Unlocking the power of trading volume in Tehran Stock Exchange (TSE): A case study on Mobarakeh Steel Company stocks using XGBoost

This study investigates the effectiveness of volume-related features in predicting stock price movements in the Tehran Stock Exchange, focusing on Mobarakeh Steel Company as a case study, focusing solely on ۱۹ volume metrics extracted from trading data since ۲۰۰۹. Our analysis employs a binary classification approach using the XGBoost model to predict weekly price direction. Results confirm the significant importance of volume characteristics in anticipating stock price movements, highlighting the unique dynamics of the Tehran Stock Exchange (TSE) compared to global markets. This research underscores the relevance of volume-based indicators in enhancing stock price prediction models within Tehran's specific trading environment.

کلیدواژه ها:

stock ، prediction ، XGBoost ، volume ، indicator ، Tehran ، Mobarakeh