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Análise do índice preço-benefício ajustado ciclicamente nos portfólios do mercado acionário brasileiro, 2011-2019

Ronald Mauricio Martínez Contreras | Biografia
Politécnico Grancolombiano
Rubén Darío Martínez Amado | Biografia
Institución Universitaria Politécnico Grancolombiano
Rodrigo Atehortúa Santamaria | Biografia
Politécnico Grancolombiano
Nydia Consuelo Hernández Mora | Biografia
Politécnico Grancolombiano
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Resumo

Este artigo avalia as bondades do indicador preço-benefício ajustado ciclicamente para a construção de portfólios de inversão no mercado de ações brasileiro para o período 2011-2019. Para cumprir este objetivo tomou informação do valor das ações de trinta e três empresas que invertem na bolsa de valores do Brasil e se lhes aplica o índice para a construção de portfólios eficientes. O comportamento dos ativos financeiros que compõem ditos portfólios foi comparado com o índice Bovespa, e depois procedeu-se a calcular o valor de risco, com o objetivo de gerar portfólios de inversão com um risco equivalente ao da Bovespa. Apesar da existência de estudos de aplicação deste indicador em diversos mercados, são poucos os que se concentram no preço-benefício ajustado ciclicamente para a construção de portfólios de inversão e não se percebe a existência de análise desse tipo enfocado no mercado latino-americano, daí a importância desse trabalho. Como resultado, observou-se que o rendimento dos portfólios construído com essa metodologia supera a Bovespa em seis dos nove anos estudados, ademais, entre 2011 e 2019 os portfólios construídos geraram um rendimento 3,27 vezes maior ao da Bovespa.

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Referências

  1. Abuzayed, B., Bouri, E., Fayoumi, N. y Jalkh, N. (2021). Systemic Risk Spillover Across Global and Country Stock Markets During The COVID-19 Pandemic. Economic Analysis and Policy, 71(C), 180-197. https://doi.org/10.1016/j.eap.2021.04.010
  2. Ahmed, D., Soleymani, S., Zaka, M. y Hasan, H. (2021). Managing The Risk Based on Entropic Value-At-Risk Under a Normal-Rayleigh Distribution. Applied Mathematics and Computation, 402. https://doi.org/10.1016/j.amc.2021.126129
  3. Angelini, N., Bormetti, G., Marmi, S. y Nardini, F. (2018). Value Matters: The Long-Run Behavior of Stock Index Returns. Review of Economics and Finance, 12(2), 16-28. http://www.bapress.ca/ref/ref-article/1923-7529-2018-02-16-13.pdf
  4. Anott, R. K. (2018). CAPE Fear: Why CAPE Naysayers Are Wrong. Research affiliates. Banco Mundial. (2021). Crecimiento del PIB en (% anual) – Brazil. https://datos.bancomundial.org/indicator/NY.GDP.MKTP.KD.ZG?locations=BR
  5. Bolek, M., Pietraszewki, P. y Wolski, R. (2021). Companies’ Growth vs. Growth Opportunity: Evidence from the Regular and Alternative Stock Markets in Poland. Acta Oeconomica, 71(2), 279-307. https://doi.org/10.1556/032.2021.00014
  6. Bunn, O., Staal, A., Zhuang, J. Lazanas, A., Ural, C. y Shiller, R. (2014). Es-Cape-Ing from Overvalued Sectors: Sector Selection Based on The Cyclically Adjusted Price-Earnings (CAPE) Ratio. Journal of Portfolio Management, 41(1), 16-33. https://doi.org/10.3905/jpm.2014.41.1.016
  7. Clare, A., Seaton, J., Smith, P. y Thomas, S. (2017). Reducing Sequence Risk Using Trend Following and the CAPE Ratio. Financial Analysts Journal, 73(4), 1-13. https://doi.org/10.2469/faj.v73.n4.5
  8. Costa, C. T., Vieira da Silva, W., Brito de Almeida, L. y Pereira da Veiga, C. (2017). Empirical Evidence of the Existence of Speculative Bubbles in the Prices of Stocks Traded on the São
  9. Paulo Stock Exchange. Contaduría y Administración, 62(4), 1317-1334. https://doi.org/10.1016/j.cya.2017.02.007
  10. Cunha, F. M., Meira, E., Orsato, R., Cabus, M. y Lucena, A. (2021). Do Low-Carbon Investments in Emerging Economies Pay Off? Evidence From The Brazilian Stock Market. International Review of Financial Analysis, 74. https://doi.org/10.1016/j.irfa.2021.101700
  11. Dimitrov, V. y Jain, P. (2018). Shiller’s CAPE: Market Efficiency and Risk. Financial Review, 53(4), 741-771. https://doi.org/10.1111/fire.12167.
  12. Feldman, T., Jung, A. y Klein, J. (2015). Buy and Hold Versus Timing Strategies: The Winner Is... The Journal of Portfolio Management, 42(1), 110-118. https://doi.org/10.3905/jpm.2015.42.1.110
  13. Guang L., Yu, C. P., Shiu, N. y Shin, T. (2022). The Efficient Market Hypothesis and the Fractal Market Hypothesis: Interfluves, Fusions, and Evolutions. Sage Open, 12(1), 1-8. https://doi.org/10.1177/21582440221082137
  14. Kouaissah, N. (2021). Using Multivariate Stochastic Dominance to Enhance Portfolio Selection and Warn Of Financial Crises. The Quarterly Review of Economics and Finance, 80, 480-493. https://doi.org/10.1016/j.qref.2021.03.015
  15. Lu, Y., Li, M., Tang, X. y Wang, H. (2021). A Cluster Representative Selection Method for Stock Portfolio Based on Efficient Frontier. Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design, CSCWD. https://doi.org/10.1109/CSCWD49262.2021.9437767
  16. Mihai, S. (2015). Etfs Performance Europe - a Good Start Or Not? Procedia Economics and Finance, 30, 955-966. https://doi.org/10.1016/S2212-5671(15)01346-5
  17. Mustika, M., Mulyawati, T., Apriyanto, D. K., Sausan, A. y Rofikah. (2021). Application of Pascoletti- Serafini scalarization modification method to solve multi-objective optimization problems for stock portfolio. Journal of Physics: Conference Series, 1821(1). https://doi.org/10.1088/17426596/1821/1/012058
  18. Philips, T. y Kodor, A. (2020). Ultra-Simple Shiller’s CAPE: How One Year’s Data Can Predict Equity Market Returns Better Than Ten. Journal of Portfolio Management, 46(4), 140-155. https://doi.org/10.3905/jpm.2020.1.124
  19. Quiroga, E. (2017). Eficiencia en los mercados financieros y predicción de precios de los activos. Ciencias Administrativas, (10), 1-11. https://www.redalyc.org/journal/5116/511653854005/511653854005.pdf
  20. Sehgal, R. M. y Mehra, A. (2021). Robust Reward–Risk Ratio Portfolio Optimization. International Transactions in Operational Research, 28(4). https://doi.org/10.1111/itor.12652
  21. Sharma, C. S. y Sahni, N. (2021). A Mutual Information Based R-Vine Copula Strategy to Estimate Var in High Frequency Stock Market Data. PLoS ONE, 16(6). https://doi.org/10.1371/journal.pone.0253307
  22. Shiller, R. (2000). Irrational exuberance. Princeton University Press. http://www.library.fa.ru/files/Shiller2.pdf
  23. Siegel, J. (2016). The Shiller CAPE Ratio: A New Look. Financial Analysts Journal, 72(3), 41-50. https://doi.org/10.2469/faj.v72.n3.1
  24. Smith, S. (2015). Ossiam launches Shiller Barclays CAPE Sector Value Europe ETF. ETF Strategy. https://www.etfstrategy.com/ossiam-launches-shiller-barclays-cape-sector-value-europeetf-41259/
  25. Song, S., Tian, S. y Li, H. (2021). An Intraday-Return-Based Value-at-Risk Model Driven by Dynamic Conditional Score with Censored Generalized Pareto Distribution. Journal of Asian Economics, 74. https://doi.org/10.1016/j.asieco.2021.101314
  26. Straehl, P. y Bernstein, W. (2017). The Long-Run Drivers of Stock Returns: Total Payouts and the Real Economy. Financial Analysts Journal, 73(3), 1-21. https://doi.org/10.2469/faj.v74.n1.6
  27. Sulistianingsih, E., Rosadi, D. y Abdurakhman. (2021). Risk Analysis of Five Stocks Indexed by LQ45 Using Credible Value at Risk and Credible Expected Tail Loss. Journal of Physics: Conference Series, 1918(4). https://doi.org/10.1088/1742-6596/1918/4/042023
  28. Tan, R. (2021). Changes in The Portfolio Management And Construction Under The Pandemic Era.E3S Web of Conferences, 275. https://doi.org/10.1051/e3sconf/202127501005
  29. Tuarob, S., Wettayakorn, P., Phetchai, P., Traivijitkhun, S., Lim., S., Noraset, T. y Thaipisutikul,
  30. T. (2021). DAVIS: A Unified Solution for Data Collection, Analyzation, and Visualization in Real-Time Stock Market Prediction. Financial Innovation, 7(1). https://doi.org/10.1186/s40854-021-00269-7
  31. Waser, O. (2021). Modelling the shiller CAPE ratio, mean reversion, and return forecasts. Journal of Portfolio Management, 47(3). https://doi.org/10.3905/JPM.2021.1.210
  32. Xu, Q., Li, M. y Jiang, C. (2021). Network-Augmented Time-Varying Parametric Portfolio Selection: Evidence from The Chinese Stock Market. North American Journal of Economics and Finance, 58. https://doi.org/10.1016/j.najef.2021.101503
  33. Zhao, H., Gan Chen, Z. Hiu Zhan, Z., Kwong, S. y Zhang, J. (2021). Multiple Populations Co-Evolutionary Particle Swarm Optimization for Multi-Objective Cardinality Constrained Portfolio Optimization Problem. Neurocomputing, 430, 58-70. https://doi.org/10.1016/jneucom.2020.12.022
Como Citar
Martínez Contreras, R. M., Martínez Amado, R. D., Atehortúa Santamaria, R., & Hernández Mora, N. C. (2022). Análise do índice preço-benefício ajustado ciclicamente nos portfólios do mercado acionário brasileiro, 2011-2019. Semestre Económico, 25(58), 1-23. https://doi.org/10.22395/seec.v25n58a3

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