Individual Stock Price Prediction by Using KAP and Twitter Sentiments with Machine Learning for BIST30

Abstract

© 2022 IEEE.In this study we used machine learning models for predicting individual stock price and volume changes using sentiments from public disclosures and tweets. Public Disclosure Platform (KAP) is the mandated regulatory platform for disclosing news about companies listed in Borsa Istanbul. Investors in Borsa Istanbul use Twitter to express their sentiments about stocks. By combining people\"s sentiment on Twitter and companies\" disclosures, our prediction model predicts the volume and price changes of individual company stocks listed in BIST30. Financial data regarding market conditions consisting of daily price changes of BIST30, DJI, USD, and Gold per Ounce are also added to enhance the prediction accuracy of the model. Our model achieves an maximum of 80% individual stock price prediction accuracy for companies with high social media presence and public disclosure count. We also achieve 74.7% mean volume prediction accuracy across all BIST30 companies.