ENHANCING DATA MANAGEMENT IN FINANCIAL FORECASTING WITH BIG DATA ANALYTICS
Keywords:
Big Data Analytics, Financial Forecasting, Data Management, Machine Learning, Predictive Modeling, Risk ManagementAbstract
The rapid growth of data in the financial sector has created both challenges and opportunities for enhancing data management and forecasting accuracy. Traditional forecasting models often fall short due to limited data processing capabilities, leaving financial institutions at a disadvantage in responding to real-time market changes. This study investigates the role of big data analytics in transforming data management practices and improving forecasting outcomes within financial institutions. By examining various big data tools and techniques—such as machine learning, sentiment analysis, and real-time processing—this research demonstrates that big data analytics can significantly enhance predictive accuracy and enable timely decision-making. The findings underscore the potential of big data to improve risk management, personalized customer service, and investment strategies, while also highlighting implementation challenges such as high computational costs, data security, and regulatory compliance. This comprehensive approach provides valuable insights for financial institutions aiming to integrate big data analytics into their forecasting frameworks to foster greater operational resilience and strategic agility.
References
Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013). Big data: Issues and challenges moving forward. 2013 46th Hawaii International Conference on System Sciences, 995-1004. https://doi.org/10.1109/HICSS.2013.645
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209. https://doi.org/10.1007/s11036-013-0489-0
Gartner. (2012). The importance of "big data": A definition. Retrieved from https://www.gartner.com/en/documents/
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51-59. https://doi.org/10.1089/big.2013.1508
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Zhang, X., Fuehres, H., & Gloor, P. A. (2015). Predicting stock market indicators through Twitter: “I hope it is not as bad as I fear”. Procedia - Social and Behavioral Sciences, 26, 55-62. https://doi.org/10.1016/j.sbspro.2011.10.562
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. https://doi.org/10.1016/j.jocs.2010.12.007
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263-286. https://doi.org/10.1016/j.jbusres.2016.08.001
Dumbill E. Making Sense of Big Data. Big Data. 2013 Mar;1(1):1-2. doi: 10.1089/big.2012.
