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Title: Stock trading and prediction using deep learning neural network
Authors: Cheam, Nicholas Yen Kait
Keywords: DRNTU::Engineering
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Abstract: Everyday millions of shares trade, with an overall value of a few hundred million. This is due to stockbrokers, traders, stock analysts, portfolio managers or investment bankers trading shares to get monetary gains. However, with the stock market's volatility there is no definite guarantee of profiting. In some severe cases the market may crash. These crashes resulted in devastating losses for most, if not all, of the players in the stock market. In this paper, we will look at the various models people have used to predict stock prices in order to make gains, investigate if development in deep learning neural network models are an improvement over existing models and to test out various parameters to get more accurate predictions of stock prices.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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