Skip to content
KPI Science News
ISSN 2617-5509 · e‑ISSN 2663-7472 Open Access · CC BY 4.0
Vol. 144 · Issue 3 · 2026 Sep 29, 2026 article

AN APPROACH TO CONSTRUCTING AN INFORMATIVE FEATURE SPACE OF TECHNICAL INDICATORS FOR FINANCIAL PROCESSES BASED ON NEURAL NETWORK MODELS

RT
Roman Tertychnyi Corresponding National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, IASA, MMSA, Ukraine Ukraine
DC
Denys Chuprin National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, IASA, MMSA, Ukraine Ukraine
PB
Petro Bidyuk National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, IASA, MMSA, Ukraine Ukraine
PublishedSep 29, 2026
PDF (Ukrainian)
KPISN 3 VOL 144 · 3
VOL 144 · ISS 3 · 2026 View issue

Abstract

Background. Financial time series are characterized by nonlinearity, non-stationarity, and a high level of noise, which significantly complicates their modeling and forecasting. The effectiveness of modern neural network models is largely determined not only by their architecture but also by the quality of the constructed input feature space. The use of technical indicators without accounting for their interdependencies may lead to multicollinearity, information redundancy, and a degradation of the models’ generalization ability. Objective. To develop an approach for constructing an informative feature space based on technical indicators and to investigate their impact on the forecasting accuracy of nonlinear and non-stationary financial processes using neural network models. Methods. The proposed approach is based on the principles of financial time series analysis and methods of data mining. For feature space construction, the approach utilizes an orthogonal combination of different indicator types — including trend, momentum, volatility, and relative price transformations. The investigation of their interaction mechanisms with the model was conducted using an LSTM architecture, focusing on the functional role of each parameter within the internal processes of information filtering and retention. Results. The developed methodology for constructing a feature space was tested on real-world stock price data for AMD using a hybrid architecture consisting of an LSTM-Autoencoder and an LSTM-Forecaster. The results demonstrated that forecasting performance strongly depends on the composition of the input feature vector. The best results were achieved by combining Price/MA and RSI, which increased the coefficient of determination from 0.8816 to 0.9316 and reduced the MAE from 3.1158 to 2.3569 compared with the baseline model. At the same time, the results confirmed that simply expanding the feature space does not guarantee improved forecasting performance due to potential information redundancy and interdependence among indicators. Conclusions . Improving the quality of forecasting financial processes can be achieved by constructing of informative and well-structured input feature space. The proposed approach of selective combination of technical indicators allows for reducing the impact of noise on model adequacy, increasing model stability, and improving its generalization ability. The obtained results create a foundation for practical implementation of high-performance automated decision support systems in financial markets.

Keywords

#informative feature space construction approach #technical indicators #Parkinson volatility #neural networks #Long Short-Term Memory #forecasting #nonlinear and non-stationary processes #time series

References

  1. Tertychnyi R. V., Chuprin D. S., Bidyuk P. I. Information system for forecasting nonlinear non-stationary processes in finance. KPI Science News. 2024. No. 1–4. P. 31–39. DOI: https://doi.org/10.20535/kpisn.2024.1-4.312719
  2. P. I. Bidiuk et al. Analiz nestatsionarnykh i neliniinykh protsesiv v ekonomitsi ta finansakh [Analysis of nonstationary and nonlinear processes in economics and finance]. – Kyiv: Igor Sikorsky Kyiv Polytechnic Institute, 2025. – 378 p. URL: https://ela.kpi.ua/handle/123456789/77009
  3. Chang Y., Park J. Y., Phillips P. C. Nonlinear econometric models with cointegrated and deterministically trending regressors. The Econometrics Journal. 2001. Vol. 4, No. 1. P. 1–36.
  4. DOI: https://doi.org/10.1111/1368-423X.00054
  5. Cavalcante R. C. et al. Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications. 2016. Vol. 55. P. 194–211. DOI: https://doi.org/10.1016/j.eswa.2016.02.006
  6. Lim B., Zohren S. Time-series forecasting with deep learning: A survey. Philosophical Transactions of the Royal Society A. 2021. Vol. 379, № 2194. Art. 20200209. DOI: https://doi.org/10.1098/rsta.2020.0209
  7. Fischer T., Krauss C. Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research. 2018. Vol. 270, No. 2. P. 654-669. DOI: https://doi.org/10.1016/j.ejor.2017.11.054
  8. Nelson D. M., Pereira A. C. M., de Oliveira R. A. Stock market's price movement prediction with LSTM neural networks. 2017 International Joint Conference on Neural Networks (IJCNN). Anchorage, 2017. P. 2623-2630. DOI: https://doi.org/10.1109/IJCNN.2017.7966019
  9. Bao W., Yue J., Rao Y. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE. 2017. Vol. 12, № 7. Art. e0180944. DOI: https://doi.org/10.1371/journal.pone.0180944
  10. Sezer O. B., Gudelek M. U., Ozbayoglu A. M. Financial time series forecasting with deep learning: A systematic literature review: 2005-2019. Applied Soft Computing. 2020. Vol. 90. Art. 106181.
  11. DOI: https://doi.org/10.1016/j.asoc.2020.106181
  12. Gu S., Kelly B., Xiu D. Empirical asset pricing via machine learning. The Review of Financial Studies. 2020. Vol. 33, No. 5. P. 2223–2273. DOI: https://doi.org/10.1093/rfs/hhaa009
  13. Patel J., Shah S., Thakkar P., Kotecha K. Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications. 2015. Vol. 42. P. 2162–2172. DOI: https://doi.org/10.1016/j.eswa.2014.10.031
  14. Atsalakis G. S., Valavanis K. P. Surveying stock market forecasting techniques – Part II: Soft computing methods. Expert Systems with Applications. 2009. Vol. 36. P. 5932–5941.
  15. DOI: https://doi.org/10.1016/j.eswa.2008.07.006
  16. Chong E., Han C., Park F. Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications. 2017. Vol. 83. P. 187–205.
  17. DOI: https://doi.org/10.1016/j.eswa.2017.04.030
  18. Sirignano J., Cont R. Universal features of price formation in financial markets. Quantitative Finance. 2019. Vol. 19, No. 9. P. 1449–1459.
  19. DOI: https://doi.org/10.1080/14697688.2019.1622295
  20. Di Persio L., Honchar O. Artificial neural networks architectures for stock price prediction: Comparisons and applications. International Journal of Circuits, Systems and Signal Processing. 2016. Vol. 10. P. 403–413.
  21. URL: https://iris.univr.it/retrieve/
  22. Vargas M. R. et al. Deep learning for stock market prediction using technical indicators and financial news articles. 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro, 2018. P. 1–8.
  23. DOI: https://doi.org/10.1109/IJCNN.2018.8489208
  24. Qin Y. et al. A dual-stage attention-based recurrent neural network for time series prediction. Proceedings of IJCAI. 2017.
  25. DOI: https://doi.org/10.48550/arXiv.1704.02971
  26. Bryzgalova S., Pelger M., Zhu J. Forest through the Trees: Building Cross-Sections of Stock Returns. The Journal of Finance. 2025. Vol. 80, No. 5. P. 2447–2506. DOI: https://doi.org/10.1111/jofi.13477
  27. Zhang G. P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 2003. Vol. 50. P. 159–175.
  28. DOI: https://doi.org/10.1016/S0925-2312(01)00702-0
  29. Tsantekidis A. et al. Forecasting stock prices from the limit order book using convolutional neural networks. 2017 IEEE 19th Conference on Business Informatics (CBI). Thessaloniki, 2017. Vol. 1. P. 7–12.
  30. DOI: https://doi.org/10.1109/CBI.2017.23
§ 06 — Related

Similar articles in this journal

Related peer-reviewed studies published in this journal.
View all issues

Most read articles by the same author(s)

Similar Articles

1-10 of 120

You may also start an advanced similarity search for this article.