Predicting Stock Prices Using LSTM

By Murtaza Roondiwala, Harshal Patel, S. Varma. 2017

260 citations recorded by the source corpus.

: 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

Subfields: Management Science and Operations Research

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In Management Science and Operations Research

Citations in the corpus

Forecasting Economics and Financial Time Series: ARIMA vs. LSTM (2018, background): “…[Hochreiter and Schmidhuber, 1997; Gers, 2000; Yim, 2002; Zhang, 2005; Graves et al., 2009, 2013; Schmidhuber, 2015; Brownlee, 2016; Gamboa, 2017; Roondiwala, 2017] as well as its applications in economics and finance data such as predicting the volatility of the S&P 500 [Kohzadi, 1996; Giles at…”
Predicting Equity Price with Corporate Action Events Using LSTM-RNN (2018, background): “[2] compared the accuracy of forecast of the stock price by LSTM-RNN when the stock price of NIFTY50 stocks of National Stock Exchange of India is combined with the data of Open, Close, High, and Low respectively.”
CNN-LSTM Neural Network Model for Quantitative Strategy Analysis in Stock Markets (2017, background): “With these memory cells, networks are able to eﬀectively associate memories and input remote in time, hence suit to grasp the structure of stock data dynamically over time with high prediction capacity [6].”