Predicting The Stock Trend Using News Sentiment Analysis and Technical Indicators in Spark

Taylan Kabbani and Fatih Enes Usta
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Taylan Kabbani: Ozyegin University
Fatih Enes Usta: Marmara University

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Abstract: Predicting the stock market trend has always been challenging since its movement is affected by many factors. Here, we approach the future trend prediction problem as a machine learning classification problem by creating tomorrow_trend feature as our label to be predicted. Different features are given to help the machine learning model predict the label of a given day; whether it is an uptrend or downtrend, those features are technical indicators generated from the stock's price history. In addition, as financial news plays a vital role in changing the investor's behavior, the overall sentiment score on a given day is created from all news released on that day and added to the model as another feature. Three different machine learning models are tested in Spark (big-data computing platform), Logistic Regression, Random Forest, and Gradient Boosting Machine. Random Forest was the best performing model with a 63.58% test accuracy.

Date: 2022-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-fmk and nep-for
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