BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

BazEkon home page

Meny główne

Katterbauer Klemens (Euclid University, Central African Republic), Moschetta Philippe (Euclid University, Central African Republic)
An Innovative Artificial Intelligence and Natural Language Processing Framework for Asset Price Forecasting Based on Islamic Finance: A Case Study of the Saudi Stock Marke
Econometric Research in Finance, 2021, vol. 6, nr 2, s. 183-196, rys., wykr., bibliogr. 9 poz.
Słowa kluczowe
Sztuczna inteligencja, Języki naturalne, Rynki finansowe, Finanse islamskie
Artificial intelligence, Natural languages, Financial markets, Islam finance
JEL classification: G17, C70
Arabia Saudyjska
Saudi Arabia
Artificial intelligence has transformed the forecasting of stock prices and the evaluation of companies. Novel techniques, allowing the real-time processing of large amounts of data, have enabled the use of data on various external factors to improve the forecasting of the company's value and stock price. Although conventional approaches solely focus on the use of quantitative data, history has shown that news announcements and statements may significantly affect the performance of the stock value of companies. We present an innovative framework for integrating a nonlinear autoregressive network with a natural language processing approach to analyze stock price movements and forecast stock prices. The framework analyzes and processes the company's financial statements, determining indicative factors and transforming them into categorical parameters which are then integrated into a nonlinear autoregressive network to estimate and forecast the company's stock price. The analysis of several Saudi companies listed in the Tadawul index affirms the improved estimation of the stock price and the possibility of a more precise prediction of long-term stock price evolution(original abstract)
Dostępne w
Biblioteka Szkoły Głównej Handlowej w Warszawie
Pełny tekst
  1. Chi, W. L. (2018). Stock Price Forecasting Based on Time Series Analysis. AIP Conference Proceedings, 1967(1).
  2. Farida, A., Affianti, I., and Putri, E. (2018). Stock Price Prediction Using Geometric Brownian Motion. Journal of Physics: Conference Series, 974.
  3. Guo, Y., Han, S., Shen, C., Li, Y., Yin, X., and Bai, Y. (2018). An Adaptive SVR for High-frequency Stock Price Forecasting." IEEE Access", 6:11397-11404.
  4. Lewis, C. and Young, S. (2019). Fad or Future? Automated Analysis of Financial Text and Its Implications for Corporate Reporting. Accounting and Business Research, 49(5):587-615.
  5. Mehtab, S. and Sen, J. (2019). A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing. Available at SSRN:
  6. Saudi Exchange (2021). Market Participants - Market List. Retrieved from Saudi Exchange:
  7. Vijh, M., Chandola, D., Tikkiwal, V. A., and Kumar, A. (2020). Stock Closing Price Prediction Using Machine Learning Techniques. Procedia Computer Science, 167:599-606.
  8. Wang, J. and Wang, J. (2015). Forecasting Stock Market Indexes Using Principle Component Analysis and Stochastic Time Effective Neural Networks. "Neurocomputing", 156:68-78.
  9. Zhang, J., Teng, Y.-F., and Chen, W. (2019). Support Vector Regression With Modified Firefly Algorithm for Stock Price Forecasting." Applied Intelligence", 49(5):1658-1674.
Cytowane przez
Udostępnij na Facebooku Udostępnij na Twitterze Udostępnij na Google+ Udostępnij na Pinterest Udostępnij na LinkedIn Wyślij znajomemu