Novel optimization approach for stock price forecasting using multi-layered sequential LSTM

Abstract

Stock markets can often be one of the most volatile places to invest. Statistical analysis of past stock performance and external factors play a major role in the decision to buy or sell stocks. These factors are all used to maximize profits. Stock price index forecasting has been a subject of great research for many years, and several machine learning and deep learning algorithms have been proposed to simplify this complex task, but little success has been found so far. In order to forecast stocks accurately, it is crucial to understand the context-specific dependence of stock prices on their past values. The use of Long Short Term Memory (LSTM), which is capable of understanding long-term data dependencies, can help overcome this obstacle. In this context, this paper proposes a novel optimization approach for stock price prediction that is based on a Multi-Layer Sequential Long Short Term Memory (MLS LSTM) model which makes use of the adam optimizer. Additionally, the MLS LSTM algorithm uses normalized time series data divided into time steps to determine the relationship between past values and future values in order to make accurate predictions. Furthermore, it eliminates the vanishing gradient problem associated with simple recurrent neural networks. The stock price index is forecasted by taking into account past performance information along with past trends and patterns. The results illustrate that a 95.9% prediction accuracy is achieved on the training data set and a 98.1% accuracy on the testing data set with the MLS LSTM algorithm, which dramatically exceeds the performance of other machine learning and deep learning algorithms. The mean absolute percentage error was observed to be 1.79% on the training set and 2.18% on the testing set, respectively. Moreover, the proposed model is able to estimate the stock price with a normalized root mean squared error of 0.019, thus giving an accurate forecast and making it a feasible real-world solution.