Forecasting Bank Stock Trends Using Artificial Intelligence: A Deep Dive into the Neural Prophet Approach
DOI:
https://doi.org/10.61459/ijfs.v2i1.41Keywords:
Stock Price Prediction , Banking Sector, Financial Markets, Risk Management, Decision-MakingAbstract
This research aims to use Neural Prophet, a deep learning tool, to predict stock prices in the banking sector with high accuracy and useful insights. The model's capability in managing intricate temporal patterns differentiates it, garnering attention from researchers. The significance of this research lies in its potential to enhance stock price prediction precision, especially in the context of banking stocks, offering stakeholders’ deeper insights. The model's efficacy spans stable and volatile market behaviours, making it a valuable tool for informed decision-making in finance. Accurate predictions benefit risk management, facilitating well-informed investment choices in dynamic markets.
Downloads
References
Abduh, M., & Azmi Omar, M. (2012). Islamic banking and economic growth: the Indonesian experience. International Journal of Islamic and Middle Eastern Finance and Management, 5(1), 35–47.
Abora, J. Y., Agbloyor, E. K., & Kuipo, R. (2014). Bank finance and export activities of small and medium enterprises. Review of Development Finance, 4(2), 97–103.
Abu-Mostafa, Y. S., & Atiya, A. F. (1996). Introduction to financial forecasting. Applied Intelligence, 6, 205–213.
Aguilar-Rivera, R., Valenzuela-Rendón, M., & Rodríguez-Ortiz, J. J. (2015). Genetic algorithms and Darwinian approaches in financial applications: A survey. Expert Systems with Applications, 42(21), 7684–7697.
Ahmed, R., Vveinhardt, J., Ahmad, N., & Streimikiene, D. (2017). Karachi inter-bank offered rate (KIBOR) forecasting: Box-Jenkins (ARIMA) testing approach. E&M Economics and Management, 20(2), 188–198.
Akbar, Y. H., & McBride, J. B. (2004). Multinational enterprise strategy, foreign direct investment and economic development: the case of the Hungarian banking industry. Journal of World Business, 39(1), 89–105.
Almalaq, A., & Edwards, G. (2017). A Review of Deep Learning Methods Applied on Load Forecasting. 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 511–516. IEEE. https://doi.org/10.1109/ICMLA.2017.0-110
Almasarweh, M., & Alwadi, S. (2018). ARIMA model in predicting banking stock market data. Modern Applied Science, 12(11), 309.
Alomari, M., Al Rababa’a, A. R., Rehman, M. U., & Power, D. M. (2022). Infectious diseases tracking and sectoral stock market returns: A quantile regression analysis. The North American Journal of Economics and Finance, 59, 101584.
Arévalo, R., García, J., Guijarro, F., & Peris, A. (2017). A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Systems with Applications, 81, 177–192.
Arisoma, D. S., Supangat, S., & Narulita, L. F. (2019). System Design and Development of Financial Product Sales Forecasting with exponentially weighted moving average and exponential smoothing method. Proceeding UII-ICABE, 1–6.
Baele, L., De Jonghe, O., & Vander Vennet, R. (2007). Does the stock market value bank diversification? Journal of Banking & Finance, 31(7), 1999–2023.
Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42(20), 7046–7056.
Bhegawati, D. A. S., & Utama, M. S. (2020). The role of banking in Indonesia in increasing economic growth and community welfare. South East Asia Journal of Contemporary Business, Economics and Law, 22(1), 83–91.
Bouwman, C. H. S., Kim, H., & Shin, S.-O. S. (2018). Bank capital and bank stock performance. Mays Business School Research Paper, (3007364).
Brownlee, J. (2018). Deep learning for time series forecasting: predict the future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194–211.
ChikkaKrishna, N. K., Rachakonda, P., & Tallam, T. (2022). Short - Term Traffic Prediction Using Fb-PROPHET and Neural-PROPHET. 2022 IEEE Delhi Section Conference (DELCON), 1–4. IEEE. https://doi.org/10.1109/DELCON54057.2022.9753459
Civcir, I., & Akkoc, U. (2021). Non-linear ARDL approach to the oil-stock nexus: Detailed sectoral analysis of the Turkish stock market. Resources Policy, 74, 102424.
Ersan, D., Nishioka, C., & Scherp, A. (2020). Comparison of machine learning methods for financial time series forecasting at the examples of over 10 years of daily and hourly data of DAX 30 and S&P 500. Journal of Computational Social Science, 3, 103–133.
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4.
Ghosh, I., Sanyal, M. K., & Jana, R. K. (2021). Co-movement and dynamic correlation of financial and energy markets: An integrated framework of nonlinear dynamics, wavelet analysis and DCC-GARCH. Computational Economics, 57, 503–527.
Hadad, M. D., Hall, M. J. B., Kenjegalieva, K. A., Santoso, W., & Simper, R. (2011). Banking efficiency and stock market performance: an analysis of listed Indonesian banks. Review of Quantitative Finance and Accounting, 37, 1–20.
Hardi, I., Dawood, T. C., & Syathi, P. B. (2021). Determinants Comparative Advantage of Non-Oil Export 34 Provinces in Indonesia. International Journal of Business, Economics, and Social Development, 2(3), 98–106. https://doi.org/10.46336/ijbesd.v2i3.137
Hardi, I., Idroes, G. M., Hardia, N. A. K., Fajri, I., Furqan, N., Noviandy, T. R., & Utami, R. T. (2023). Assessing the Linkage Between Sustainability Reporting and Indonesia’s Firm Value: The Role of Firm Size and Leverage. Indatu Journal of Management and Accounting, 1(1), 21–28. https://doi.org/10.60084/ijma.v1i1.79
Hardi, I., Idroes, G. M., Utami, R. T., Dahlia, P., Mirza, M. A. F., Humam, R. A., … Mahdani, R. (2023). Dynamic Impact of Inflation and Exchange Rate in Indonesia’s Top 10 Market Capitalization Companies: Implications for Stock Prices. Indatu Journal of Management and Accounting, 1(2 SE-Articles), 51–59. https://doi.org/10.60084/ijma.v1i2.110
Hardi, I., Ringga, E. S., Fijay, A. H., Maulana, A. R. R., Hadiyani, R., & Idroes, G. M. (2023). Decomposed Impact of Democracy on Indonesia’s Economic Growth. Ekonomikalia Journal of Economics, 1(2), 51–60.
Hardi, I., Saputra, J., Hadiyani, R., Maulana, A. R. R., & Idroes, G. M. (2023). Decrypting the Relationship Between Corruption and Human Development: Evidence from Indonesia. Ekonomikalia Journal of Economics, 1(1), 1–9. https://doi.org/10.60084/eje.v1i1.22
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2). Springer.
Hiransha, M., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2018). NSE stock market prediction using deep-learning models. Procedia Computer Science, 132, 1351–1362.
Idroes, G M, Syahnur, S., Majid, S. A., Sasmita, N. R., & Idroes, R. (2021). Provincial economic level analysis in Indonesia based on the geothermal energy potential and growth regional domestic products using cluster analysis. IOP Conference Series: Materials Science and Engineering, 1087(1), 12079. IOP Publishing.
Idroes, Ghalieb Mutig, Hardi, I., Nasir, M., Gunawan, E., Maulidar, P., & Maulana, A. R. R. (2023). Natural Disasters and Economic Growth in Indonesia. Ekonomikalia Journal of Economics, 1(1), 33–39. https://doi.org/10.60084/eje.v1i1.55
Idroes, Ghalieb Mutig, Syahnur, S., Majid, M. S. A., Idroes, R., Kusumo, F., & Hardi, I. (2023). Unveiling the Carbon Footprint: Biomass vs. Geothermal Energy in Indonesia. Ekonomikalia Journal of Economics, 1(1), 10–18. https://doi.org/10.60084/eje.v1i1.47
Idroes, Ghazi Mauer, Maulana, A., Suhendra, R., Lala, A., Karma, T., Kusumo, F., … Noviandy, T. R. (2023). TeutongNet: A Fine-Tuned Deep Learning Model for Improved Forest Fire Detection. Leuser Journal of Environmental Studies, 1(1), 1–8. https://doi.org/10.60084/ljes.v1i1.42
Imambi, S., Prakash, K. B., & Kanagachidambaresan, G. R. (2021). PyTorch. https://doi.org/10.1007/978-3-030-57077-4_10
Jha, B. K., & Pande, S. (2021). Time series forecasting model for supermarket sales using FB-prophet. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 547–554. IEEE.
Jiang, W. (2021). Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications, 184, 115537.
Jiang, W., & Zhang, L. (2018). Geospatial data to images: A deep-learning framework for traffic forecasting. Tsinghua Science and Technology, 24(1), 52–64.
Jiang, W., & Zhang, L. (2020). Edge-siamnet and edge-triplenet: New deep learning models for handwritten numeral recognition. IEICE Transactions on Information and Systems, 103(3), 720–723.
Karamoy, H., & Tulung, J. E. (2020). The impact of banking risk on regional development banks in Indonesia. Banks and Bank Systems, 15(2).
Khurana, S., Sharma, G., Miglani, N., Singh, A., Alharbi, A., Alosaimi, W., … Goyal, N. (2022). An intelligent fine-tuned forecasting technique for covid-19 prediction using neuralprophet model. Comput. Mater. Contin, 71, 629–649.
Kramer, O. (2016). Scikit-Learn. https://doi.org/10.1007/978-3-319-33383-0_5
Llugsi, R., Yacoubi, S. El, Fontaine, A., & Lupera, P. (2021). Comparison between Adam, AdaMax and Adam W optimizers to implement a Weather Forecast based on Neural Networks for the Andean city of Quito. 2021 IEEE Fifth Ecuador Technical Chapters Meeting (ETCM), 1–6. IEEE. https://doi.org/10.1109/ETCM53643.2021.9590681
Long, W., Lu, Z., & Cui, L. (2019). Deep learning-based feature engineering for stock price movement prediction. Knowledge-Based Systems, 164, 163–173.
Mala, C. M. F., Sofyan, J. F., Adhikara, M. F. A., & Jumono, S. (2021). the relationship between banking intermediation and real economic growth (a case study of indonesia for the period 2007–2019). Journal of Southwest Jiaotong University, 56(6).
Maria, F. C., & Eva, D. (2011). Exchange-Rates Forecasting: Exponential smoothing techniques and ARIMA models. Annals of Faculty of Economics, 1(1), 499–508.
Mondal, P., Shit, L., & Goswami, S. (2014). Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. International Journal of Computer Science, Engineering and Applications, 4(2), 13.
Nguyen, T. H., Shirai, K., & Velcin, J. (2015). Sentiment analysis on social media for stock movement prediction. Expert Systems with Applications, 42(24), 9603–9611.
Noviandy, T. R., Idroes, G. M., Maulana, A., Hardi, I., Ringga, E. S., & Idroes, R. (2023). Credit Card Fraud Detection for Contemporary Financial Management Using XGBoost-Driven Machine Learning and Data Augmentation Techniques. Indatu Journal of Management and Accounting, 1(1), 29–35. https://doi.org/10.60084/ijma.v1i1.78
Noviandy, T. R., Maulana, A., Idroes, G. M., Suhendra, R., Adam, M., Rusyana, A., & Sofyan, H. (2023). Deep Learning-Based Bitcoin Price Forecasting Using Neural Prophet. Ekonomikalia Journal of Economics, 1(1), 19–25. https://doi.org/10.60084/eje.v1i1.51
Otoritas Jasa Keuangan (OJK). (2023). Statistik Perbankan Indonesia - Mei 2023. Retrieved from https://ojk.go.id/id/kanal/perbankan/data-dan-statistik/statistik-perbankan-indonesia/Pages/Statistik-Perbankan-Indonesia---Mei-2023.aspx
Park, C., & Irwin, S. H. (2007). What do we know about the profitability of technical analysis? Journal of Economic Surveys, 21(4), 786–826.
Peng, Y., Chen, W., Wei, P., & Yu, G. (2020). Spillover effect and Granger causality investigation between China’s stock market and international oil market: A dynamic multiscale approach. Journal of Computational and Applied Mathematics, 367, 112460.
Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., … Iyengar, S. S. (2019). A Survey on Deep Learning. ACM Computing Surveys, 51(5), 1–36. https://doi.org/10.1145/3234150
Rawat, W., & Wang, Z. (2017). Deep convolutional neural networks for image classification: A comprehensive review. Neural Computation, 29(9), 2352–2449.
Rezaei, M. A., Fathollahi, A., Rezaei, S., Hu, J., Gheisarnejad, M., Teimouri, A. R., … Khooban, M.-H. (2022). Adaptation of a real-time deep learning approach with an analog fault detection technique for reliability forecasting of capacitor banks used in mobile vehicles. IEEE Access, 10, 132271–132287.
Ruslan, S. M. M., & Mokhtar, K. (2021). Stock market volatility on shipping stock prices: GARCH models approach. The Journal of Economic Asymmetries, 24, e00232–e00232.
Sarveswararao, V., Ravi, V., & Vivek, Y. (2023). ATM cash demand forecasting in an Indian bank with chaos and hybrid deep learning networks. Expert Systems with Applications, 211, 118645.
Sen, J., & Chaudhuri, T. (2017). A robust predictive model for stock price forecasting. Proceedings of the 5th International Conference on Business Analytics and Intelligence (ICBAI 2017), Indian Institute of Management, Bangalore, India.
Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning : A systematic literature review: 2005–2019. Applied Soft Computing, 90, 106181. https://doi.org/10.1016/j.asoc.2020.106181
Shah, D., Isah, H., & Zulkernine, F. (2019). Stock market analysis: A review and taxonomy of prediction techniques. International Journal of Financial Studies, 7(2), 26.
Shehzad, M. K., Rose, L., Azam, M. F., & Assaad, M. (2022). Real-Time Massive MIMO Channel Prediction: A Combination of Deep Learning and NeuralProphet. GLOBECOM 2022-2022 IEEE Global Communications Conference, 1423–1428. IEEE.
Syarif, A. (2020). Forecasting the development of islamic bank in Indonesia: Adopting ARIMA model. JTAM (Jurnal Teori Dan Aplikasi Matematika), 4(2), 190–203.
Tkáč, M., & Verner, R. (2016). Artificial neural networks in business: Two decades of research. Applied Soft Computing, 38, 788–804.
Torbat, S., Khashei, M., & Bijari, M. (2018). A hybrid probabilistic fuzzy ARIMA model for consumption forecasting in commodity markets. Economic Analysis and Policy, 58, 22–31.
Triebe, O., Hewamalage, H., Pilyugina, P., Laptev, N., Bergmeir, C., & Rajagopal, R. (2021). NeuralProphet: Explainable Forecasting at Scale. https://doi.org/https://doi.org/10.48550/arXiv.2111.15397
Tsai, C. F., & Wang, S. P. (2009). Stock price forecasting by hybrid machine learning techniques. Proceedings of the International Multiconference of Engineers and Computer Scientists, 1(755), 60.
Vangala, S., & Vadlamani, R. (2020). ATM Cash demand forecasting in an Indian Bank with chaos and deep learning. ArXiv Preprint ArXiv:2008.10365.
Velásquez, R. M. A. (2022). A case study of NeuralProphet and nonlinear evaluation for high accuracy prediction in short-term forecasting in PV solar plant. Heliyon, 8(9).
Yu, Z., Niu, K., Chen, X., Guo, Z., & Li, D. (2022). A Hybrid Model Based on NeuralProphet and Long Short-Term Memory for Time Series Forecasting. 2022 IEEE International Conference on Big Data (Big Data), 1182–1191. IEEE.
Zaini, B. J., Mansor, R., Yusof, Z. M., Gabda, D., & Seng, W. K. (2020). Comparison of double exponential smoothing for Holt’s method and artificial neural network in forecasting the Malaysian banking stock markets. J. Appl. Stat., 48, 2231–2238.
Zhao, Z.-Q., Zheng, P., Xu, S., & Wu, X. (2019). Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232.
Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126–139.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 The International Journal of Financial Systems
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
IJFS article's license is CC-BY-NC. Authors grant the journal/publisher publishing rights with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.