DINÂMICA FINANCEIRA E SÉRIES TEMPORAIS: UM ESTUDO BIBLIOMÉTRICO E SISTEMÁTICO DA PRODUÇÃO ACADÊMICA
DOI:
https://doi.org/10.56238/arev7n8-123Palavras-chave:
Análise Bibliométrica, Finanças Corporativas, Séries Temporais, Modelos Híbridos, Inteligência ArtificialResumo
Este artigo apresenta um estudo bibliométrico sistemático sobre a aplicação de modelos de séries temporais na análise das finanças corporativas no período de 2010 a 2025. Por meio do exame de 84 artigos científicos indexados em bases de dados acadêmicas, são identificados e sintetizados cinco eixos temáticos principais: evolução teórica dos modelos, abordagens metodológicas, áreas de aplicação, tendências emergentes e integração da inteligência artificial. Os resultados demonstram uma transição dos modelos univariados clássicos, como ARIMA e SARIMA, para arquiteturas híbridas mais robustas e complexas, como LSTM-ARFIMA, que combinam aprendizado profundo com técnicas econométricas tradicionais. Essas metodologias são aplicadas em contextos diversos, incluindo previsão de falências, gestão de risco financeiro, formulação de políticas públicas e avaliação de desempenho corporativo. Além disso, observa-se uma crescente relevância da inteligência artificial e do aprendizado de máquina como ferramentas complementares para lidar com problemas financeiros caracterizados por altos níveis de volatilidade e não linearidade. Esse panorama metodológico e temático permite identificar lacunas de pesquisa e potenciais sinergias entre disciplinas correlatas. O estudo conclui destacando a necessidade de desenvolver modelos mais integrados e adaptativos, capazes de aprimorar a precisão das projeções financeiras e fortalecer a tomada de decisões estratégicas em ambientes dinâmicos e incertos. As implicações teóricas e práticas da análise são relevantes tanto para acadêmicos quanto para profissionais da área financeira interessados em metodologias avançadas para compreender as dinâmicas corporativas contemporâneas.
Downloads
Referências
Abdullah, S. Md., & Zaby, S. (2021). Seasoned equity offerings and differences in share-price impact by firm categories. International Journal of Financial Studies, 9(3), 36. https://doi.org/10.3390/ijfs9030036
Angus, A., Casado, M. R., & Fitzsimons, D. (2012). Exploring the usefulness of a simple linear regression model for understanding price movements of selected recycled materials in the UK. Resources, Conservation and Recycling, 60, 10–19. https://doi.org/10.1016/j.resconrec.2011.10.011
Barros, P. P., & Nunes, L. C. (2010). The impact of pharmaceutical policy measures: An endogenous structural-break approach. Social Science & Medicine, 71(3), 440–450. https://doi.org/10.1016/j.socscimed.2010.04.020
Basse, T., Schwarzbach, C., & Graf von der Schulenburg, J.-M. (2023). Dividend policy issues in the European pharmaceutical industry: New empirical evidence. The European Journal of Health Economics, 24(5), 803–816. https://doi.org/10.1007/s10198-022-01510-5
Bhaduri, S. N. (2014). Applying approximate entropy (ApEn) to speculative bubble in the stock market. Journal of Emerging Market Finance, 13(1), 43–68. https://doi.org/10.1177/0972652714534023
Bontempi, M. E., & Golinelli, R. (2012). The effect of neglecting the slope parameters’ heterogeneity on dynamic models of corporate capital structure. Quantitative Finance, 12(11), 1733–1751. https://doi.org/10.1080/14697688.2011.572903
Cheng, H., & Zhang, X. (2022). Empirical analysis of enterprise financial management risk prediction in view of associative memory neural network. Security and Communication Networks, 2022, 7825000. https://doi.org/10.1155/2022/7825000
Cheung, Y., Connelly, J. T., Jiang, P., & Limpaphayom, P. (2011). Does corporate governance predict future performance? Evidence from Hong Kong. Financial Management, 40(1), 159–197. https://doi.org/10.1111/j.1755-053X.2010.01138.x
Chou, W.-H., Feng, Z., Li, B., & Liu, F. (2025). A first look at financial data analysis using ChatGPT-4o. Journal of Risk and Financial Management, 18(2), 99. https://doi.org/10.3390/jrfm18020099
Dhar, V., Sun, C., & Batra, P. (2019). Transforming finance into vision: Concurrent financial time series as convolutional nets. Big Data, 7(4), 276–285. https://doi.org/10.1089/big.2019.0139
Dlouhy, M. (2011). Mental health services in the health accounts: The Czech Republic. Social Psychiatry and Psychiatric Epidemiology, 46(6), 447–453. https://doi.org/10.1007/s00127-010-0210-6
Dorfleitner, G., & Rößle, F. (2018). The financial performance of the health care industry: A global, regional and industry specific empirical investigation. The European Journal of Health Economics, 19(4), 585–594. https://doi.org/10.1007/s10198-017-0904-8
Garcia, M. T. M., & Guerreiro, J. P. S. M. (2016). Internal and external determinants of banks’ profitability. Journal of Economic Studies, 43(1), 90–107. https://doi.org/10.1108/JES-09-2014-0166
He, F., Chen, L., & Lucey, B. M. (2024). Chinese corporate biodiversity exposure. Finance Research Letters, 70, 106275. https://doi.org/10.1016/j.frl.2024.106275
Huang, A., Bi, Q., Chang, M., Feng, X., & Zhang, A. (2024). Predicting corporate financial risk using artificial bee colony-attention-gated recurrent unit model. Journal of Organizational and End User Computing, 36(1), 1–23. https://doi.org/10.4018/JOEUC.345244
Ilha, P. C. da S., Piacenti, C. A., & Leismann, E. L. (2018). Uma análise comparativa da competitividade econômico-financeira das cooperativas agroindustriais do Oeste do Paraná. Revista de Economia e Sociologia Rural, 56(1), 91–106. https://doi.org/10.1590/1234-56781806-94790560106
Jimbo-Sotomayor, R., Watts, E., Armijos, L., Sriudomporn, S., Sánchez, X., Echeverria, A., Whittembury, A., & Patenaude, B. (2022). Return on investment of 10-valent pneumococcal conjugate vaccine in Ecuador from 2010 to 2030. Value in Health Regional Issues, 31, 148–154. https://doi.org/10.1016/j.vhri.2022.05.003
Lee, H. Y., Beh, W. L., & Lem, K. H. (2020). Wavelet as a viable alternative for time series forecasting. Austrian Journal of Statistics, 49(3), 38–47. https://doi.org/10.17713/ajs.v49i3.1030
Lightwood, J., & Glantz, S. (2011). Effect of the Arizona tobacco control program on cigarette consumption and healthcare expenditures. Social Science & Medicine, 72(2), 166–172. https://doi.org/10.1016/j.socscimed.2010.11.015
Ling, H. ‘Fox,’ & Stone, D. B. (2016). Time-varying forecasts by variational approximation of sequential Bayesian inference. Quantitative Finance, 16(1), 43–67. https://doi.org/10.1080/14697688.2015.1034759
Liu, Y. (2020). Construction of marine economic forecast management system based on artificial intelligence. Journal of Coastal Research, 112(sp1), 232–235. https://doi.org/10.2112/JCR-SI112-063.1
Lorek, K. S., & Pagach, D. P. (2012). The impact of accruals and lines of business on analysts’ earnings forecast superiority. Review of Quantitative Finance and Accounting, 39(3), 293–308. https://doi.org/10.1007/s11156-011-0254-z
Lorek, K. S., & Willinger, G. L. (2011). Multi-step-ahead quarterly cash-flow prediction models. Accounting Horizons, 25(1), 71–86. https://doi.org/10.2308/acch.2011.25.1.71
Mafruhah, I. (2024). Migrant workers remittances in fostering country-of-origin entrepreneurship and financial inclusion: Life cycle-permanent income hypothesis. Montenegrin Journal of Economics, 20(4). https://doi.org/10.14254/1800-5845/2024.20-4.6
Marcelino-Aranda, M., Torres, A., Novoa, C., Muñoz-Marcelino, D., & Camacho, A. (2022). Desempeño financiero de las empresas más importantes, familiares y no familiares, en México. Revista Espacios, 43(2), 77–90. https://doi.org/10.48082/espacios-a22v43n02p06
Migliaccio, G., & Tucci, L. (2019). Economic assets and financial performance of Italian wine companies. International Journal of Wine Business Research, 32(3), 325–352. https://doi.org/10.1108/IJWBR-04-2019-0026
Mikhaylov, A., & Bhatti, M. I. M. (2025). The link between DFA portfolio performance, AI financial management, GDP, government bonds growth and DFA trade volumes. Quality & Quantity, 59(1), 339–356. https://doi.org/10.1007/s11135-024-01940-8
Mohammed Al-Matari, E. (2025). Do corporate environmental sustainability affect corporate performance? The role of board diversity evidence from Saudi Arabia stock market. Contaduría y Administración, 70(3), 507. https://doi.org/10.22201/fca.24488410e.2025.5591
Nguluwe, B., & Mayamiko Dunga, H. (2024). An examination of the relationship between budget deficit and economic growth in Malawi. African Journal of Business and Economic Research, 19(1), 175–198. https://doi.org/10.31920/1750-4562/2024/v19n1a8
Puig-Junoy, J., Rodríguez-Feijoó, S., & Lopez-Valcarcel, B. G. (2014). Paying for formerly free medicines in Spain after 1 year of co-payment: Changes in the number of dispensed prescriptions. Applied Health Economics and Health Policy, 12(3), 279–287. https://doi.org/10.1007/s40258-014-0097-6
Rashid Khan, H. U., Zaman, K., Usman, B., Nassani, A. A., Aldakhil, A. M., & Qazi Abro, M. M. (2019). Financial management of natural resource market: Long-run and inter-temporal (forecast) relationship. Resources Policy, 63, 101452. https://doi.org/10.1016/j.resourpol.2019.101452
Saâdaoui, F., & Rabbouch, H. (2024). Financial forecasting improvement with LSTM-ARFIMA hybrid models and non-Gaussian distributions. Technological Forecasting and Social Change, 206, 123539. https://doi.org/10.1016/j.techfore.2024.123539
Singh, P., & Kumar, B. (2012). Trade-off theory vs pecking order theory revisited. Journal of Emerging Market Finance, 11(2), 145–159. https://doi.org/10.1177/0972652712454514
Taušer, J., & Buryan, P. (2011). Exchange rate predictions in international financial management by enhanced GMDH algorithm. Prague Economic Papers, 20(3), 232–249. https://doi.org/10.18267/j.pep.398
Vaz de Melo Mendes, B., & Aíube, C. (2011). Copula based models for serial dependence. International Journal of Managerial Finance, 7(1), 68–82. https://doi.org/10.1108/17439131111109008
Wang, M. (2024). Artificial intelligence empowers the construction of first-class financial management system. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-0518
Yang, C., Xin, X., Li, X., & Li, L. (2024). Role of natural resource and mineral rent on economic development: Perspective on green reforms and financial management. Resources Policy, 95, 105181. https://doi.org/10.1016/j.resourpol.2024.105181
