Stock Trend Prediction Using Sentiment Index and Enhanced SVM with an Entropy-Based Sentiment Cost Function
Subject Areas : electrical and computer engineering
M. Yaghoubzadeh
1
,
A. Ebrahimi moghadam
2
*
,
M. Khademi
3
,
H. Sadoghi Yazdi
4
1 - Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad
2 - Department of Electrical Engineering, Faculty Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhadof Engineering, Ferdowsi University of Mashhad
3 - Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
4 - Computer Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Keywords: Fin-BERT, sentiment analysis, stock market prediction, SVM,
Abstract :
Stock market prediction has always been a focus of researchers. Advances in artificial intelligence and machine learning algorithms have enabled the use of textual data alongside numerical data for better stock market forecasting and performance. In this research, to predict the trend of the NewYork Stock Exchange (NYSE) index, numerical data, textual data, and a machine learning model were employed. The model's input includes numerical data as well as the results of sentiment analysis from texts extracted from X (formerly Twitter). Sentiment analysis is performed using a specific machine learning algorithm, Fin-BERT. Additionally, to improve prediction results, prior knowledge of data distribution is incorporated into the cost function of the proposed classifier (SVM). This knowledge is obtained through the calculation of sentiment entropy. Experimental results show that incorporating sentiment entropy into the model's cost function improves prediction performance.
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