Indian Stock-Market Prediction using Stacked LSTM AND Multi-Layered Perceptron
Siddharth Banyal1, Pushkar Goel2, Deepank Grover3

1Siddharth Banyal*, Maharaja Agrasen Institute of Technology Rohini Sector, New Delhi.
2Pushkar Goel, Maharaja Agrasen Institute of Technology, Rohini Sector, New Delhi.
3Deepank Grover, Maharaja Agrasen Institute of Technology Rohini Sector, New Delhi.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 24, 2019. | Manuscript published on January 10, 2020. | PP: 1051-1055 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8026019320/2020©BEIESP | DOI: 10.35940/ijitee.C8026.019320
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Abstract: We aim to construe the Stacked Long–Short term memory (LSTM) and Multi-layered perceptron intended for the NSE-Stock Market prediction. Stock market prediction can be instrumental in determining the future value of a company stock.It is imperative to say that a successful prediction of a stock’s future price could yield significant profit which would be beneficial for those who invested in the pipeline of things including stock market prediction. The model uses the information pertaining to the stocks and contemplates the previous model accuracy to innovate the approach used in our paper. The experimental evaluation is based on the historical data set of National Stock Exchange (NSE). The proposed approach aims to provide models like Stacked LSTM and MLP which perform better than its contemporaries which have been achieved to a certain extent. This can be verified by the results embedded in the paper . The future research can be focused on adding more variables to the model by fetching live data from the internet as well as improving model by selecting more critical factors by ensemble learning. 
Keywords: Deep Learning, Recurrant Neural Network, Stacked LSTM, Multi layered Perceptron Stock Market Prediction, Mean Squared Error, Mean Absolute Percentage Error
Scope of the Article: Deep Learning