• Rajnandini Rawat
• Dr. Amit Shrivastava
• Dr. Kapil Chaturvedi

The stock market is an emerging network that offers an infrastructure for all financial transactions from the world in a dynamic rate called stock value, which is devised using market stability. Prediction of stock values provides huge profit opportunities which are considered as an inspiration for research in stock market prediction. Long short term memory (LSTM) is a model that increases the memory of recurrent neural networks. Recurrent neural networks hold short term memory in that they allow earlier determining information to be employed in the current neural networks. For immediate tasks, the earlier data is used. We may not possess a list of all of the earlier information for the neural node. The long short-term memory (LSTM) and gated recurrent unit (GRU) models are popular deep-learning architectures for stock market forecasting. Various studies have speculated that incorporating financial news sentiment in forecasting could produce a better performance than using stock features alone. This study carried a normalized comparison on the performances of LSTM and GRU for stock market forecasting under the same conditions and objectively assessed the significance of incorporating the financial news sentiments in stock market forecasting. Both the LSTM-News and GRUNews models are able to produce better forecasting in stock price equally.

Stock Market, LSTM, GRU

"Stock Price Volatility and Forecasting using Hybrid Model of LSTM & GRU Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.d773-d780, August-2022, Available :http://www.jetir.org/papers/JETIR2208384.pdf

"Stock Price Volatility and Forecasting using Hybrid Model of LSTM & GRU Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. ppd773-d780, August-2022, Available at : http://www.jetir.org/papers/JETIR2208384.pdf