TY - GEN
T1 - Forecasting Stock Prices with Sequential Deep Learning
T2 - 8th International Conference on Vocational Education and Electrical Engineering, ICVEE 2025
AU - Yohannes, Ervin
AU - Febriansyah, Aldin
AU - Septiyanti, Nisa Dwi
AU - Suparji,
AU - Wiyono, Agus
AU - Indriyanti, Aries Dwi
AU - Utaminingrum, Fitri
AU - Lin, Chih Yang
AU - Muchtar, Kahlil
AU - Enkhbat, Avirmed
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Investors often face significant challenges in predicting fluctuating stock price movements, which can lead to uncertainty and suboptimal investment decisions. This study aims to evaluate the performance of the Long Short-Term Memory (LSTM) deep learning model in forecasting stock prices. The dataset utilized is derived from the Pakistan Stock Exchange (KSE 100), and the model's performance is assessed using evaluation metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The experimental results demonstrate that the LSTM model achieves strong predictive performance, with the lowest recorded MSE of 0.0004, RMSE of 0.0210, MAE of 0.0141, and MAPE of 0.0209, corresponding to an accuracy rate of 97.91%. These findings highlight the effectiveness of the LSTM model in stock price prediction and provide valuable insights for investors seeking to enhance decision-making through data-driven forecasting approaches.
AB - Investors often face significant challenges in predicting fluctuating stock price movements, which can lead to uncertainty and suboptimal investment decisions. This study aims to evaluate the performance of the Long Short-Term Memory (LSTM) deep learning model in forecasting stock prices. The dataset utilized is derived from the Pakistan Stock Exchange (KSE 100), and the model's performance is assessed using evaluation metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The experimental results demonstrate that the LSTM model achieves strong predictive performance, with the lowest recorded MSE of 0.0004, RMSE of 0.0210, MAE of 0.0141, and MAPE of 0.0209, corresponding to an accuracy rate of 97.91%. These findings highlight the effectiveness of the LSTM model in stock price prediction and provide valuable insights for investors seeking to enhance decision-making through data-driven forecasting approaches.
KW - deep learning
KW - forecasting
KW - long short-term memory
KW - sequential
KW - stock prices
UR - https://www.scopus.com/pages/publications/105031393852
U2 - 10.1109/ICVEE66651.2025.11281457
DO - 10.1109/ICVEE66651.2025.11281457
M3 - Conference contribution
AN - SCOPUS:105031393852
T3 - 2025 8th International Conference on Vocational Education and Electrical Engineering: Shaping a Sustainable Future with Green Innovation and Industry Collaboration for Education and Intelligent Technology Advancements, ICVEE 2025
SP - 177
EP - 183
BT - 2025 8th International Conference on Vocational Education and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 September 2025 through 25 September 2025
ER -