Maiti, A.Shetty D, P.2026-02-062020IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2020, Vol.2020-November, , p. 1215-122021593442https://doi.org/10.1109/TENCON50793.2020.9293712https://idr.nitk.ac.in/handle/123456789/30565In this paper, we predict the stock prices of five companies listed on India's National Stock Exchange (NSE) using two models- the Long Short Term Memory (LSTM) model and the Generative Adversarial Network (GAN) model with LSTM as the generator and a simple dense neural network as the discriminant. Both models take the online published historical stock-price data as input and produce the prediction of the closing price for the next trading day. To emulate the thought process of a real trader, our implementation applies the technique of rolling segmentation for the partition of training and testing dataset to examine the effect of different interval partitions on the prediction performance. © 2020 IEEE.DiscriminantGANGeneratorLSTMNeural networksRolling segmentationTechnical indicators