Determining the Best Activation Functions for Predicting Stock Prices in Different (Stock Exchanges) Through Multivariable Time Series Forecasting of LSTM

LSTM (Long Short-Term Memory) has proven its worth in terms of predicting Stock prices through questioning market conditions. This research focuses on the quality of LSTM predictions when various activation functions are applied within the context of noisy market data. In this research, we have used 25 different stocks from diverse stock exchanges and observed the predictions created by different activation functions such as Relu, Elu, and TanH. Our research would involve this accuracy within the context of average loss accumulation and price predictions for the stock sample. The market conditions will imply the features of similar epoch runs, and the same training and testing period, which are irrespective of SE and LSTM feature parameters defined by market-benefitting suggestions. This research has found an accuracy of 80% through the multivariable prediction method derived from the Hyperbolic Tangent activation function, suggesting that this function is the best for price prediction based on LSTM through the multivariable method.