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JAIT 2022 Vol.13(2): 125-131
doi: 10.12720/jait.13.2.125-131

A New Framework for Analyzing News in the Financial Markets to Enhance the Investor’s Perception

Issam Aattouchi and Mounir Ait Kerroum
Ibn Tofail University, Kenitra, Morocco

Abstract—In finance, the flow of news is constantly updated in a way that changes the investors’ understanding, influences their sentiment, and hence, shapes the financial markets (Asset returns, Volatility, Interest rates, etc.). Thus, one of the most important challenges that an investor has to overcome is to extract useful information from text data (News) in order to be used in decision making after reviewing all the literature dealing with the analysis of financial news. In this paper, we introduce an automated methodology to extract relevant words and topics from the FOMC (The Federal Open Market Committee) Minutes reports. Using the Latent Dirichlet Allocation (LDA) algorithm to track the main topics in the FOMC reports especially the “inflation” topic, we made a dimension reduction of our corpus by considering only the first 250 words belonging to this topic. Using the reduced corpus, we applied filters to clean non discriminative words and we employ deep learning in detecting only sentiment-charged words. By the end, we obtained a list of the most relevant words (dictionary) in the FOMC minutes and a prediction (with an accuracy of 98.76% and a correlation of 77%) of the Fed Funds rates.
Index Terms—FOMC, classification, LDA, CNN, prediction, news, finance, market, interest rates, neural networks

Cite: Issam Aattouchi and Mounir Ait Kerroum, "A New Framework for Analyzing News in the Financial Markets to Enhance the Investor’s Perception," Journal of Advances in Information Technology, Vol. 13, No. 2, pp. 125-131, April 2022.
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.