Speculation of Stock Marketing Using Advanced Recursive Techniques

Abstract

In the current scenario, the economic status of countries is dependent on stock markets. However, predicting the future prices of any stock is a multifaceted task, as the nature of data is complex and unstructured in nature, which is difficult understand. The focus of the study relies on applying deep neural techniques with regression-based application to discover knowledge from financial databases. The authors have applied LSTM, an advanced version of RNN, and regression-based methods such as ARIMA for predicting future prices of stocks. The study was supported by implementing the techniques on real-world data that was captured from SBI for 6 years. The data has significant opening and closing prices of stock markets. To implement the current study approach, the authors have utilized Python language, where result predicts various performance parameters such as MAE, MSE, RMSE, and bias for both LSTM as well as ARIMA. The performance matrix of LSTM and ARIMA were compared for MAE (mean absolute error) for LSTM, which is 4.32, whereas for ARIMA is 3.83. Also, MSE (mean squared error) value for LSTM is 29.52, for ARIMA was 24.53, and RMSE (root mean squared error) for LSTM and ARIMA are 5.43 and 4.95. The overall accuracy of both of the algorithm were widely applied for real-world prediction among the stock market analysis.