RNNLBL : A Recurrent Neural Network and Log Bilinear based Efficient Stock Forecasting Model

Abstract

Recent years have seen the wide use of Time series forecasting (TSF) for predicting the future price stock, modeling and analyzing of finance time series helps in guiding the trades and investors decision. Moreover considering the stock as the dynamic environment, it is pronounced as the non-linearity of time series which affects the stock price forecast immediately. Hence, in this research work we propose intelligent TSF model, which helps in forecasting the early prediction of stock prices. The proposed stock price forecasting model employed both short-term (i.e. recent behavior fluctuation) using log bilinear (LBL) model and long-term (i.e., historical) behavior using recurrent neural network (RNN) based LSTM (long short term memory )model. Subsequently, this model is mainly helpful for the home brokers since they do not possess enough knowledge about the stock market. Proposed RNNLBL hybrid model shows the satisfying forecasting performance, these results in overall profit for the investors and trades. Furthermore, proposed model possesses a promising forecasting in case of the non-linear time series since the pattern of non-linear pattern are highly improbable to capture through these state-of-art stock price forecasting models.

Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model

“…This section presents the performance evaluation of the proposed SASPF model over other prediction models. The GAN-FD model [15] was chosen for comparison as it achieved much better results than existing LSTM based stock forecasting methods [21,[24][25][26][27][28], as investors' sentiments are considered as a major contributing parameter. Results obtained for sentiment index (positive, negative, neutral and compound).…”