Predicting Stock Prices Using LSTM

Murtaza Roondiwala, Harshal Patel, Shraddha Varma

Abstract: The art of forecasting the stock prices has been a difficult task for many of the researchers and analysts. In fact, investors are highly interested in the research area of stock price prediction. For a good and successful investment, many investors are keen in knowing the future situation of the stock market. Good and effective prediction systems for stock market help traders, investors, and analyst by providing supportive information like the future direction of the stock market. In this work, we present a recurrent neural network (RNN) and Long Short-Term Memory (LSTM) approach to predict stock market indices.

Keywords: Long short-term memory LSTM, recurrent neural network RNN, nifty 50, root mean square error RMSE, prediction, stock prices

How to Cite?: Murtaza Roondiwala, Harshal Patel, Shraddha Varma, "Predicting Stock Prices Using LSTM", Volume 6 Issue 4, April 2017, International Journal of Science and Research (IJSR), Pages: 1754-1756, https://www.ijsr.net/getabstract.php?paperid=ART20172755, DOI: https://dx.doi.org/10.21275/ART20172755