FINANCIAL DYNAMICS AND TIME SERIES: A BIBLIOMETRIC AND SYSTEMATIC STUDY OF ACADEMIC PRODUCTION
DOI:
https://doi.org/10.56238/arev7n8-123Keywords:
Bibliometric Analysis, Corporate Finance, Time Series, Hybrid Models, Artificial IntelligenceAbstract
This article presents a systematic bibliometric study on the application of time series models in the analysis of corporate finance over the period 2010–2025. Through the examination of 84 scientific articles indexed in academic databases, five main thematic axes are identified and synthesized: theoretical evolution of the models, methodological approaches, application areas, emerging trends, and the integration of artificial intelligence. The results demonstrate a shift from classical univariate models, such as ARIMA and SARIMA, to more robust and complex hybrid architectures, including LSTM-ARFIMA, which combines deep learning with traditional econometric techniques. These methodologies are applied in diverse contexts, including bankruptcy prediction, financial risk management, public policy design, and corporate performance evaluation. Furthermore, a growing relevance of artificial intelligence and machine learning is observed as complementary tools to address financial problems with high levels of volatility and nonlinearity. This methodological and thematic overview identifies research gaps and potential synergies between related disciplines. The study concludes by highlighting the need to develop more integrated and adaptive models that can improve the accuracy of financial projections and strengthen strategic decision-making in dynamic and uncertain environments. The theoretical and practical implications of the analysis are relevant to both academics and financial professionals interested in advanced methodologies for understanding current corporate dynamics.
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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
