Predicting NASDAQ Stock Exchange Rate Using Artificial Neural Network
This study investigates the effectiveness of artificial neural networks in forecasting the daily NASDAQ stock exchange rate. Various feed-forward ANN models trained with the back propagation algorithm were evaluated using short-term historical stock prices and day of the week as inputs. The models were developed and validated using NASDAQ data from January 28, 2015, to June 18, 2015. Different input datasets were considered, and predictive models for NASDAQ index based on prior days' data were analyzed. The methodology, results, and comparison of prediction methods are discussed in detail in this research.
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Presentation Transcript
Stock market index prediction using artificial neural network Amin Hedayati Moghaddam, Moein Hedayati Moghaddam, Morteza Esfandyari Journal of Economics, Finance and Administrative Science Volume 21, Issue 41, December 2016, Pages 89-93 Presenter: Cheng-Han Wu Date:2018/3/13
Abstract In this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ stock exchange rate was investigated. Several feed forward ANNs that were trained by the back propagation algorithm have been assessed. The methodology used in this study considered the short-term historical stock prices as well as the day of week as inputs. Daily stock exchange rates of NASDAQ from January 28, 2015 to 18 June, 2015 are used to develop a robust model. First 70 days (January 28 to March 7) are selected as training dataset and the last 29 days are used for testing the model prediction ability. Networks for NASDAQ index prediction for two type of input dataset (four prior days and nine prior days) were developed and validated.
Artificial neural network ? ??= ?=1 ????? ??= ?(??+ ??) ? ? = ?(? ? 1 ,? ? 2 , ? ? 3 , ,? ? ? ,?(?)) from 2015/1/28~2015/6/18
Predicting NASDAQ index 2 ????. ?????. Determination coefficient : ?2= 1 2 ????. ? 2 ?????. ????. Mean square error : MSE = , ? is total number of data ?
Result Levenberg-Marquardt (LM) One Step Secant (OSS) Gradient Descent with- adaptive learning rate (GDA)
Result Four prior days Nine prior days
Four prior days Nine prior days
