Deep Learning Frameworks for Financial Time-Series Forecasting and Investment Risk Assessment
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I1P154Keywords:
Deep Learning, Financial Time-Series Forecasting, LSTM, GRU, Investment Risk Assessment, Value at Risk, Portfolio OptimizationAbstract
Financial time-series forecasting is a vital approach to the planning of investments, optimization of a portfolio, market analysis and financial risk management. Non-linear behavior of financial markets, fluctuating volatility, market uncertainty and complex temporal influences, however, restrict the usefulness of the traditional methods of statistical forecasting. This research article introduces a deep learning model of time-series financial forecasting and risk analysis of investments through the combination of Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and Convolutional Neural Networks (CNNs). The framework works out through historical stock prices, trading volumes, technical indicators and the measures of market volatility to determine temporal patterns and future price movements. Moreover, the mechanisms of risk assessment include volatility estimation, maximum drawdown and Value at risk (VaR) to assess possible losses in the investments in different market circumstances. To compare the forecasting accuracy, model generalization, computational efficiency and risk estimation potentials across various deep learning architectures, a comparative analytical approach is suggested. The framework also facilitates the adaptive portfolio decisions because it is based on the integration of predictive outputs with risk-sensitive investment decisions. The mechanisms of explainability can further illuminate important market indicators and model forecasts and enhance transparency in financial decisions. The offered strategy is a unifying base of the smart financial prediction and risk-sensitive investment management that assists investors and financial analysts in making smart choices in the shifting and unpredictable market conditions.
References
[1] [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, Article 106181. https://doi.org/10.1016/j.asoc.2020.106181
[2] Zhang, C., Sjarif, N. N. A., & Ibrahim, R. (2023). Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022. WIREs Data Mining and Knowledge Discovery. https://doi.org/10.1002/widm.1519
[3] Aryal, S., Nadarajah, D., Rupasinghe, P. L., Jayawardena, C., & Kasthurirathna, D. (2020). Comparative analysis of deep learning models for multi-step prediction of financial time series. Journal of Computer Science, 16(10), 1401–1416. https://doi.org/10.3844/jcssp.2020.1401.1416
[4] Aldhyani, T. H. H., & Alzahrani, A. (2022). Framework for predicting and modeling stock market prices based on deep learning algorithms. Electronics, 11(19), Article 3149. https://doi.org/10.3390/electronics11193149
[5] Lu, W. J., Li, J. Z., Li, Y. F., Sun, A. J., & Wang, J. Y. (2020). A CNN-LSTM-based model to forecast stock prices. Complexity, 2020, Article 6622927. https://doi.org/10.1155/2020/6622927
[6] Chen, Y., Fang, R., Liang, T., Sha, Z., Li, S., Yi, Y., & Song, H. (2021). Stock price forecast based on CNN-BiLSTM-ECA model. Scientific Programming, 2021, Article 2446543. https://doi.org/10.1155/2021/2446543
[7] Wang, H., Wang, J., Cao, L., Li, Y., Sun, Q., & Wang, J. (2021). A stock closing price prediction model based on CNN-BiSLSTM. Complexity, 2021, Article 5360828. https://doi.org/10.1155/2021/5360828
[8] Kang, C. Y., Lee, C. P., & Lim, K. M. (2022). Cryptocurrency price prediction with convolutional neural network and stacked gated recurrent unit. Data, 7(11), Article 149. https://doi.org/10.3390/data7110149
[9] Deng, C., Huang, Y., Hasan, N., & Bao, Y. (2022). Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition. Information Sciences, 607, 297–321. https://doi.org/10.1016/j.ins.2022.05.088
[10] [Staffini, A. (2022). Stock price forecasting by a deep convolutional generative adversarial network. Frontiers in Artificial Intelligence, 5, Article 837596. https://doi.org/10.3389/frai.2022.837596
[11] Lin, Y., Liao, Q. D., Lin, Z. X., Tan, B., & Yu, Y. Y. (2022). A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction. Resources Policy, 78, Article 102884. https://doi.org/10.1016/j.resourpol.2022.102884
[12] 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. https://doi.org/10.1016/j.eswa.2016.02.006
[13] Tang, Y., Song, Z., Zhu, Y., Yuan, H., Hou, M., Ji, J., & Li, J. (2022). A survey on machine learning models for financial time series forecasting. Neurocomputing, 512, 363–380.
[14] Wang, B., & Wang, J. (2020). Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation. Energy Economics, 90, Article 104827. https://doi.org/10.1016/j.eneco.2020.104827
[15] Livieris, I. E., Pintelas, E., & Pintelas, P. (2020). A CNN–LSTM model for gold price time-series forecasting. Neural Computing and Applications, 32(23), 17351–17360. https://doi.org/10.1007/s00521-020-04867-x
