APA Style
Varadharajan, V. , Varadharajan, V. Smith, N. , Smith, N. Kalla, D. , Kalla, D. Kumar, G. R. , Kumar, G. R. Samaah, F. , & Samaah, F. (2024). Stock Closing Price and Trend Prediction with LSTM-RNN.
Current Research in Public Health, 4(1), 1-13.
https://doi.org/10.31586/jaibd.2024.877
ACS Style
Varadharajan, V. ; Varadharajan, V. Smith, N. ; Smith, N. Kalla, D. ; Kalla, D. Kumar, G. R. ; Kumar, G. R. Samaah, F. ; Samaah, F. Stock Closing Price and Trend Prediction with LSTM-RNN.
Current Research in Public Health 2024 4(1), 1-13.
https://doi.org/10.31586/jaibd.2024.877
Chicago/Turabian Style
Varadharajan, Vivek, Vivek Varadharajan. Nathan Smith, Nathan Smith. Dinesh Kalla, Dinesh Kalla. Ganesh R Kumar, Ganesh R Kumar. Fnu Samaah, and Fnu Samaah. 2024. "Stock Closing Price and Trend Prediction with LSTM-RNN".
Current Research in Public Health 4, no. 1: 1-13.
https://doi.org/10.31586/jaibd.2024.877
AMA Style
Varadharajan V, Varadharajan VSmith N, Smith NKalla D, Kalla DKumar GR, Kumar GRSamaah F, Samaah F. Stock Closing Price and Trend Prediction with LSTM-RNN.
Current Research in Public Health. 2024; 4(1):1-13.
https://doi.org/10.31586/jaibd.2024.877
@Article{crph877,
AUTHOR = {Varadharajan, Vivek and Smith, Nathan and Kalla, Dinesh and Kumar, Ganesh R and Samaah, Fnu and Polimetla, Kiran},
TITLE = {Stock Closing Price and Trend Prediction with LSTM-RNN},
JOURNAL = {Current Research in Public Health},
VOLUME = {4},
YEAR = {2024},
NUMBER = {1},
PAGES = {1-13},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/877},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2024.877},
ABSTRACT = {The stock market is very volatile and hard to predict accurately due to the uncertainties affecting stock prices. However, investors and stock traders can only benefit from such models by making informed decisions about buying, holding, or investing in stocks. Also, financial institutions can use such models to manage risk and optimize their customers' investment portfolios. In this paper, we use the Long Short-Term Memory (LSTM-RNN) Recurrent Neural Networks (RNN) to predict the daily closing price of the Amazon Inc. stock (ticker symbol: AMZN). We study the influence of various hyperparameters in the model to see what factors the predictive power of the model. The root mean squared error (RMSE) on the training was 2.51 with a mean absolute percentage error (MAPE) of 1.84%.},
}
%0 Journal Article
%A Varadharajan, Vivek
%A Smith, Nathan
%A Kalla, Dinesh
%A Kumar, Ganesh R
%A Samaah, Fnu
%A Polimetla, Kiran
%D 2024
%J Current Research in Public Health
%@ 2831-5162
%V 4
%N 1
%P 1-13
%T Stock Closing Price and Trend Prediction with LSTM-RNN
%M doi:10.31586/jaibd.2024.877
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/877
TY - JOUR
AU - Varadharajan, Vivek
AU - Smith, Nathan
AU - Kalla, Dinesh
AU - Kumar, Ganesh R
AU - Samaah, Fnu
AU - Polimetla, Kiran
TI - Stock Closing Price and Trend Prediction with LSTM-RNN
T2 - Current Research in Public Health
PY - 2024
VL - 4
IS - 1
SN - 2831-5162
SP - 1
EP - 13
UR - https://www.scipublications.com/journal/index.php/JAIBD/article/view/877
AB - The stock market is very volatile and hard to predict accurately due to the uncertainties affecting stock prices. However, investors and stock traders can only benefit from such models by making informed decisions about buying, holding, or investing in stocks. Also, financial institutions can use such models to manage risk and optimize their customers' investment portfolios. In this paper, we use the Long Short-Term Memory (LSTM-RNN) Recurrent Neural Networks (RNN) to predict the daily closing price of the Amazon Inc. stock (ticker symbol: AMZN). We study the influence of various hyperparameters in the model to see what factors the predictive power of the model. The root mean squared error (RMSE) on the training was 2.51 with a mean absolute percentage error (MAPE) of 1.84%.
DO - Stock Closing Price and Trend Prediction with LSTM-RNN
TI - 10.31586/jaibd.2024.877
ER -
