Investigation of investment in stock market using deep learning models

Abstract

Investing becomes an integral part of the financial world. As one market gamer encourages the launch or continuation of existing activities, the other players in the market are earning their shares. Therefore, each investor looks for the best solution, tactics and a method that will help to profitably determine the direction of the movement of shares. However, investors face difficulties in predicting stock variation forecasts. Often the chosen and used method is not an accurate forecasting tool, so investors focus on deep learning methods that become a support system for the investor. The article examines the comparison of the forecasts for the schedules received in deep learning and technical analysis methods, and the uncertainty about the predictability of share prices. The results of this work remain relevant and relevant, as studies relate to the use of a deep learning approach in investment, which is heavily analysed and investigated by researchers, and is still attempting to understand the success of its utilisation by investing.