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Sarkar, B. and Shahid, A. (2005) Hybrid FinBERT-LSTM Deep Learning Framework for Stock Price Prediction: A Sentiment Analysis Approach Using Earnings Call Transcripts. EasyChair Preprint.

has been cited by the following article:

• TITLE: Optimizing Stock Market Prediction Using Long Short-Term Memory Networks

  AUTHORS: Nadia Afrin Ritu, Samsun Nahar Khandakar, Md. Masum Bhuiyan, Md. Imdadul Islam

  KEYWORDS: Long Short-Term Memory (LSTM), Stock Market, Prediction, Time Series Analysis, Deep Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.2, February 27, 2025

  ABSTRACT: Deep learning plays a vital role in real-life applications, for example object identification, human face recognition, speech recognition, biometrics identification, and short and long-term forecasting of data. The main objective of our work is to predict the market performance of the Dhaka Stock Exchange (DSE) on day closing price using different Deep Learning techniques. In this study, we have used the LSTM (Long Short-Term Memory) network to forecast the data of DSE for the convenience of shareholders. We have enforced LSTM networks to train data as well as forecast the future time series that has differentiated with test data. We have computed the Root Mean Square Error (RMSE) value to scrutinize the error between the forecasted value and test data that diminished the error by updating the LSTM networks. As a consequence of the renovation of the network, the LSTM network provides tremendous performance which outperformed the existing works to predict stock market prices.