Augmented Textual Features-Based Stock Market Prediction

Salah Bouktif, Ali Fiaz, Mamoun Awad

Research output: Contribution to journalArticlepeer-review

61 Citations (Scopus)


Due to its dynamics, non-linearity and complexity nature, stock market is inherently difficult to predict. One of the attractive objectives is to predict stock market movement direction by using public sentiments analysis. However, there is an active debate about the usefulness of this approach and the strength of causality between stock market trends and sentiments. The opinions of researchers range from rejecting the relationship to confirming a clear causality between sentiments and trading in stock markets. Nevertheless, many advanced computational methods have adopted sentiment-based features, yet did not attain maturity and performance. In this paper, we are contributing constructively in this debate by empirically investigating the predictability of stock market movement direction using an enhanced method of sentiments analysis. Precisely, we experiment on stock prices history, sentiments polarity, subjectivity, N-grams, customized text-based features in addition to features lags that are used for a finer-grained analysis. Five research questions have been investigated towards answering issues associated with stock market movement prediction using sentiment analysis. We have collected and studied the stocks of ten influential companies belonging to different stock domains in NASDAQ. Our analysis approach is complemented by a sophisticated causality analysis, an algorithmic feature selection and a variety of machine learning techniques including regularized models stacking. A comparison of our approach with other sentiment-based stock market prediction approaches including Deep learning, establishes that our proposed model is performing adequately and predicting stock movements with a higher accuracy of 60%.

Original languageEnglish
Article number9016182
Pages (from-to)40269-40282
Number of pages14
JournalIEEE Access
Publication statusPublished - 2020
Externally publishedYes


  • Machine learning
  • model stacking
  • sentiment analysis
  • stock movement direction prediction
  • textual features extraction
  • tweets mining

ASJC Scopus subject areas

  • General Engineering
  • General Computer Science
  • General Materials Science

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