Abstract
Automatically analyzing financial data is the subject of much ongoing research. The purpose of this study was to research the possibility of using deep learning methods to predict and forecast the value of selected financial data in financial quarterly reports: cash flow from operating activities, cash flow from investing activities and cash flow from financing activities. The study examined the quarterly financial reports of selected companies listed on the Warsaw Stock Exchange (WSE), from September 2008 to December 2019, where each report consists of about 250 indicators. Based on the principles of financial analysis and the interdependency between financial indicators, a set of interdependent indicators was established and a multidimensional long short-term memory network (M-LSTM) was processed to predict future index values based on historical data. A reinforcement learning technique was used to see if it would improve prediction performance relative to the classical deep learning technique. The results show that the institute’s value prediction is performed significantly better up to a one-year horizon, i.e. up to four upcoming quarterly reports, given coupled financial data than uncoupled. It is also shown how the update of observations (reinforced learning) has an impact on the prediction result.
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References
Hedayati Moghaddama, A., Hedayati Moghaddamb, M., Esfandyari, M.: Stock market index prediction using artificial neural network. J. Econ. Finance Adm. Sci. 21, 89–93 (2016)
Kyoung-jae, K.: Financial time series forecasting using support vector machines. Neurocomputing 55(1–2), 307–319 (2003)
Korczak, J., Hemes, M.: Deep learning for financial time series forecasting in a-trader system. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, pp. 905–912 (2017)
Franc-Dąbrowska, J., Zbrowska, M.: Prognozowanie finansowe dla spółki X – spółka logistyczna. Zeszyty Naukowe SGGW w Warszawie. Ekonomika i Organizacja Gospodarki Żywnościowej 64, 251–270 (2008). (in Polish)
Chen, K., Zhou, Y., Dai, F.: A LSTM-based method for stock returns prediction: a case study of China stock market. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 2823–2824 (2015). https://doi.org/10.1109/BigData.2015.7364089
Zhao, Z., Rao, R., Tu, S., Shi, J.: Time-weighted LSTM model with redefined labeling for stock trend prediction. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1210–1217 (2017). https://doi.org/10.1109/ICTAI.2017.0018
Roondiwala, M., Patel, H., Varma, S.: Predicting stock prices using LSTM. Int. J. Sci. Res. (IJSR) 6 (2017). https://doi.org/10.21275/ART20172755
Qiu, J., Wang, B., Zhou, C.: Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLoS ONE 15(1) (2020). https://doi.org/10.1371/journal.pone.0227222
Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. Eur. J. Oper. Res. 270(2), 654–669 (2018). https://EconPapers.repec.org/RePEc:eee:ejores:v:270:y:2018:i:2:p:654-669
BCG Matrix (2021). http://www.netmba.com/strategy/matrix/bcg/
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Elsaraiti, M., Merabet, A.: Application of long-short-term-memory recurrent neural networks to forecast wind speed. Appl. Sci. 11, 2387 (2021). https://doi.org/10.3390/app11052387
Shumway, R.H., Stoffer, D.S.: Time Series Analysis and its Applications: With R Examples, 4th edn. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52452-8
Huang, J., Chai, J., Cho, S.L.: Deep learning in finance and banking: a literature review and classification. Front. Bus. Res. China 14, 13 (2020). https://doi.org/10.1186/s11782-020-00082-6
Gałuszka, A., Pacholczyk, M., Bereska, D., Skrzypczyk, K.: Planning as artificial intelligence problem - short introduction and overview. In: Nawrat, A., Simek, K., Świerniak, A. (eds.) Advanced Technologies for Intelligent Systems of National Border Security. SCI, vol. 440, pp. 95–103. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-31665-4_8
Roondiwala, M., Patel, H., Varma, S.: Financial time series forecasting with deep learning: a systematic literature review: 2005–2019 (2020)
Sezer, O.B., Gudelek, M.U., Ozbayoglu, A.M.: Appl. Soft Comput. J. 90, Article no. 106181 (2020)
Gałuszka, A., Probierz, E., Olczyk, A., Kocerka, J., Klimczak, K., Wisniewski, T:. The application of SISO LSTM networks to forecast selected items in financial quarterly reports - case study. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds.) Computational Science and Its Applications - ICCSA 2022 Workshops, Malaga, Spain, July 4–7, Proceedings pt 5, pp. 605–616 (2022)
Acknowledgments
We would like to thank the stock exchange experts for their critical comments. The work has been funded by GPW Data Grant No. POIR.01.01.01-00-0162/19 in 2021. The work of Adam Gałuszka was supported in part by the Silesian University of Technology (SUT) through the subsidy for maintaining and developing the research potential grant in 2022. The work of Eryka Probierz was supported in part by the European Union through the European Social Fund as a scholarship under Grant POWR.03.02.00-00-I029, and in part by the Silesian University of Technology (SUT) through the subsidy for maintaining and developing the research potential grant in 2022 for young researchers in analysis. This work was supported by Upper Silesian Centre for Computational Science and Engineering (GeCONiI) through The National Centre for Research and Development (NCBiR) under Grant POIG.02.03.01-24-099/13. The work of Karol Jędrasiak and Aleksander Nawrat has been supported by National Centre for Research and Development as a project ID: DOB-BIO10/19/02/2020 “Development of a modern patient management model in a life-threatening condition based on self-learning algorithmization of decision-making processes and analysis of data from therapeutic processes”.
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Gałuszka, A., Nawrat, A., Probierz, E., Jędrasiak, K., Wiśniewski, T., Klimczak, K. (2023). On the Application of Multidimensional LSTM Networks to Forecast Quarterly Reports Financial Statements. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_40
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