Integrating Volume Features with Technical Indicators for Stock Price Direction Prediction in the Tehran Stock Exchange: A Case Study of Bandar Abbas Oil Refining Company

This research investigates the efficacy of integrating volume features with technical indicators to improve stock price movement prediction accuracy, focusing on the shares of Bandar Abbas Oil Refining Company. Utilizing the company's transaction data from its listing date, key technical and volume features were extracted. A random forest model was employed to predict the stock's price direction for the upcoming week, achieving an accuracy of ۸۰% on the test set. On-Balance Volume (OBV), lower Bollinger Band, Moving Average Convergence Divergence (MACD), and upper Bollinger Band were identified as important predictors, highlighting the significance of volume metrics alongside technical indicators for forecasting stock price direction in the Tehran Stock Exchange market.

Keywords:

Stock price prediction , Tehran Stock Exchange , TSE , volume features , technical indicators , random forest , machine learning , ensemble methods , trading volume , MACD , Bollinger Bands , ATR , OBV , feature importance , market dynamics , investment strategies , risk management , algorithmic trading.