Forecasting Financial Trends in the Zambian Banking Sector: Leveraging Historical Data for Informed Decision-Making
DOI:
https://doi.org/10.59413/eafj/v3.i2.1Keywords:
Historical data, forecasting, banking sector, decision-making, balance sheet, income statement, forecast accuracy, Normalized Root Mean Square Error (NRMSE), Regression analysis, future trendsAbstract
This study examines the integration of historical data in forecasting future outcomes, focusing on the banking sector in Zambia. The study analyzes data for five banks in Zambia from January 2010 to December 2020 and examines the accuracy of forecasts for key financial variables such as total assets, loans and advances, deposits, revenue and profit after tax. Using regression analysis, the study develops forecast models that are calibrated with historical data from 2010 to 2017. The accuracy of these forecasts is assessed using normalized root mean square error (RMSE), which provides a standardized metric for evaluation. The study highlights that historical data serves as a reliable predictor of future outcomes and demonstrates the effectiveness of forecasting techniques in projecting past trends into the future. Consequently, decisions based on these forecasts can provide a sufficiently accurate indication of future scenarios in the Zambian banking sector.
References
Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: forecasting and control. Holden-Day.
Brown, L., & Black, J. (2010). The role of historical data in long-term forecasting. *International Journal of Forecasting.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
Jones, L. (2020). Predicting Future Trends in Banking: A Case Study of Four Banks. International Journal of Financial Analysis, 18(3), 89-104.
Jones, R., et al. (2015). The importance of historical data in weather forecasting: A case study. Journal of Climate.
Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT Press.
Kumar, A., et al. (2017). Historical data and machine learning algorithms in predictive modeling: A comparative study. IEEE Transactions on Knowledge and Data Engineering.
Lee, M., & Chen, K. (2014). Predictive power of historical data in financial forecasting: Evidence from stock markets. Journal of Financial Economics.
Makridakis, S., Wheelwright, S., & Hyndman, R. J. (1998). Forecasting: methods and applications. John Wiley & Sons.
Patel, N., & Gupta, R. (2019). The predictive power of historical data in marketing analytics: A case study of consumer behavior. Journal of Marketing Research.
Smith, J., Brown, L., & Wilson, K. (2019). Leveraging Historical Data for Future Predictions in Banking. Journal of Financial Analytics, 12(2), 56-72.
Smith, T., & Johnson, L. (2012). Utilizing past data for future predictions: A review of methods and applications. Journal of Applied Econometrics.
Smith, T., & Johnson, L. (2020). Using past data for future demand forecasting in supply chain management: A review. International Journal of Production Economics.
Wang, G., & Zhang, H. (2016). Using past performance to predict future outcomes: A meta-analysis. Journal of Applied Psychology.
White, E., & Miller, S. (2018). Predictive modeling in healthcare: The role of historical data in patient outcome prediction. Journal of Healthcare Management.
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