The research aims to estimate stock returns using artificial neural networks and to test the
performance of the Error Back Propagation network, for its effectiveness and accuracy in
predicting the returns of stocks and their potential in the field of financial markets and to
rationalize investor decisions. A sample of companies listed on the Iraq Stock Exchange was
selected with (38) stock for a time series spanning (120) months for the years (2010_2019).
The research found that there is a weakness in the network of Error Back Propagation
training and the identification of data patterns of stock returns as individual inputs feeding the
network due to the high fluctuation in the rates of returns leads to variation in proportions and
in different directions, negatively and positively.