Ineficiencia en los mercados automatizados de EE.UU. y México

Contenido principal del artículo

Montserrat Reyna Miranda
Armando Tapia
Ricardo Mansilla
https://orcid.org/0000-0002-1248-0959
Ninfa Rodríguez Chávez

Resumen

El presente trabajo muestra un análisis de la hipótesis de los mercados eficientes (EMH), introducida por Fama (1970), que ha sido piedra angular en la teoría y la práctica en los mercados financieros por sus implicaciones para la predicción de precios futuros. La eficiencia de los mercados ha sido ampliamente explorada en la literatura, pero los avances tecnológicos recientes han hecho posible comerciar más rápido que nunca, por lo que surge una vez más la cuestión de examinar la eficiencia de los mercados de alta frecuencia, que aún no ha sido investigada en profundidad. El trabajo explora la eficiencia del mercado en varias frecuencias, desde 1 segundo hasta 10 días, en los mercados bursátiles de Estados Unidos (EE.UU.) y México. Se modeló la distribución empírica y la correlación de las series de rendimiento de los activos para tratar de evaluar si los mercados siguen un paseo aleatorio y así se puede concluir que la información se incorpora instantáneamente y el mercado es eficiente. Los resultados muestran que los mercados no son eficientes para altas frecuencias, pero por encima del umbral de 10 días las series siguen un paseo aleatorio con una distribución asintóticamente normal. La conclusión es importante tanto para los profesionales como para los académicos, ya que sugiere la posibilidad de pronosticar precios con alta frecuencia mediante la utilización de instrumentos estadísticos.

Descargas

Los datos de descargas todavía no están disponibles.

PLUMX Metrics

Detalles del artículo

Cómo citar
Reyna Miranda, M. ., Tapia, A., Mansilla, R., & Rodríguez Chávez, N. (2025). Ineficiencia en los mercados automatizados de EE.UU. y México. The Anáhuac Journal, 25(1), Pág. 2–19. https://doi.org/10.36105/theanahuacjour.2025v25n1.2722
Sección
Artículos
Biografía del autor/a

Montserrat Reyna Miranda, Universidad Anáhuac México, México

Montserrat Reyna is a research professor at the School of Economics and Business at Universidad Anáhuac México. She holds a PhD in Financial Sciences from EGADE Business School, an undergraduate degree in Mathematics from Universidad Nacional Autónoma de México (UNAM), and is a CFA Charterholder. Her research interests focus on investment theory and risk management of investment portfolios. She has several articles published in peer-reviewed journals of recognized quality and book chapters. She has been a speaker at several international conferences. She carries out consulting projects in data science applied to finance.

Armando Tapia, Tecnológico de Monterrey, México

Armando Tapia has a Ph.D. in Financial Sciences from the EGADE Business School and an MBA in Finance from the Instituto Tecnológico Autónomo de México (ITAM).
His research interests focus on investment theory, investment portfolio management, and data science applied to stock market finance. He has professional experience in the financial industry in the field of pension fund investments and investment strategies for hedge fund portfolios. He has been a speaker at several international conferences. Tapia’s teaching experience is in the field of programming for finance and economics.

Ricardo Mansilla, Centro de Investigaciones Interdisciplinarias en Ciencias y Humanidades, UNAM, México

Ricardo Mansilla holds a PhD in Mathematics from the University of Havana and a master’s degree in Economics from Carleton University. His extensive teaching experience includes teaching mathematics, statistics, and economics courses at various levels and universities in Mexico and abroad. He has participated as a speaker at various international conferences. He has extensive scientific production in applied statistics, including applications to medicine, economics, demography, and finance, in authorship and coordination of books, book chapters, and more than 70 articles.

Ninfa Rodríguez Chávez, Université Paris Panthéon Sorbonne, Francia

Ninfa Rodríguez Chávez graduated from the Université Paris 1 Panthéon Sorbonne with a master’s degree in Management, specializing in Finance and Social Responsibility, and holds a bachelor’s degree in Accounting from the Universidad Nacional Autónoma de México (UNAM). Her research is focused on financial markets and the financial inclusion of vulnerable groups. She has participated as a speaker at international conferences. Her professional experience is centered on corporate finance in different industries and countries.

Citas

Bessembinder, H., & K. Chan. (1998). Market Efficiency and the Returns to Tech- nical Analysis. Financial Management, 27(2): 5–17. https://www.jstor.org/sta-ble/3666289

Budish, E., Cramton, P., & Sim, J. (2015). The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response. The Quarterly Journal of Economics, 130(4), 1547–1621. https://doi.org/10.1093/qje/qjv027

Brogaard, J., Hendershott, T., & Riordan, R. (2014). High-Frequency Trading and Price Discovery. The Review of Financial Studies, 27(8), 2267–2306. https://doi.org/10.1093/rfs/hhu032

Chen, K., & Li, X. (2006). Is Technical Analysis Useful for Stock Traders in China? Evi- dence from the SZSE Component A-Share Index. Pacific Economic Review, 11(4): 477–488. https://doi.org/10.1111/j.1468-0106.2006.00329.x

Danak, D., & Patel, N. (2020). A Study of Efficiency of Index Futures, Lead-Lag Rela- tionship, and Speed of Adjustments in India Using Hgh-Frequency Data. Indian Journal of Finance, 14(4), 7–23. https://doi.org/10.17010/ijf/2020/v14i4/151705

Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383–417. http://dx.doi.org/10.2307/2325486

Fifield, S., D. Power, & D. Sinclair. (2005). An Analysis of Trading Strategies in Eleven European Stock Markets. European Journal of Finance, 11(6): 531–548. https://doi.org/10.1080/1351847042000304099

Ftiti, Z., Jawadi, F., Louhichi, W., & Madani, M. E. A. (2020). Are Oil and Gas Futures Markets Efficient? A Multifractal Analysis. Applied Economics, 53(2), 164–184. https://doi.org/10.1080/00036846.2020.1801984

Gnedenko, B. V., & Kolmogorov, A. N. (1968). Limit Distributions for Sums of Independ- ent Random Variables. Addison-Wesley.

Gopikrishnan, P., Plerou, V., Nunes Amaral, L. A., Meyer, M., & Stanley, H. E. (1999). Scaling of the Distribution of Fluctuations of Financial Market Indices. Physical Review E 60(5), 5305–5316. https://doi.org/10.1103/PhysRevE.60.5305

Heng, P., Niblock, S. J., Harrison, J. L., & Hu, H. (2020). The Impact of High-Frequency Trading on Australian Futures Market Liquidity and Efficiency. Journal of Deriva- tives, 27(4), 51–76. https://doi.org/10.3905/jod.2020.1.097

Jarque, C. M., & Bera, A. K. (1980). Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals. Economics Letters, 6(3), 255–259. https://doi.org/10.1016/0165-1765(80)90024-5

Karemera, D., K. Ojah & J.A. Cole. (1999). Random Walks and Market Efficiency Tests: Evidence from Emerging Equity Markets. Review of Quantitative Finance and Ac- counting, 13(2), 171–188. https://doi.org/10.1023/A:1008399910942

Koutrouvelis, I. A. (1981). An Iterative Procedure for the Estimation of the Param- eters of Stable Laws. Communications in Statistics—Simulation and Computation, 10(1), 17–28. https://doi.org/10.1080/03610918108812189

Kristoufek, L., & M. Vosvrda. (2013). Measuring Capital Market Efficiency: Global and Local Correlations Structure. Physica A: Statistical Mechanics and Its Applications, 392(1): 184–193. https://doi.org/10.1016/j.physa.2012.08.003

Lahrech, A., & Sylwester, K. (2011). US and Latin American Stock Market Link- ages. Journal of International Money and Finance, 30(7), 1341–1357. https://doi.org/10.1016/j.jimonfin.2011.07.004

Leone, V., & F. Kwabi. (2019). High Frequency Trading, Price Discovery and Market Ef- ficiency in the FTSE100, Economics Letters, 181, 174–177. https://doi.org/10.1016/j.econlet.2019.05.022

Ljung, G. M., & Box, G. E. (1978). On a Measure of Lack of Fit in Time Series Models. Biometrika, 65(2), 297–303. https://doi.org/10.1093/biomet/65.2.297

Mensi, W., Lee, Y., Al-Yahyaee, K. H., Sensoy, A., & Yoon, S. (2019). Intraday Down- ward/Upward Multifractality and Long Memory in Bitcoin and Ethereum Markets: An Asymmetric Multifractal Detrended Fluctuation Analysis. Finance Research Let- ters, 31, 19–25. https://doi.org/10.1016/j.frl.2019.03.029

Mishra, S. (2019). Testing Martingale Hypothesis Using Variance Ratio Tests: Evidence from High-Frequency Data of NCDEX Soya Bean Futures. Global Business Review, 20(6), 1407–1422. https://doi.org/10.1177/0972150919848937

Mobarek, A., A. Sabur, & R. Bhuyan. (2008). Market Efficiency in Emerging Stock Mar- ket: Evidence from Bangladesh. Journal of Emerging Market Finance, 7(1): 17–41. https://doi.org/10.1177/097265270700700102

Picardo, E. (2014, June 2). Top Stocks High-Frequency Traders (HFTs) Pick (BAC,F,CSCO,INTC). HedgeChatter. Retrieved on October 26, 2020 from: https://www.hedgechatter.com/top-stocks-high-frequency-traders-hfts-pick-bacfcscointc/

Plerou, V., Gopikrishnan, P., Nunes Amaral, L. A., Meyer, M., & Stanley, H. E. (1999). Scaling of the Distribution of Price Fluctuations of Individual Companies. Physical Review E, 60(6), 6519–6529. https://doi.org/10.1103/PhysRevE.60.6519

Ratner, M., & Leal, R. P.C. (1999). Tests of Technical Trading Strategies in the Emerg- ing Equity Markets of Latin America and Asia. Journal of Banking and Finance, 23(12): 1887–1905. https://doi.org/10.1016/S0378-4266(99)00042-4