##### URI
https://hdl.handle.net/10355/56058
 dc.contributor.advisor He, Zhihai, 1973- eng dc.contributor.author Gao, Qiyuan eng dc.date.issued 2016 eng dc.date.submitted 2016 Spring eng dc.description.abstract In this research, we study the problem of stock market forecasting using Recurrent Neural Network(RNN) with Long Short-Term Memory (LSTM). The purpose of this research is to examine the feasibility and performance of LSTM in stock market forecasting. We optimize the LSTM model by testing different configurations, i.e., the number of neurons in hidden layers and number of samples in sequence. Instead of using daily stock price data, we collect hourly stock data from the IQFEED database in order to train our model with relatively low noise samples. Nevertheless, based on the prediction results of LSTM model, we build up a stock database with six U.S market stocks from five different industries. The average test accuracy of these six stocks is 54.83%, where the highest accuracy is at 59.5% while the lowest is at 49.75%. We then develop a trade simulator to evaluate the performance of our model by investing the portfolio within a period of 400 hours, the total profit gained by the model is $413,233.33 with$6,000,000 initial investment. eng dc.identifier.uri https://hdl.handle.net/10355/56058 dc.language English eng dc.publisher University of Missouri--Columbia eng dc.relation.ispartofcommunity University of Missouri--Columbia. Graduate School. Theses and Dissertations eng dc.rights OpenAccess. eng dc.rights.license This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. dc.title Stock market forecasting using recurrent neural network eng dc.type Thesis eng thesis.degree.discipline Computer engineering (MU) eng thesis.degree.grantor University of Missouri--Columbia eng thesis.degree.level Masters eng thesis.degree.name M.S. eng
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