Stock market index prediction using artificial neural network

By Amin Hedayati Moghaddam, Moein Hedayati Moghaddam, Morteza Esfandyari. 2016

276 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research

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Citations in the corpus

NSE Stock Market Prediction Using Deep-Learning Models (2018, background): “A Network has three section of layers input, hidden and output layers [8].”
Hardware Trojan Attacks on Neural Networks (2018, background): “The rapid evolution of machine learning has advanced numerous research ﬁelds and industries, including safety-critical areas such as biometric security, autonomous driving, cyber-security, health, and ﬁnancial planning [1]–[7].”
CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, background): “In (Moghaddam et al., 2016), authors predicted NASDAQ prices based on the historical price of four and nine days ago.”