• Vikas Burri
• Lalasa Mukku

Abstract : Accurately forecasting stock market returns presents a significant challenge due to the unpredictable and complex nature of financial markets. The advancement of artificial intelligence and improved computational capabilities has led to more effective predictive methods for stock prices. This study applies Artificial Neural Network and Random Forest techniques to predict the closing price for five companies operating in different sectors using financial data such as Open, High, Low, and Close prices. New variables derived from this data are used as inputs for the models. Evaluation of the models based on standard strategic indicators like RMSE and MAPE indicates their efficiency in predicting stock closing prices due to their low values.

Machine learning, deep learning, artificial intelligence, stock market, stock trading

"Forecasting Stock Closing Prices through Artificial Intelligence Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.g300-g303, April-2024, Available :http://www.jetir.org/papers/JETIR2404637.pdf

"Forecasting Stock Closing Prices through Artificial Intelligence Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppg300-g303, April-2024, Available at : http://www.jetir.org/papers/JETIR2404637.pdf