The use of neural networks, GARCH models, and the Bollinger Bands technical indicator for stock trading decision making
Keywords and Phrases
Bollinger Bands are a widely used technical indicator for measuring and displaying the volatility of securities. The bands accomplish this by showing whether prices are high with the use of an upper band, and whether they are low with the use of a lower band. The bands are based on the volatility (standard deviation) of the past price data. This indicator can aid in rigorous pattern recognition and is useful in comparing current price action to possible buy and sell signals, helping to arrive at a self-contained systematic trading decision. However, due to its inherent characteristics, the indicator can provide false signals during trading in some trending markets. The research in this thesis develops two modified models, one combining neural networks with the Bollinger Bands technical indicator, and another incorporating a GARCH-in-mean model with the Bollinger Bands technical indicator to predict and trade on the security trend. The assumption of the combined system is that the neural network or GARCH model will help to overcome the lagging aspects of the Bollinger Bands indicator by providing a next day forecast, allowing the trader to make the correct trading decisions. The profitability of the model is tested using 10 American stocks and indexes"--Abstract, leaf iii.
Engineering Management and Systems Engineering
M.S. in Engineering Management
University of Missouri--Rolla
viii, 86 leaves
© 2005 Yanqiong Dong, All rights reserved.
Thesis - Citation
Stocks -- Prices -- Mathematical models
Stock price forecasting
Neural networks (Computer science)
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://laurel.lso.missouri.edu/record=b5664786~S5
Dong, Yanqiong, "The use of neural networks, GARCH models, and the Bollinger Bands technical indicator for stock trading decision making" (2005). Masters Theses. 5847.
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