The stock market is regarded as volatile, complex, tumultuous, and dynamic. Forecasting stock performance has proven to be a challenging end
The stock market is regarded as volatile, complex, tumultuous, and dynamic. Forecasting stock performance has proven to be a challenging endeavour due to its increasing need for investment and growth prospects. At the forefront of machine learning, deep learning models facilitate the straightforward and efficient exploration and identification of optimal stocks, the hybrid forecasting models (LSTM and ARIMA) are used in the prediction of stock increase. This paper incorporated the MCDM technique to determine the optimal stocks for investment. The Analytic Hierarchy Process (AHP) is used to assign weights to various financial factors. These weights are then used by the Technique for Order Preference by Similarity to Ideal solution (TOPSIS) technique, which is a component Multi Criteria Decision Making method (MCDM), to compute and rank the optimal stocks for investment. Stock analysis involves considering numerous criteria and sub-criteria, which might lead to an unsuitable answer. To address this uncertainty, we utilize Neutrosophic Treesoft sets, which primarily handle numerous criteria, sub-criteria, and an increased number of sub-sub-criteria. Given a larger number of criteria, we will be capable of providing a precise solution to the problem. Furthermore, the definitions of fuzzy treesoft sets and neutrosophic treesoft sets have been presented for the first time. A plotly graph is generated to compare the real and projected stock prices for all the equities. All these are implemented using the program language python, which seems to be simple and easily understandable when compared to the other programming languages like Julia, MATLAB and so on. This hybrid methodology facilitates the forecast of stock prices, the ranking of stocks based on several financial and non-financial factors using AHP and TOPSIS, and the visualization of the outcomes. [ABSTRACT FROM AUTHOR]
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