UM MODELO ECONOMÉTRICO PAINEL-MIDAS DOS RETORNOS DOS ATIVOS DO MERCADO ACIONÁRIO BRASILEIRO

Resumo

Apresentamos a especificação, estimação e análise de um modelo econométrico para explicar e projetar os retornos das ações do mercado acionário brasileiro. As variáveis explicativas do modelo incluem variáveis macroeconômicas, fundamentalistas e comportamentais amostradas em diferentes frequências. O modelo utiliza a metodologia de regressão MIDAS, que permite a estimação de regressões com variáveis mensuradas em diferentes frequências. A amostra contempla as ações das instituições não financeiras do mercado acionário brasileiro entre 2010 e 2016. Os resultados indicam que o modelo é robusto em explicar e projetar os retornos individuais das ações listadas naquele mercado.

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Referências

Akaike, H. (1974), “A new look at the statistical model identification”. IEEE Transactions on Automatic Control. 19(6):716-723. doi:10.1109/TAC.1974.1100705.

_________. (1976), “Canonical Correlation Analysis of Time Series and the Use of an Information Criterion”. Mathematics in Science and Engineering. 126:27-96. doi:10.1016/S0076-5392(08)60869-3.

Andreou, E.(2016). “On the use of high frequency measures of volatility in MIDAS regressions”. Journal of Econometrics, 193(2): 367–389.doi:10.1016/j.jeconom.2016.04.012.

Andreou, E.; Ghysels, E; Kourtellos, A. (2010), “Regression models with mixed sampling frequencies”. Journal of Econometrics. 158(2):246-261. doi:10.1016/j.jeconom.2010.01.004.

_______________________________. (2013), “Should Macroeconomic Forecasters Use Daily Financial Data and How?” Journal of Business & Economic Statistics. 31(2):240-251. doi:10.1080/07350015.2013.767199.

Antonakakis, N.; Gupta, R.; Tiwari, A.K. (2017), Has the correlation of inflation and stock prices changed in the United States over the last two centuries? Research in International Business and Finance. 42:1-8. doi:10.1016/j.ribaf.2017.04.005

Baltagi, B.H. (2005), “Econometric Analysis of panel data”. 3. ed. Chichester: John Wiley&Sons.

Banz, R.W. (1981), “The relationship between return and market value of common stocks”. Journal of Financial Economics. 9(1):3-18. doi:10.1016/0304-405X(81)90018-0.

Brown, G.W.; Cliff, M.T. (2005), “Investor sentiment and asset valuation”. The Journal of Business. 78(2):405-440. doi:10.1086/427633.

Campbell, J.Y.; Thompson, S.B. (2008), “Predicting excess stock returns out of sample: can anything beat the historical average?” Review of Financial Studies. 21(4):1509–1531. doi:10.1093/rfs/hhm055

Chambers, M.J. (2016), “The estimation of continuous time models with mixed frequency data”. Journal of Econometrics. 193(2):390-404. doi:10.1016/j.jeconom.2016.04.013.

Chen, P.; Zhang, G. (2007), “How do accounting variables explain stock price movements? Theory and evidence”. Journal of Accounting and Economics. 43(2-3):219–244. doi:10.1016/j.jacceco.2007.01.001.

Christou, C.; Cunado, J.; Gupta, R.; Hassapis, C. (2017), “Economic policy uncertainty and stock market returns in Pacific Rim countries: Evidence based on a Bayesian panel VAR model”. Journal of Multinational Financial Management. 40:92-102. doi:10.1016/j.mulfin.2017.03.001.

Diebold, F.X.; Mariano, R.S. (1995), “Comparing Predictive Accuracy”. Journal of Business and Economic Statistics. 13:253-265. doi:10.1198/073500102753410444.

Fama, E.F.; French, K.R. (1992), “The cross-section of expected stock returns”. Journal of Finance. 47(2):427-465. doi:10.2307/2329112.

___________________. (1993), “Common risk factors in the returns on stocks and bonds”. Journal of Financial Economics. 33(1):3-56. doi:10.1016/0304-405X(93)90023-5.

Ghysels, E.; Santa-Clara, P; Valkanov, R. (2004), “The MIDAS touch: Mixed data sampling regression models”. Cirano.Série scientifique.

________________________________. (2005), “There is a risk-return tradeoff after all”. Journal of Financial Economics. 76(3):509–548. doi:10.1016/j.jfineco.2004.03.008.

Ghysels, E.; Sinko, A.; Valkanov, R. (2007), “Midas regressions: Further results and new directions”. Econometric Reviews. 26(1):53–90. doi:10.1080/07474930600972467.

Hadhri, S; Ftiti, Z. (2017), “Stock return predictability in emerging markets: Does the choice of predictors and models matter across countries?” Research in International Business and Finance.42:39–60. doi:10.1016/j.ribaf.2017.04.057.

Hou, K.; Xue, C.; Zhang, L. (2015), “Digesting anomalies: An investment approach”. Review of Financial Studies. 28(3):650-705. doi:10.1093/rfs/hhu068.

Hannan, E.J.; Quinn, B.G. (1979), “The Determination of the Order of an Autoregression”. Journal of the Royal Statistical Society. 41(2):190-195. Doi:177.142.61.153.

Jegadeesh, N.; Titman, S. (1993), “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency”. The Journal of Finance. 48(1):65-91. doi:10.1111/j.1540-6261.1993.tb04702.x.

__________________. (2001), “Profitability of Momentum Strategies: An Evaluation of Alternative Explanations”. The Journal of Finance. 56(2):699-720. doi:10.1111/0022-1082.00342.

Kahneman, D.; Tversky, A. (1979), “Prospect Theory: An analysis of decision under risk”. Econometrica. 47(2):263-291. doi:10.2307/1914185.

Keim, D.B. (1983), “Size-related anomalies and stock return seasonality: Further empirical evidence”. Journal of Financial Economics. 12(1):13-32. doi:10.1016/0304-405X(83)90025-9.

Kuzin, V.; Marcellino, M; Schumacher, C. (2011). “MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area”. International Journal of Forecasting. 27(2):529-542. doi:10.1016/j.ijforecast.2010.02.006.

Li, X.; Shang, W.; Wang, S.; Ma, J. (2015). “A MIDAS modelling framework for Chinese inflation index forecast incorporating Google search data”. Electronic Commerce Research and Applications, 14(2):112–125.doi: 10.1016/j.elerap.2015.01.001.

Liston, D.P. (2016), “Sin stock returns and investor sentiment”. The Quarterly Review of Economics and Finance. 59:63-70. doi:10.1016/j.qref.2015.08.004.

Machado, M.A.V.; Medeiros, O.R. (2011), “Modelos de precificação de ativos e o efeito liquidez: evidências empíricas no mercado acionário brasileiro”. Revista Brasileira de Finanças. 9(3):383-412.

Marcellino, M.; Schumacher, C.C. (2010), “Factor-MIDAS for now-and forecasting with ragged-edge data: A model comparison for German GDP”. Oxford Bulletin of Economics and Statistics. 72(4):518-550. doi:10.1111/j.1468-0084.2010.00591.x.

Martins, O.S.; Paulo, E.; Albuquerque, P.H.M. (2013), “Negociação com informação privilegiada e retorno das ações na BM&FBovespa”. RAE. 53(4):350-362.

Markowitz, H. (1952), “Portfolio Selection”. The Journal of Finance. 7(1):77-91.

Mussa, A.; Famá, R., Santos, J.O. (2012). “A adição do fator de risco momento ao modelo de precificação de ativos dos três fatores de Fama & French aplicado ao mercado acionário brasileiro”. REGE. 19(3):431-447. doi:10.5700/issn.2177-8736.rege.2012.49925.

Myers, S.C. (1984), “The capital structure puzzle”. Journal of Finance. 39(3):575-592. doi:10.1111/j.1540-6261.1984.tb03646.x

Pan, Z.; Wang, Q.; Wang, Y.; Yang, L.(2018). “Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model”. Energy Economics. 72:177–187.doi: /10.1016/j.eneco.2018.04.008.

Rapach, D.E.; Strauss, J; Zhou, G.Z. (2010), “Out-of-sample equity premium prediction: Combination forecasts and links to the real economy”. Review of Financial Studies. 23(2): 821–862. doi:0.1093/rfs/hhp063.

Rouwenhorst, G.K. (1998), “International Momentum Strategies”. The Journal of Finance. 53(1):267-284. 1998. doi:10.1111/0022-1082.95722.

Schwarz, G. (1978), “Estimating the Dimension of a Model”. Annals of Statistics. 6(2):461-464.

Welch, I.; Goyal, A. (2008). “A comprehensive look at the empirical performance of equity premium prediction”. Review of Financial Studies. 21:1455–1508. doi:10.1093/rfs/hhm014.

Wink Jr., M.V.; Pereira, P.L.V. (2011) “Modeling and Forecasting of Realized Volatility: Evidence from Brazil”. Brazilian Review of Econometrics, 31(2):315–337.doi: 10.12660/bre.v31n22011.4056.

Yoshinaga, C.E.; Castro Jr. F.H.F. (2012), “The Relationship between Market Sentiment Index and Stock Rates of Return: a Panel Data Analysis”. BAR. 9(2):189-210.

Zhao, X.; Han, M.; Ding, L.; Calin, A.C.(2018). “Forecasting carbon dioxide emissions based on a hybrid of mixed data sampling regression model and back propagation neural network in the USA”. Environmental Science and Pollution Research. 25(3):2899–2910.doi: 10.1007/s11356-017-0642-6.