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Predicting the Direction of NEPSE Index Movement with News Headlines Using Machine Learning

Author

Listed:
  • Keshab Raj Dahal

    (Department of Mathematics, State University of New York Cortland, Cortland, NY 13045, USA)

  • Ankrit Gupta

    (Department of Computer Science, Central Michigan University, Mt Pleasant, MI 48859, USA)

  • Nawa Raj Pokhrel

    (Department of Physics and Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA)

Abstract

Predicting stock market movement direction is a challenging task due to its fuzzy, chaotic, volatile, nonlinear, and complex nature. However, with advancements in artificial intelligence, abundant data availability, and improved computational capabilities, creating robust models capable of accurately predicting stock market movement is now feasible. This study aims to construct a predictive model using news headlines to predict stock market movement direction. It conducts a comparative analysis of five supervised classification machine learning algorithms—logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN)—to predict the next day’s movement direction of the close price of the Nepal Stock Exchange (NEPSE) index. Sentiment scores from news headlines are computed using the Valence Aware Dictionary for Sentiment Reasoning (VADER) and TextBlob sentiment analyzer. The models’ performance is evaluated based on sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC). Experimental results reveal that all five models perform equally well when using sentiment scores from the TextBlob analyzer. Similarly, all models exhibit almost identical performance when using sentiment scores from the VADER analyzer, except for minor variations in AUC in SVM vs. LR and SVM vs. ANN. Moreover, models perform relatively better when using sentiment scores from the TextBlob analyzer compared to the VADER analyzer. These findings are further validated through statistical tests.

Suggested Citation

  • Keshab Raj Dahal & Ankrit Gupta & Nawa Raj Pokhrel, 2024. "Predicting the Direction of NEPSE Index Movement with News Headlines Using Machine Learning," Econometrics, MDPI, vol. 12(2), pages 1-26, June.
    • Handle: RePEc:gam:jecnmx:v:12:y:2024:i:2:p:16-:d:1412218
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    References listed on IDEAS

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    2. Shun Chen & Lei Ge, 2019. "Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1507-1515, September.
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