Financial Statement Analysis Based on RNN-RBM Model

Main Article Content

Fang Liu


Financial statement analysis is a critical component of decision-making for businesses, investors, and financial professionals. To enhance the accuracy and effectiveness of such analysis, this paper introduces the application of an innovative approach known as the Intelligent Swarm Regression ARIMA Model. This advanced model combines the power of swarm intelligence with ARIMA (AutoRegressive Integrated Moving Average) time series forecasting, offering a robust methodology for predicting and analyzing key financial metrics. The study begins by providing an overview of the Intelligent Swarm Regression ARIMA model and its application to financial data. Through a comprehensive analysis of financial statements, including market capitalization, revenue, net income, and other crucial indicators, the model's efficacy in predicting future values is evaluated. Additionally, the paper examines the deviations between predicted and actual financial values, offering insights into the model's accuracy and areas for potential improvement. The findings of this research are invaluable for investors, financial analysts, and companies seeking to optimize their financial performance and strategic decision-making. By leveraging the Intelligent Swarm Regression ARIMA Model, stakeholders can make well-informed choices that lead to better financial outcomes and a competitive advantage in a dynamic economic landscape. This paper represents a significant step forward in the financial analysis, providing a practical methodology and a pathway to enhanced financial decision-making. As the importance of financial data continues to grow, this research offers a promising avenue for achieving financial success and stability.

Article Details

Author Biography

Fang Liu

1Fang Liu

1The bursar's office, Shandong University of political science and law, Jinan, Shandong, China, 250014

*Corresponding author e-mail:

Copyright © JES 2024 on-line :


Brown, S. V., Hinson, L. A., & Tucker, J. W. (2022). Financial statement adequacy and firms’ MD&A disclosures. Available at SSRN 3891572.

Liu, M., Li, G., Li, J., Zhu, X., & Yao, Y. (2021). Forecasting the price of Bitcoin using deep learning. Finance research letters, 40, 101755.

Li, S., Shi, W., Wang, J., & Zhou, H. (2021). A deep learning-based approach to constructing a domain sentiment lexicon: a case study in financial distress prediction. Information Processing & Management, 58(5), 102673.

Jiang, W. (2021). Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications, 184, 115537.

Venkateswarlu, Y., Baskar, K., Wongchai, A., Gauri Shankar, V., Paolo Martel Carranza, C., Gonzáles, J. L. A., & Murali Dharan, A. R. (2022). An efficient outlier detection with deep learning-based financial crisis prediction model in big data environment. Computational Intelligence and Neuroscience, 2022.

Sun, Y., & Li, J. (2022). Deep Learning for Intelligent Assessment of Financial Investment Risk Prediction. Computational Intelligence and Neuroscience, 2022.

Mehtab, S., & Sen, J. (2022). Analysis and forecasting of financial time series using CNN and LSTM-based deep learning models. In Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2021 (pp. 405-423). Springer Singapore.

Shi, Y., Li, W., Zhu, L., Guo, K., & Cambria, E. (2021). Stock trading rule discovery with double deep Q-network. Applied Soft Computing, 107, 107320.

Kovacova, M., & Lăzăroiu, G. (2021). Sustainable organizational performance, cyber-physical production networks, and deep learning-assisted smart process planning in Industry 4.0-based manufacturing systems. Economics, Management and Financial Markets, 16(3), 41-54.

Hu, Z., Zhao, Y., & Khushi, M. (2021). A survey of forex and stock price prediction using deep learning. Applied System Innovation, 4(1), 9.

Rezaei, H., Faaljou, H., & Mansourfar, G. (2021). Stock price prediction using deep learning and frequency decomposition. Expert Systems with Applications, 169, 114332.

Horvath, B., Muguruza, A., & Tomas, M. (2021). Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models. Quantitative Finance, 21(1), 11-27.

Torres, J. F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., & Troncoso, A. (2021). Deep learning for time series forecasting: a survey. Big Data, 9(1), 3-21.

de Oliveira Carosia, A. E., Coelho, G. P., & da Silva, A. E. A. (2021). Investment strategies applied to the Brazilian stock market: a methodology based on sentiment analysis with deep learning. Expert Systems with Applications, 184, 115470.

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695.

Ngo, V. M., Nguyen, H. H., & Van Nguyen, P. (2023). Does reinforcement learning outperform deep learning and traditional portfolio optimization models in frontier and developed financial markets?. Research in International Business and Finance, 65, 101936.

Li, Y., & Pan, Y. (2022). A novel ensemble deep learning model for stock prediction based on stock prices and news. International Journal of Data Science and Analytics, 1-11.

Jing, N., Wu, Z., & Wang, H. (2021). A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction. Expert Systems with Applications, 178, 115019.

Frame, J. M., Kratzert, F., Klotz, D., Gauch, M., Shalev, G., Gilon, O., ... & Nearing, G. S. (2022). Deep learning rainfall–runoff predictions of extreme events. Hydrology and Earth System Sciences, 26(13), 3377-3392.

Chintalapudi, N., Battineni, G., & Amenta, F. (2021). Sentimental analysis of COVID-19 tweets using deep learning models. Infectious disease reports, 13(2), 329-339.

Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577.

Chen, Z., Chen, W., Smiley, C., Shah, S., Borova, I., Langdon, D., ... & Wang, W. Y. (2021). Finqa: A dataset of numerical reasoning over financial data. arXiv preprint arXiv:2109.00122.

Dhyani, M., & Kumar, R. (2021). An intelligent Chatbot using deep learning with Bidirectional RNN and attention model. Materials today: proceedings, 34, 817-824.

Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., ... & Arnold, C. W. (2021). Federated learning improves site performance in multicenter deep learning without data sharing. Journal of the American Medical Informatics Association, 28(6), 1259-1264.

Roszkowska, P. (2021). Fintech in financial reporting and audit for fraud prevention and safeguarding equity investments. Journal of Accounting & Organizational Change, 17(2), 164-196.

Nicholson, J. M., Mordaunt, M., Lopez, P., Uppala, A., Rosati, D., Rodrigues, N. P., ... & Rife, S. C. (2021). Scite: A smart citation index that displays the context of citations and classifies their intent using deep learning. Quantitative Science Studies, 2(3), 882-898.

Cabrero-Holgueras, J., & Pastrana, S. (2021). SoK: Privacy-Preserving Computation Techniques for Deep Learning. Proc. Priv. Enhancing Technol., 2021(4), 139-162.

Khalife, D., Yammine, J., & El Bazi, T. (2023). Understanding the Effect of Investors’ Sentiments on the S&P 500 Price Levels: A Deep Learning Model Approach. Khalife D., Yammine J., El Bazi T.,(2023),'Understanding the Effect of Investors’ Sentiments on the S&P, 500, 118-139.

Zhu, X., Ao, X., Qin, Z., Chang, Y., Liu, Y., He, Q., & Li, J. (2021). Intelligent financial fraud detection practices in post-pandemic era. The Innovation, 2(4).

Mehtab, S., Sen, J., & Dutta, A. (2021). Stock price prediction using machine learning and LSTM-based deep learning models. In Machine Learning and Metaheuristics Algorithms, and Applications: Second Symposium, SoMMA 2020, Chennai, India, October 14–17, 2020, Revised Selected Papers 2 (pp. 88-106). Springer Singapore.

Pramod, A., Naicker, H. S., & Tyagi, A. K. (2021). Machine learning and deep learning: Open issues and future research directions for the next 10 years. Computational analysis and deep learning for medical care: Principles, methods, and applications, 463-490.

Al-Hashedi, K. G., & Magalingam, P. (2021). Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019. Computer Science Review, 40, 100402.

Sattari, M. T., Apaydin, H., Band, S. S., Mosavi, A., & Prasad, R. (2021). Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation. Hydrology and Earth System Sciences, 25(2), 603-618.

Haq, A. U., Zeb, A., Lei, Z., & Zhang, D. (2021). Forecasting daily stock trend using multi-filter feature selection and deep learning. Expert Systems with Applications, 168, 114444.