An Innovative Artificial Intelligence and Natural Language Processing Framework for Asset Price Forecasting Based on Islamic Finance: A Case Study of the Saudi Stock Market

Klemens Katterbauer; Philippe Moschetta

doi:10.2478/erfin-2021-0009

Klemens Katterbauer Euclid University, Central African Republic http://orcid.org/0000-0001-5513-4418
Philippe Moschetta Euclid University, Central African Republic

DOI: https://doi.org/10.2478/erfin-2021-0009

Keywords: Artificial Intelligence, Islamic Finance, Financial Statements, Natural Language Processing, Saudi Arabia, Financial Markets

Abstract

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.

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