An Element Gathering Optimization-Based Probabilistic Multi-model Neural Network Classification Algorithm for Stock Market Prediction

Stock market prediction is the practice of predicting the performance of a specific stock or the market as a whole using statistical and machine learning approaches. Stock market forecasting is to give investors a better knowledge of the prospective risks and benefits of purchasing or selling a specific stock. Since machine learning algorithms create predictive models that can anticipate future stock prices or market trends using previous market data as well as other pertinent variables, this approach fills any missing information by utilizing a preprocessed supervised algorithm called as, depth adjacent surrounding (DSA). Then, a novel optimization algorithm, known as, element gathering (EG) is used to optimize the required features extracted from the data. This process aids in lowering the quantity of input information needed to generate reliable predictions and enhances the effectiveness of the prediction model. Based on the input data, a probabilistic multi-model neural network (PMNN) predicts whether to buy or not to buy a stock. This neural network combines the results of various distinct models to provide a single, more precise and reliable forecast. The main objective of this procedure is to create a tool that can assist investors in making more informed choices regarding whether to invest in a specific stock or not, based on a data-driven study of pertinent market and company data. In comparison with more conventional approaches, this strategy can offer a more accurate and dependable prediction of stock performance by employing machine learning algorithms to analyze and understand massive amounts of data.