Improving stock price prediction using technical indicators and sentiment analysis

Document Type : Original Article

Authors

1 Department of computer engineering, Yazd University, Yazd, Iran

2 Associated Professor, Department of computer engineering, Yazd University, Yazd, Iran

Abstract

In this paper, the impact of sentiment analysis on increasing the accuracy of stock price predictions using technical indicators is investigated. Six basic estimators and five effective technical indicators are used to predict stock prices. The time window sizes were set at 3, 7, 14, 30, 45, and 60 days, respectively, and the best time window was selected to predict stock prices. The goal was to predict stock prices after 7 days. The best models for predicting stock prices, based on MSE assessment criteria, were linear regression models, multilayer perceptron regression, and random forest regression with MSE values of 34.78, 37.78, and 1%, respectively. The results showed that the volume of tweets was associated with the volume of trade, as well as the mean score of positive sentiment in one day, with stock prices.

Keywords

TABRIZ JOURNAL OF ELECTRICAL ENGINEERING

Articles in Press, Corrected Proof
Available Online from 03 July 2024

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