Prediction of the investment decision in the Damascus Stock Exchange using Artificial Neural Networks

Abstract

The current research aims to  identify the most  market variables and the best companies able to  predict using Artificial Neural Networks. The research sample consists of all listed companies whose shares were traded from the date of January 1, 2015 until December 31, 2022, amounting to 23 companies. Market (return, trading value, trading volume, number of deals, share turnover rate, number of trading days, price index for each company, total risk, market value, earnings per share), in addition to the variable number of investors, the most important findings of the research:

The relative importance of market variables varies in their ability to predict the investment decision, as they came in the following order (total risks, returns, number of transactions, trading volume, expected monthly return, market value, trading value, share turnover rate, index for each company, earnings per share, number of trading days).

The relative importance of the companies listed on the Damascus Stock Exchange differs in terms of their ability to predict the investment decision according to market variables, where the best performance was for Al-Ahlia Company for the manufacture of vegetable oils and the worst performance for the Syrian National Insurance Company in terms of the possibility of relying on its data to predict the investment decision.