirer, R., & Hammoudeh, S. (2013). Investor herds and regime-switching: Evidence from Gulf Arab stock markets. Journal of International Financial Markets, Institutions & Money, 23, 295–321. https://doi.org/10.1016/j.intfin.2012.09.007
  • Black, F. (1986). Noise. The Journal of Finance, 41(3), 529–543. https://doi.org/10.1111/J.1540- 6261.1986.TB04513.X
  • Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28– 43. https://doi.org/10.2469/faj.v48.n5.28
  • Bu, Q. (2019). Investor Sentiment and Mutual Fund Alpha. Journal of Behavioral Finance, 21(1), 57– 65. https://doi.org/10.1080/15427560.2019.1594814
  • Bu, Q. (2021). Mutual Fund Alpha: Is It Managerial or Emotional? Journal of Behavioral Finance, 22(1), 46–55. https://doi.org/10.1080/15427560.2020.1716361
  • Cambón, M. I., & Estévez, L. (2016). A Spanish Financial Market Stress Index (FMSI). The Spanish Review of Financial Economics, 14(1), 23–41. https://doi.org/10.1016/j.srfe.2016.01.002
  • Carhart, M. M. (1997). On Persistence in Mutual Fund Performance. The Journal of Finance, LII(1), 57– 82.
  • Chen, C.-D., Chen, C.-C., Tang, W.-W., & Huang, B.-Y. (2009). The Positive and Negative Impacts of the Sars Outbreak: A Case of the Taiwan Industries. The Journal of Developing Areas, 43(1), 281–293.
  • Da, Z., Engelberg, J., & Gao, P. (2015). The Sum of All FEARS Investor Sentiment and Asset Prices. Review of Financial Studies, 28(1), 1–32. https://doi.org/10.1093/rfs/hhu072
  • Daniel, K., Hirshleifer, D., & Teoh, S. H. (2002). Investor psychology in capital markets: evidence and policy implications. Journal of Monetary Economics, 49(1), 139–209. https://doi.org/10.1016/S0304-3932(01)00091-5
  • De Bondt, W. F. M., & Thaler, R. (1985). Does the Stock Market Overreact? The Journal of Finance, 40(3), 793–805. https://doi.org/10.1111/J.1540-6261.1985.TB05004.X
  • De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise Trader Risk in Financial Markets. Journal of Political Economy, 98(4), 703–738. https://doi.org/10.1086/261703
  • Ding, C. G., Wang, H.-J., Lee, M.-C., Hung, W.-C., & Lin, C.-P. (2014). How Does the Change in Investor Sentiment over Time Affect Stock Returns? Emerging Markets Finance and Trade, 50(sup2), 144–158. https://doi.org/10.2753/REE1540-496X5002S210
  • Durand, R. B., Lim, D., & Zumwalt, J. K. (2011). Fear and the Fama-French Factors. Financial Management, 40(2), 409–426. https://doi.org/10.1111/J.1755-053X.2011.01147.X
  • Fama, E. (1965). The behavior of stock-market prices. Journal of Business, 38(1), 34–105.
  • Fama, E. F., & French, K. R. (1992). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56.
  • González, J., & Ortiz, E. (2020). Testing the overreaction hypothesis in the Mexican stock market. Contaduría y Administración, 65(1), 1–23. https://doi.org/http://dx.doi.org/10.22201/fca.24488410e.2019.1794
  • Griffith, J., Najand, M., & Shen, J. (2020). Emotions in the Stock Market. Journal of Behavioral Finance, 21(1), 42–56. https://doi.org/10.1080/15427560.2019.1588275
  • Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271. https://doi.org/10.2307/1913827
  • Kahneman, D., & Riepe, M. W. (1998). Aspects of Investor Psychology. The Journal of Portfolio Management, 24(4), 52–65. https://doi.org/10.3905/jpm.1998.409643
  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrika, 47, 263-291. Econometrica, 47(2), 263–292. https://doi.org/10.2307/1914185
  • Koo, B., Chae, J., & Kim, H. (2018). Does Internet Search Volume Predict Market Returns and Investors’ Trading Behavior? Journal of Behavioral Finance, 20(3), 316–338. https://doi.org/10.1080/15427560.2018.1511561
  • Li, J. (2015). The asymmetric effects of investor sentiment and monetary policy on stock prices. Applied Economics, 47(24), 2514–2522. https://doi.org/10.1080/00036846.2015.1008770
  • Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statistics, 47(1), 13–37.
  • Liu, L., & Zhang, T. (2015). Economic policy uncertainty and stock market volatility. Finance Research Letters, 15, 99–105. https://doi.org/10.1016/j.frl.2015.08.009
  • López-Cabarcos, M. Á., Pérez-Pico, A. M., Vázquez-Rodríguez, P., & López-Pérez, M. L. (2020). Investor sentiment in the theoretical field of behavioural finance. Economic ResearchEkonomska Istrazivanja , 33(1), 2101–2119. https://doi.org/10.1080/1331677X.2018.1559748
  • Luo, C., Seco, L., & Wu, L.-L. B. (2015). Portfolio optimization in hedge funds by OGARCH and Markov Switching Model. Omega, 57, 34–39. https://doi.org/10.1016/j.omega.2015.01.021
  • Mehra, R., & Prescott, E. C. (1985). The equity premium a Puzzle. In Journal of Monetary Economics (Vol. 15).
  • Newey, W. K., & West, K. D. (1987). A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica, 55(3), 703–708.
  • Nikkinen, J., & Peltomäki, J. (2019). Crash Fears and Stock Market Effects: Evidence From Web Searches and Printed News Articles. Journal of Behavioral Finance, 21(2), 117–127. https://doi.org/10.1080/15427560.2019.1630125
  • Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using google trends. Scientific Reports, 3, 1684. https://doi.org/10.1038/SREP01684
  • Raddatz, C., & Schmukler, S. L. (2012). Deconstructing Herding: Evidence from Pension Fund Investment Behavior. Journal of Financial Services Research, 43(1), 99–126. https://doi.org/10.1007/s10693-012-0155-x
  • Ross, S. (1976). Return, Risk and Arbitrage. In I. Friend & J. Bicksler (Eds.), Risk and Return in Finance (pp. 189–218). Ballinger.
  • Rotta, P. N., & Valls Pereira, P. L. (2016). Analysis of contagion from the dynamic conditional correlation model with Markov Regime switching. Applied Economics, 48(25), 2367–2382. https://doi.org/10.1080/00036846.2015.1119794
  • Sharpe, W. (1963). A simplified model for portfolio analysis. Management Science, 9(2), 277–293.
  • Sharpe, W. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, XIX(3), 425–442.
  • Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83–104. https://doi.org/10.1257/089533003321164967
  • Shiller, R. J. (2014). Speculative asset prices. American Economic Review, 104(6), 1486–1517. https://doi.org/10.1257/aer.104.6.1486
  • Simon, D. P., & Wiggins III, R. A. (2001). S&P futures returns and contrary sentiment indicators. Journal of Futures Markets, 21(5), 447–462. https://doi.org/10.1002/FUT.4
  • Smales, L.A. (2017). The importance of fear: investor sentiment and stock market returns. Applied Economics, 49(34), 3395–3421. https://doi.org/10.1080/00036846.2016.1259754
  • Smales, Lee A. (2016). The role of political uncertainty in Australian financial markets. Accounting & Finance, 56(2), 545–575. https://doi.org/10.1111/acfi.12107
  • Swamy, P. A. V. B., & Arora, S. S. (1972). The Exact Finite Sample Properties of the Estimators of Coefficients in the Error Components Regression Models. Econometrica, 40(2), 261. https://doi.org/10.2307/1909405
  • Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458. https://doi.org/10.1126/SCIENCE.7455683
  • Uhl, M. W. (2014). Reuters Sentiment and Stock Returns. Journal of Behavioral Finance, 15(4), 287– 298. https://doi.org/10.1080/15427560.2014.967852
  • Viebig, J. (2020). Exuberance in Financial Markets: Evidence from Machine Learning Algorithms. Journal of Behavioral Finance, 21(2), 128–135. https://doi.org/10.1080/15427560.2019.1663849
  • Walid, C., & Nguyen, D. K. (2014). Exchange rate movements and stock market returns in a regimeswitching environment: Evidence for BRICS countries. Research in International Business and Finance, 31, 46–56. https://doi.org/10.1016/j.ribaf.2013.11.007
  • Wang, C. (2001). Investor Sentiment and Return Predictability in Agricultural Futures Markets. Journal of Futures Markets, 21(10), 929–952. https://doi.org/10.1002/fut.2003
  • Wang, C. (2003). Investor sentiment, market timing, and futures returns. Applied Financial Economics, 13(12), 891–898. https://doi.org/10.1080/0960310032000129653
  • Wolff, A. F. (2013). Investor sentiment and stock prices in the subprime mortgage crisis. Applied Financial Economics, 23(16), 1301–1309. https://doi.org/10.1080/09603107.2013.804163
  • Wu, P.-C., Liu, S.-Y., & Chen, C.-Y. (2016). Re-examining risk premiums in the Fama–French model: The role of investor sentiment. North American Journal of Economics and Finance, 36, 154– 171. https://doi.org/10.1016/j.najef.2015.12.002
  • Ye, W., Zhu, Y., Wu, Y., & Miao, B. (2016). Markov regime-switching quantile regression models and financial contagion detection. Insurance: Mathematics and Economics, 67, 21–26. https://doi.org/10.1016/j.insmatheco.2015.11.002
  • Zheng, Y. (2014). The linkage between aggregate stock market investor sentiment and commodity futures returns. Applied Financial Economics, 24(23), 1491–1513. https://doi.org/10.1080/09603107.2014.925073
Fonte de datos: Dialnet