Using Technical Indicators and Artificial Neural Networks (ANN) to Predict the Movement of Stock Prices «An Applied Study on the Amman Stock Exchange»

Abstract

The need for the existence of specialized institutions contributed to transferring funds from entities that enjoy a surplus from them to entities that suffer from a deficit and that can collect these surpluses and direct them towards investment areas as the traded financial instruments varied. The most important of which was the common stock being the most prevalent in the financial markets where the success of investment and financial decisions depends on the availability of the necessary information for decision makers that rely on logical scientific analysis methods. Therefore, in the fields of artificial intelligence, neuronal networks have emerged as one of the techniques of data mining that helps in discovering previously unknown patterns of large amounts of data, and accessing interpretable models in order to extract knowledge and support the decision - making process.
The research is based on the use of the WEKA tool, using the multi - layer perceptron of the neuronal network, multi - layer network with a reverse spread, and technical indicators for predicting the movement of historical stock prices for one day, based on the modeling of some technical transactions to reach the future price of the share within a day.
The research focuses on the possibility of building a neural network model based on the modeling of some technical transactions to predict the future movement of stock prices.