Document Type : Research Article
Authors
1 Department of Mathematics, Vellore Institute of Technology, Vellore, India
2 Department of Mathematics, School of Science, Kathmandu University, Nepal
Abstract
In an environment marked by financial volatility and rapid economic shifts, reliable forecasts are critical for informed policy-making and strategic financial planning. This study investigates the detailed mathematical exploration followed by its computational performance of time series and deep learning models namely ARIMA, RNN, and TCN applied to foreign assets and liabilities of banking system of Nepal consisting monetary authorities and various depository corporations. Using available primary data, we analyze trends, seasonal patterns, trajectories and their descriptive statistics to capture underlying behaviors. Also, in our work, we have identified the optimal ARIMA order that most effectively captures the linear trend. Empirical study shows that the RNN is able to handle the non-linear patterns which is determined by performance metrices on the training and testing split. TCN being computationally extensive model is not able to capture robust relation of the data due to lack of long-range dependencies and large time windowed dataset for training. Based on our results, the RNN could be used as the most suitable time series forecasting model for the foreign assets and liabilities of Nepal as it enhances the accuracy, minimizes error, and improves effectiveness in contributing to decision-making in banking system.
Keywords
[2] S. Bai, J. Z. Kolter, and V. Koltun. An empirical evaluation of generic convolutional and
recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271, 2018.
[3] Y. Bao, J. Yue, and Y. Rao. A deep learning framework for financial time series using stacked
autoencoders and long short-term memory. PLoS ONE, 12(7):e0180944, 2017.
[4] Y. Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient
descent is difficult. IEEE Transactions on Neural Networks, 5(2):157–166, 1994.
[5] A. Borovykh, S. Bohte, and C. W. Oosterlee. Conditional time series forecasting with convolutional neural networks. arXiv preprint arXiv:1703.04691, 2017.
[6] N. Boustani, A. Emrouznejad, R. Gholami, O. Despic, and A. Ioannou. Improving the predictive accuracy of the cross-selling of consumer loans using deep learning networks. Annals
of Operations Research, 339:613–630, 2023.
[7] G. E. P. Box, G. M. Jenkins, and G. C. Reinsel. Time Series Analysis: Forecasting and
Control. Wiley, 4th edition, 2008.
[8] P. J. Brockwell and R. A. Davis. Time Series: Theory and Methods. Springer, 1991.
[9] D. A. Dickey and W. A. Fuller. Distribution of the estimators for autoregressive time series
with a unit root. Journal of the American Statistical Association, 74(366):427–431, 1979.
[10] F. X. Diebold and R. S. Mariano. Comparing predictive accuracy. Journal of Business &
Economic Statistics, 13(3):253–263, 1995.
[11] J. L. Elman. Finding structure in time. Cognitive Science, 14(2):179–211, 1990.
[12] E. F. Fama. Efficient capital markets: A review of theory and empirical work. The Journal
of Finance, 25(2):383–417, 1970.
[13] T. Fischer and C. Krauss. Deep learning with long short-term memory networks for financial
market predictions. European Journal of Operational Research, 270(2):654–669, 2018.
[14] J. Y. Franceschi, A. Dieuleveut, and M. Jaggi. Unsupervised scalable representation learning for multivariate time series. In Advances in Neural Information Processing Systems
(NeurIPS), 2019.
[15] I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. MIT Press, 2016.
[16] A. Graves. Generating sequences with recurrent neural networks. arXiv preprint
arXiv:1308.0850, 2013.
[17] J. J. Hopfield. Neural networks and physical systems with emergent collective computational
abilities. Proceedings of the National Academy of Sciences, 79(8):2554–2558, 1982.
[18] R. J. Hyndman and G. Athanasopoulos. Forecasting: Principles and Practice. OTexts, 2018.
[19] A. E. Khandani, A. J. Kim, and A. W. Lo. Consumer credit-risk models via machine-learning
algorithms. Journal of Banking & Finance, 34(11):2767–2787, 2010.
[20] Z. Li, B. Wang, and Y. Chen. Incorporating economic indicators and market sentiment effect
into US Treasury bond yield prediction with machine learning. Journal of Infrastructure,
Policy and Development, 8:7671, 2024.
[21] S. Makridakis, E. Spiliotis, and V. Assimakopoulos. Statistical and machine learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3):e0194889, 2018.
[22] S. Mohammad Almasarweh and S. AL Wadi. ARIMA model in predicting banking stock
market data. Modern Applied Science, 12(11):309–309, 2018.
[23] S. Moolchandani. Advanced credit risk assessment using Markov Chain Monte Carlo techniques. International Journal of Science and Research (IJSR), 13(4):7, 2024.
[24] A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, and K.
Kavukcuoglu. WaveNet: A generative model for raw audio. arXiv preprint arXiv:1609.03499,
2016.
[25] D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by backpropagating errors. Nature, 323:533–536, 1986.
[26] L. P. Masole, P. Sharon Nwanamidwa, M. Chanza, E. Munapo, and K. Mpeta. Modeling and
forecasting bank stock prices: GARCH and ARIMA approaches. In Intelligent Computing
and Optimization, pages 118–132. Springer, 2023.
[27] R. S. Tsay. Analysis of Financial Time Series. Wiley, 2nd edition, 2005.
[28] K. M. S. Uddin and N. Tanzim. Forecasting GDP of Bangladesh using ARIMA model.
International Journal of Business and Management, 16(6), 2021.
[29] J. Yao, J. Wang, B. Wang, B. Liu, and M. Jiang. A hybrid CNN-LSTM model for enhancing
bond default risk prediction. Journal of Computer Technology and Software, 3(6), 2024.
[30] Y. Yu, G. Kuang, J. Zhu, L. Shen, and M. Wang. Long-term interbank bond rate prediction
based on ICEEMDAN and machine learning. IEEE Access, 12:46241–46262, 2024.
[31] G. P. Zhang. Time series forecasting using a hybrid ARIMA and neural network model.
Neurocomputing, 50:159–175, 2003.
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