Decifrando a dinâmica dos preços: Análise bibliométrica das tendências de pesquisa no mercado financeiro
DOI:
https://doi.org/10.36517/contextus.2025.95480Palavras-chave:
dinâmica de preços; mercados financeiros; bibliometria; análise de base; agenda de pesquisa.Resumo
Contextualização: A dinâmica do comportamento dos preços financeiros se consolidou como um tema central na literatura econômica, especialmente diante da volatilidade crescente dos mercados, avanços tecnológicos e novas interdependências globais. A compreensão dos fatores que influenciam essa dinâmica é essencial, principalmente em um cenário caracterizado por incertezas e transformações digitais constantes.
Objetivo: O estudo visa investigar a evolução da literatura científica sobre a dinâmica dos preços financeiros, abrangendo o período de 1970 a 2024. O foco está em mapear a trajetória da pesquisa, identificar suas bases teóricas e sociais, e delinear as tendências emergentes que moldam a agenda de pesquisa atual e futura na área.
Método: A pesquisa adotou uma abordagem bibliométrica, analisando 3.648 publicações extraídas das bases de dados Scopus e Web of Science. O processo de análise foi dividido em três etapas: (i) evolução temporal da produção científica, (ii) análise da base conceitual e social, por meio de redes de co-ocorrência, mapa temático e colaboração entre autores, e (iii) identificação de tendências emergentes, com ênfase em treze áreas de estudo.
Resultados: A literatura sobre dinâmica de preços mostrou crescimento consistente, com destaque para os períodos de crise econômica e inovação tecnológica. A produção científica revelou uma crescente integração entre abordagens micro e macroeconômicas, com foco em modelos empíricos.
Conclusões: As tendências emergentes indicam que a integração de tecnologias avançadas e práticas sustentáveis terá um impacto significativo na modelagem dos preços e na tomada de decisões de investimento. A pesquisa também aponta para novas direções, como a consideração de variáveis ambientais e a necessidade de modelos híbridos e adaptativos para lidar com a volatilidade e complexidade dos mercados financeiros.
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Referências
Agnello, L., Castro, V., Hammoudeh, S., & Sousa, R. (2020). Global factors, uncertainty, weather conditions and energy prices: On the drivers of the duration of commodity price cycle phases. Energy Economics, 90. https://doi.org/10.1016/j.eneco.2020.104862
Almutairi, H., Pierru, A., & Smith, J. (2024). Managing the oil market under misinformation: A reasonable quest? Journal of Commodity Markets, 34. https://doi.org/10.1016/j.jcomm.2024.100403
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Arslan, S. (2024). Bitcoin Price Prediction Using Sentiment Analysis and Empirical Mode Decomposition. Computational Economics, 65, 2227–2248. https://doi.org/10.1007/s10614-024-10588-3
Ashena, M., Khezri, H. L., & Shahpari, G. (2024). Investigation into the dynamic relationships between global economic uncertainty and price volatilities of commodities, raw materials, and energy. Applied Economic Analysis, 32. https://doi.org/10.1108/aea-06-2023-0207
Ashwin, J. (2024). Financial news media and volatility: Is there more to newspapers than news? Journal of Financial Markets, 69. https://doi.org/10.1016/j.finmar.2024.100896
Asproudis, E., Gedikli, Ç., Talavera, O., & Yılmaz, O. (2024). Returns to solar panels in the housing market: A meta learner approach. Energy Economics, 137. https://doi.org/10.1016/j.eneco.2024.107768
Bachelier, L. (1900). Théorie de la spéculation. Annales scientifiques de l'École Normale Supérieure, 17, 21–86. https://www.numdam.org/item?id=ASENS_1900_3_17__21_0
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
Batten, J. A., Mo, D., & Pourkhanali, A. (2024). Can inflation predict energy price volatility? Energy Economics, 129, https://doi.org/10.1016/j.eneco.2023.107158
Beckmann, J., Geldner, T., & Wuestenfeld, J. (2024). The relevance of media sentiment for small and large scale bitcoin investors. Journal of International Financial Markets, Institutions and Money, 92. https://doi.org/10.1016/j.intfin.2024.101963
Berry, C., & Börjesson, M. (2024). Income and fuel price elasticities of car use on micro panel data. Energy Economics, 135. https://doi.org/10.1016/j.eneco.2024.107661
Bhattacharya, P. S. (2024). Pass‐through to export prices: Evidence from Australia. The World Economy, 47, 2574–2607. https://doi.org/10.1111/twec.13554
Boddin, D., & Stähler, F. (2024). Import tariffs and transport prices. Canadian Journal of Economics, 24, 430–458. https://doi.org/10.1111/caje.12708
Brock, W. A., & Hommes, C. H. (1998). Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control, 22(8–9), 1235-1274. https://doi.org/10.1016/S0165-1889(98)00011-6
Buccheri, G., Grassi, S., & Vocalelli, G. (2024). Estimating Risk in Illiquid Markets: A Model of Market Friction with Stochastic Volatility. Journal of Financial Econometrics, 22. https://doi.org/10.1093/jjfinec/nbad006
Bunek, G. D., & Janzen, J. P. (2024). Does public information facilitate price consensus? Characterizing USDA announcement effects using realized volatility. Journal of Commodity Markets, 33. https://doi.org/10.1016/j.jcomm.2024.100382
Campos-Martins, S., & Hendry, D. F. (2024). Common volatility shocks driven by the global carbon transition. Journal of Econometrics, 239. https://doi.org/10.1016/j.jeconom.2023.05.008
Castro, C., & Jimenez-Rodriguez, R. (2024). The impact of oil shocks on the stock market. Global Finance Journal, 60. https://doi.org/10.1016/j.gfj.2024.100967
Chen, K., & Yang, J. (2024). Price dynamics and volatility jumps in bitcoin options. Financial Innovation, 10(132), 1-29. https://doi.org/10.1186/s40854-024-00653-z
Chuliá, H., Klein, T., Mendoza, J. A. M., & Uribe, J. M. (2024). Vulnerability of European electricity markets: A quantile connectedness approach. Energy Policy, 184. https://doi.org/10.1016/j.enpol.2023.113862
Ciaschini, C., & Recchioni, M. C. (2024). A market sentiment indicator, behaviourally grounded, for the analysis and forecast of volatility and bubbles. Review of Behavioral Finance, 16. https://doi.org/10.1108/rbf-07-2021-0128
Conlon, T., Corbet, S., & Hou, Y. (2024). Navigating the green transition: the influence of energy volatility on green and sustainable ETFs. Applied Economics Letters. https://doi.org/10.1080/13504851.2024.2337323
Conlon, T., Corbet, S., Hou, Y., Hu, Y., & Oxley, L. (2024). Seeking a shock haven: Hedging extreme upward oil price changes. International Review of Financial Analysis, 94. https://doi.org/10.1016/j.irfa.2024.103245
Cuaresma, J. C., Fortin, I., Hlouskova, J., & Obersteiner, M. (2024). Regime‐dependent commodity price dynamics: A predictive analysis. Journal of Forecasting, 43. https://doi.org/10.1002/for.3152
Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor Psychology and Security Market Under- and Overreactions. The Journal of Finance, 6. https://doi.org/10.1111/0022-1082.00077
De Lipsis, V., & Agnolucci, P. (2024). Climate change and the US wheat commodity market. Journal of Economic Dynamics and Control, 161. https://doi.org/10.1016/j.jedc.2024.104823
Ding, S., Wang, A., Cui, T., Du, M., & Zhou, X. (2024). Commodity market stability and sustainable development: The effect of public health policies. Research in International Business and Finance, 70. https://doi.org/10.1016/j.ribaf.2024.102386
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
Durisin, B., & Puzone, F. (2009). Maturation of Corporate Governance Research, 1993–2007: An Assessment. Corporate Governance: An International Review, 17(3), 266-291. https://doi.org/10.1111/j.1467-8683.2009.00739.x
Engle, R. F., & Russell, J. R. (1998). Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data. Econometrica, 66(5), 1127-1162. https://doi.org/10.2307/2999632
Fama, E. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25, 383-417.
Fama, E., & French, K. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3-56. https://doi.org/10.1016/0304-405X(93)90023-5
Fama, E., & French, K. (1996). Multifactor Explanations of Asset Pricing Anomalies. The Journal of Finance, 51. https://doi.org/10.1111/j.1540-6261.1996.tb05202.x
Fama, E., & French, K. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1-22. https://doi.org/10.1016/j.jfineco.2014.10.010
Fan, G., Zhang, R., Cao, C., Peng, L., Yeh, Y., & Hong, W. (2024). The volatility mechanism and intelligent fusion forecast of new energy stock prices. Financial Innovation, 10, 1-37. https://doi.org/10.1186/s40854-024-00621-7
Ferrero, A., Harrison, R., & Nelson, B. (2024). House Price Dynamics, Optimal LTV Limits and the Liquidity Trap. The Review of Economic Studies, 91, 940–971, https://doi.org/10.1093/restud/rdad040
Filser, L., Silva, F., & Oliveira, O. (2017). State of research and future research tendencies in lean healthcare: A bibliometric analysis. Scientometrics, 112(2), 799-816. https://doi.org/10.1007/s1119 2-017-2409-8
Findenegg, J., & Wessel, J. (2024). Pump or pedal? The impact of fuel prices on cycling in Germany. Transportation Research Part A: Policy and Practice, 186. https://doi.org/10.1016/j.tra.2024.104146
Gaio, L. E., Stefanelli, N. O., Pimenta, T., Bonacim, C. A. G., & Gatsios, R. C. (2022). The impact of the Russia-Ukraine conflict on market efficiency: Evidence for the developed stock market. Finance Research Letters. https://doi.org/10.1016/j.frl.2022.103302
Geng, Q., Hao, X., & Wang, Y. (2024). Forecasting the volatility of crude oil futures: A time‐dependent weighted least squares with regularization constraint. Journal of Forecasting, 43, 309-325. https://doi.org/10.1002/for.3036
Hamilton, J. D. (2003). What is an oil shock? Journal of Econometrics,113(2), 363-398. https://doi.org/10.1016/S0304-4076(02)00207-5
Hatcher, M., & Hellmann, T. (2024). Communication, networks and asset price dynamics: a survey. Journal of Economic Interaction and Coordination, 19, 1–58. https://doi.org/10.1007/s11403-023-00395-8
Haugom, E., Lyócsa, Š., & Halousková, M. (2024). The tipping point of electricity price attention: When a problem becomes a problem. Economics Letters, 235. https://doi.org/10.1016/j.econlet.2024.111547
Haukvik, N., Cheraghali, H., & Molnár, P. (2024). The role of investors’ fear in crude oil volatility forecasting. Research in International Business and Finance, 70. https://doi.org/10.1016/j.ribaf.2024.102353
Hirshleifer, D. (2002). Investor Psychology and Asset Pricing. The Journal of Finance. https://doi.org/10.1111/0022-1082.00379
Huang, T., Kumar, A., Sacchetto, S., & Vergara‐Alert, C. (2024). Stock Comovement and Financial Flexibility. Journal of Financial and Quantitative Analysis, 59, 1141-1184 https://doi.org/10.1017/S0022109022001338
Hui, C-H., Lo, C-F., & Liu, C-H. (2024). Equity Price Dynamics under Shocks: In Distress or Short Squeeze. Risks, 2(1). https://doi.org/10.3390/risks12010001
Hussain, M., Bashir, U., & Rehman, R. U. (2024). Exchange Rate and Stock Prices Volatility Connectedness and Spillover during Pandemic Induced-Crises: Evidence from BRICS Countries. Asia-Pacific Financial Markets, 31, 183–203. https://doi.org/10.1007/s10690-023-09411-0
Karpoff, J. M. (1987). The Relation Between Price Changes and Trading Volume: A Survey. The Journal of Financial and Quantitative Analysis, 22(1), 109-126. https://doi.org/10.2307/2330874
Kumar, A., & Mallick, S. (2024). Oil price dynamics in times of uncertainty: Revisiting the role of demand and supply shocks. Energy Economics, 129. https://doi.org/10.1016/j.eneco.2023.107152
Lim, K., & Brooks, R. (2011). The evolution of stock market efficiency over time: A survey of the empirical literature. Journal of Economic Surveys, 25, 69–108. https://doi.org/10.1111/j.1467-6419.2009.00611.x
Lo, A. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. J. Portf. Manage, 30, 15-29. https://ssrn.com/abstract=602222
Lo, A. (2017). Adaptive Markets: Financial Evolution at the Speed of Thought. 2. Ed. Princeton University Press.
Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. The Review of Financial Studies, 1(1), 41–66. https://www.jstor.org/stable/2962126
Lyu, C., Do, H. X., Nepal, R., & Jamasb, T. (2024). Volatility spillovers and carbon price in the Nordic wholesale electricity markets. Energy Economics, 134. https://doi.org/10.1016/j.eneco.2024.107559
Maia, S., Benedicto, G., Prado, J., Robb, D., Bispo, O., & Brito, M. J., M. (2019). Mapping the literature on credit unions: Abibliometric investigation grounded in Scopus and Web of Science. Scientometrics, 120, 929-960. https://doi.org/10.1007/s11192-019-03165-1
Mandelbrot, B. B. (1963). The Variation of Certain Speculative Prices. The Journal of Business, 36(4), 394–419. https://www.jstor.org/stable/2351623
Massa, O. I., Karali, B., & Irwin, S. H. (2024). What do we know about the value and market impact of the US Department of Agriculture reports? Applied Economic Perspectives and Policy, 46. https://doi.org/10.1002/aepp.13409
Mohanty, S. K., & Mishra, S. (2021). Regulatory reform and market efficiency: The case of Indian agricultural commodity futures markets. Research in International Business and Finance. https://doi.org/10.1016/j.ribaf.2019.101145
Molavi, P., Tahbaz-salehi, A., & Vedolin, A. (2024). Model Complexity, Expectations, and Asset Prices. The Review of Economic Studies, 91, 2462–2507, https://doi.org/10.1093/restud/rdad073
Moraes, A. K., & Ceretta, P. S. (2023). Correlação conditional dinâmica, spillover de volatilidade e hedge para os preços do petróleo futuro e das ações das principais empresas do setor petrolífero. Estudos Econômicos, 53(2), 375-409. https://doi.org/10.1590/1980-53575325ampc
Moraes, A. K., & Ceretta, P. S. (2023). Directional volatility spillover between agricultural commodities, oil and financial indices. Organizações Rurais & Agroindustriais, 25. https://doi.org/10.48142/2520231903
Moraes, A. K., Ceretta, P. S., & Castro, L. G., Júnior. (2024). Análise Multifractal do Ibovespa: Dinâmicas de Preço, Volume Negociado e Eficiência de Mercado. Revista Evidenciação Contábil & Finanças, 12(1), 128–149. https://doi.org/10.22478/ufpb.2318-1001.2024v12n1.70209
Moraes, A. K., Ceretta, P. S., & Castro, L. G., Júnior. (2024). Explorando a assimetria na dependência entre os índices da B3: Uma análise de cópula. Revista Brasileira de Economia de Empresas, 24(1). https://doi.org/10.31501/rbee.v24i1.15137
Moraes, A. K., Furtini, A., Prado, J., Castro Junior, L., & Ceretta, P. (2024). Evolução da produção científica sobre o Mercado Eficiente: Estudo bibliométrico. Contextus – Revista Contemporânea de Economia e Gestão, 22, e92462. https://doi.org/10.19094/contextus.2024.92462
Moraes, A. K., Sobral, M., & Melo, A. (2021). An analysis of the hedge operation on hydrated ethanol in a plant in the State of Paraíba. Revista em Agronegócio e Meio Ambiente, 14, 893-906. https://doi.org/10.17765/2176-9168.2021v14n4e7850
Moraes, A. K., Sobral, M., & Melo, A. (2021). Market efficiency: application of the concept to the sucroalcooleiro sector of Paraíba. Rev. Adm. UFSM, 14, 138-161. https://doi.org/10.5902/1983465939181
Musampa, K., Eita, J. H., & Meniago, C. (2024). The Effects of Oil Price Volatility on South African Stock Market Returns. Economies, 12(1), 4. https://doi.org/10.3390/economies12010004
Narayan, P. K. (2024). Pricing behavior of clean energy stocks? Some trading implications. Energy Economics, 134. https://doi.org/10.1016/j.eneco.2024.107590
Patil, A. C., & Rastogi, S. (2019). Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature. Journal of Risk and Financial Management, 12. https://doi.org/10.3390/jrfm12020105
Peng, K., Hu, Z., & Robe, M. A. (2024). Maximum order size and market quality: Evidence from a natural experiment in commodity futures markets. The Journal of Futures Markets, 44, 803–825. https://doi.org/10.1002/fut.22494
Pinto-Ávalos, F., Bowe, M., & Hyde, S. (2024). Revisiting the pricing impact of commodity market spillovers on equity markets. Journal of Commodity Markets, 33. https://doi.org/10.1016/j.jcomm.2023.100369
Prado, J., Castro, A., Carvalho, F., Vieira, K., Machado, L., & Tonelli, D. (2016). Multivariate analysis of credit risk and bankruptcy research data: A bibliometric study involving different knowledge fields (1968–2014). Scientometrics, 106(3). https://doi.org/10.1007/s1119 2-015-1829-6
Rezitis, A. N., Andrikopoulos, P., & Daglis, T. (2024). Assessing the asymmetric volatility linkages of energy and agricultural commodity futures during low and high volatility regimes. The Journal of Futures Markets, 44. https://doi.org/10.1002/fut.22477
Robinson, Z. (2024). A macroeconomic viewpoint using a structural VAR analysis of silver price behaviour. Mineral Economics, 37, 15-23. https://doi.org/10.1007/s13563-023-00386-y
Sadorsky, P. (1999). Oil price shocks and stock market activity. Energy Economics, 21(5), 449-469. https://doi.org/10.1016/S0140-9883(99)00020-1
Samuelson, P. A. (1965). Proof that Properly Anticipated Prices Fluctuate Randomly. Industrial Management Review, 6(2), 41–49.
Scheinkman, J. A., & Xiong, W. (2003). Overconfidence and Speculative Bubbles. Journal of Political Economy, 111(6). https://doi.org/10.1086/378531
Sharif, A., Aloui, C., & Yarovaya, L. (2020). Covid-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach, International Review of Financial Analysis. 70. https://doi.org/10.1016/j.irfa.2020.101496
Shiller, R. (2000). Irrational Exuberance. The American Journal of Economics and Sociology, 59(3), 537-540. https://www.jstor.org/stable/3487895
Silva, L. C. S., & Nobre, F. C. (2021). Produção científica sobre a influência dos vieses comportamentais no processo decisório: mapeamento comparativo entre Brasil, Estados Unidos da América e Europa. Revista Contribuciones a la Economía, 19(1), 53-84. https://www.eumed.net/es/revistas/contribuciones-economia/ce-enero21/processo-decisorio
Souza, R. M. F., Baia, M. W. M., Costa, I. C. M., Machado, R. S., Mendes, A. L. B., & Souto, M. V. (2017). Análise bibliométrica dos artigos científicos em finanças publicados na Revista de Administração de Empresas (RAE) da FGV/SP, no período de 2006 a 2016. Administração: Ensino e Pesquisa, 18(3), 489–517. https://doi.org/10.13058/raep.2017.v18n3.631
Takaishi, T. (2022). Time evolution of market efficiency and multifractality of the Japanese stock market. Journal of Risk and Financial Management. https://doi.org/10.3390/jrfm15010031
Thaler, R., & Sunstein, C. (2008). Nudge. Penguin Books.
Tian, Y., Wen, H., & Fu, S. (2024). Multi-step ahead prediction of carbon price movement using time-series privileged information. Expert Systems with Applications, 255. https://doi.org/10.1016/j.eswa.2024.124825
Todorov, K. (2024). When passive funds affect prices: evidence from volatility and commodity ETFs. Review of Finance, 28, 831–863, https://doi.org/10.1093/rof/rfad038
Tsaknaki, I-Y., Lillo, F., & Mazzarisi, P. (2024). Online learning of order flow and market impact with Bayesian change-point detection methods. Quantitative Finance, 25. https://doi.org/10.1080/14697688.2024.2337300
Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. Measuring Scholarly Impact, 285-320. https://doi.org/10.1007/s11192-009-0146-3
Vecco, M., Rodríguez, J. P., & Teerink, S. (2024). Climbing the ladder? The gender gap in art prices across artists’ cohorts in the Dutch art market. European Economic Review, 163. https://doi.org/10.1016/j.euroecorev.2023.104657
Vogl, M. (2023). Hurst exponent dynamics of S&P 500 returns: Implications for market efficiency, long memory, multifractality and financial crises predictability by application of a nonlinear dynamics analysis framework. Chaos, Solitons and Fractal. https://doi.org/10.1016/j.chaos.2022.112884
Wahid, A., & Kowalewski, O. (2024). Monetary Policy Spillovers and Inter-Market Dynamics Perspective of Preferred Habitat Model. Economies, 12(5), 98. https://doi.org/10.3390/economies12050098
Wu, R., Hossain, M. A., & Zhang, H. (2024). Factors affecting the volatility of bitcoin prices. The Economics and Finance Letters, 11(2), 107–125. https://doi.org/10.18488/29.v11i2.3730
Yusuf, M‐B. O. Salau, T. J., & Adeiza, A. (2024). Oil and Food Price Before and During COVID-19 Pandemic in Nigeria: A Non-Linear ARDL Approach. Studia Universitatis, Vasile Goldis Arad – Economics Series, 34. https://doi.org/10.2478/sues-2024-0007
Zhu, J., Zhang, L., & Sun, X. (2024). Optimal liquidation using extended trading close for multiple trading days. Financial Innovation. https://doi.org/10.1186/s40854-024-00613-7
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