Does US Interest Rate Sentiment Impact Latin American ETFs?

Main Article Content

Humberto Valencia Herrera
https://orcid.org/0000-0002-4843-5965

Abstract

This article examines the dependence of Exchange Traded Fund (ETF) returns in six Latin American countries on interest rate and the Federal Reserve (FED) sentiment in the United States (US) news, during the period 2022 to 2023. For each country, robust regressions with zero to two lags for positive and negative sentiments, and previous returns were used. It was found that sentiment is statistically significant for some lags of ETF returns in Brazil, Chile, and Peru, in both, local currency and US dollar. The Latin American 40 ETF also depends on sentiment in US currency. Furthermore, a moment effect on returns in US currency and a mean reverting effect in local currency was identified.
A panel data model for the considered countries’ ETFs with random effects and zero to two lags in the change of sentiment shows that all considered changes in sentiment are statistically significant for returns, except for the change in positive sentiment without lags.

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How to Cite
Valencia Herrera, H. (2024). Does US Interest Rate Sentiment Impact Latin American ETFs?. The Anáhuac Journal, 24(1), Págs. 92–113. https://doi.org/10.36105/theanahuacjour.2024v24n1.04
Section
Artículos
Author Biography

Humberto Valencia Herrera, EGADE Business School, Tecnológico de Monterrey, Mexico

Dr. Humberto Valencia Herrera has an extensive career as a researcher, teacher, and professional in finance, economics, and management. He holds a PhD and a Master’s Degree in Economics and Decision Sciences from the Engineering Economic Systems Department, now part of the Management Science and Engineering Department at Stanford University, United States. He has been a finance, economics, management, and industrial engineering professor and researcher, and has collaborated with educational institutions in Mexico and the United States, such as the Tecnológico de Monterrey, the Universidad Iberoamericana, and Stanford University. He has numerous publications in  international and national academic journals. He is currently researching the use of artificial intelligence for investment decision-making, financial economics, energy economics, and technological development economics. He believes in the efficient and responsible management of businesses and economies, balancing environmental and social considerations with human needs.

References

Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61, 1645–1680. https://doi.org/10.1111/j.1540-6261.2006.00885.x DOI: https://doi.org/10.1111/j.1540-6261.2006.00885.x

Bernanke, B. S., & Kuttner, K. N. (2005). What explains the stock market’s reaction to federal reserve policy? The Journal of Finance, 60(3), 1221–1257. https://doi.org/10.1111/j.1540-6261.2005.00760.x DOI: https://doi.org/10.1111/j.1540-6261.2005.00760.x

Cabezón, F. (2012). Assessing the effects of foreign financial shocks in the Chilean economy. Revista de Análisis Económico, 27(2), 121–143. http://dx.doi.org/10.4067/S0718-88702012000200004 DOI: https://doi.org/10.4067/S0718-88702012000200004

Chari, S., Desai, P. H., Borde, N., & George, B. (2023). Aggregate News Sentiment and Stock Market Returns in India. Journal of Risk & Financial Management, 16(8), 376. https://doi.org/10.3390/jrfm16080376 DOI: https://doi.org/10.3390/jrfm16080376

Chen, M., Guo, Z., Abbass, K., & Huang, W. (2022). Analysis of the impact of investor sentiment on stock price using the latent Dirichlet allocation topic model. Frontiers of Environmental Science, 10, article 1068398. https://doi.org/10.3389/fenvs.2022.1068398 DOI: https://doi.org/10.3389/fenvs.2022.1068398

Cristescu, M. P., Mara, D. A., Nerișanu, R. A., Culda, L. C., & Maniu, I. (2023). Analyzing the Impact of Financial News Sentiments on Stock Prices—A Wavelet Correlation. Mathematics, 11 (23), article 4830. https://doi.org/10.3390/math11234830 DOI: https://doi.org/10.3390/math11234830

De Pontes, L. S. & Rêgo, L. C. (2022). Impact of macroeconomic variables on the topological structure of the Brazilian stock market: A complex network approach. Physica A: Statistical Mechanics and Its Applications, 604, article 127660. https://doi.org/10.1016/j.physa.2022.127660 DOI: https://doi.org/10.1016/j.physa.2022.127660

Dumiter, F. C., Turcaș, F., Nicoară, Ștefania A., Bențe, C., & Boiță, M. (2023). The Impact of Sentiment Indices on the Stock Exchange—The Connections between Quantitative Sentiment Indicators, Technical Analysis, and Stock Market. Mathematics (2227-7390), 11(14), article 3128. https://doi.org/10.3390/math11143128 DOI: https://doi.org/10.3390/math11143128

Ehrmann, M., & Fratzscher, M. (2007). Communication by Central Bank Committee Members: Different Strategies, Same Effectiveness? Journal of Money, Credit and Banking, 39(2–3), 509–541. https://doi.org/10.1111/j.0022-2879.2007.00034.x DOI: https://doi.org/10.1111/j.0022-2879.2007.00034.x

Han, Z., Sakkas, N., Danbolt, J., & Eshraghi, A. (2022). Persistence of investor sentiment and market mispricing. Financial Review, 57, 617–640. https://doi.org/10.1111/fire.12301 DOI: https://doi.org/10.1111/fire.12301

Hausman, J. A. (1978). Specification test in econometrics. Econometrica 46 (6), 1251–1271. https://doi.org/10.2307/1913827 DOI: https://doi.org/10.2307/1913827

Hindrayani, A., Putri, F. K. & Puspitasari, I. F. (2019). Spillover effect of US monetary policy to ASEAN stock market. Journal Economia. Review of Business and Economic Studies, 15(2) 213–242. https://doi.org/10.21831/economia.v15i2.26314 DOI: https://doi.org/10.21831/economia.v15i2.26314

Hutto, C. J., & Gilbert, E. E. (2014). VADER: A Parsimonious rule-based model for sentiment analysis of social media text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor. https://doi.org/10.1609/icwsm.v8i1.14550 DOI: https://doi.org/10.1609/icwsm.v8i1.14550

Juhro, S. M., Iyke, B. N., & Narayan, P. K. (2021). Interdependence between monetary policy and asset prices in ASEAN-5 countries. Journal of International Financial Markets, Institutions and Money, 75, article 101448. https://doi.org/10.1016/j.intfin.2021.101448 DOI: https://doi.org/10.1016/j.intfin.2021.101448

Kabiri, A., James, H., Landon-Lane, J., Tuckett, S., & Nyman, R. (2023). The role of sentiment in the US economy: 1920 to 1934. Economic History Review, 76, 3–30. https://doi.org/10.1111/ehr.13160 DOI: https://doi.org/10.1111/ehr.13160

Labadie, A. G. P. & Giovannini, P. (1991). Asset prices and interest rates in cashin-advance models. Journal of Political Economy, 99(6), 1215–1251. https://doi.org/10.1086/261798 DOI: https://doi.org/10.1086/261798

Lakdawala, A., Moreland, T., & Schaffer, M. (2021). The international spillover effects of US monetary policy uncertainty. Journal of International Economics, 133, article 103525. https://doi.org/10.1016/j.jinteco.2021.103525 DOI: https://doi.org/10.1016/j.jinteco.2021.103525

Lv, Y., Piao, J., Li, B., & Yang, M. (2022). Does online investor sentiment impact stock returns? Evidence from the Chinese stock market. Applied Economics Letters, 29(15), 1434–1438. https://doi.org/10.1080/13504851.2021.1937490 DOI: https://doi.org/10.1080/13504851.2021.1937490

Mendoza-Urdiales, R. A., Núñez-Mora, J. A., Santillán-Salgado, R. J., & Valencia-Herrera, H. (2022). Twitter Sentiment Analysis and Influence on Stock Performance Using Transfer Entropy and EGARCH Methods. Entropy; 24(7), 874. https://doi.org/10.3390/e24070874 DOI: https://doi.org/10.3390/e24070874

Nakhli, M. S., Dhaoui, A., & Chevallier, J. (2022). Bootstrap rolling-window Granger causality dynamics between momentum and sentiment: Implications for investors. Annals of Finance, 18, 267–283. https://doi.org/10.1007/s10436-021-00399-z DOI: https://doi.org/10.1007/s10436-021-00399-z

Tadle, R. C. (2022). FOMC minutes sentiments and their Impact on financial markets. Journal of Economics and Business, 118, article 106021. https://doi.org/10.1016/j.jeconbus.2021.106021 DOI: https://doi.org/10.1016/j.jeconbus.2021.106021

Ur Rehman, M., Raheem, I. D., Al Rababa’a, A. R., Ahmad, N., & Vo, X. V. (2023). Reassessing the predictability of the investor sentiments on US stocks: The role of uncertainty and risks. Journal of Behavioral Finance, 24 (4), 450–465. https://doi.org/10.1080/15427560.2022.2037598 DOI: https://doi.org/10.1080/15427560.2022.2037598

Wu, S. & Gu, F. (2023). Lightweight scheme to capture stock market sentiment on social media using sparse attention mechanism: a case study on Twitter. Journal of Risk and Financial Management,16 (10), 440. https://doi.org/10.3390/jrfm16100440 DOI: https://doi.org/10.3390/jrfm16100440

Wu, S., Liu, Y., Zou, Z., & Weng, T. H. (2022). S_I_LSTM: Stock price prediction based on multiple data sources and sentiment analysis. Connection Science, 34, 44–62. https://doi.org/10.1080/09540091.2021.1940101 DOI: https://doi.org/10.1080/09540091.2021.1940101

Yang, L. & Hamori, S. (2014). Spillover effect of US monetary policy to ASEAN stock markets: Evidence from Indonesia, Singapore1 and Thailand. Pacific-Basic Finance Journal, 26, 145-155. https://doi.org/10.1016/j.pacfin.2013.12.003 DOI: https://doi.org/10.1016/j.pacfin.2013.12.003

Zubair Mumtaz, M. & Smith, Z. A. (2019). Examining spillover effect of US monetary policy to European stock markets: A Markov-Switching approach. Estudios de Economia, 46(1), 89-124. http://dx.doi.org/10.4067/S0718-52862019000100089 DOI: https://doi.org/10.4067/S0718-52862019000100089