A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction

Abstract The stock market is very unstable and volatile due to several factors such as public sentiments, economic factors and more. Several Petabytes volumes of data are generated every second from different sources, which affect the stock market. A fair and efficient fusion of these data sources (factors) into intelligence is expected to offer better prediction accuracy on the stock market. However, integrating these factors from different data sources as one dataset for market analysis is seen as challenging because they come in a different format (numerical or text). In this study, we propose a novel multi-source information-fusion stock price prediction framework based on a hybrid deep neural network architecture (Convolution Neural Networks (CNN) and Long Short-Term Memory (LSTM)) named IKN-ConvLSTM. Precisely, we design a predictive framework to integrate stock-related information from six (6) heterogeneous sources. Secondly, we construct a base model using CNN, and random search algorithm as a feature selector to optimise our initial training parameters. Finally, a stacked LSTM network is fine-tuned by using the tuned parameter (features) from the base-model to enhance prediction accuracy. Our approach's emperical evaluation was carried out with stock data (January 3, 2017, to January 31, 2020) from the Ghana Stock Exchange (GSE). The results show a good prediction accuracy of 98.31%, specificity (0.9975), sensitivity (0.8939%) and F-score (0.9672) of the amalgamated dataset compared with the distinct dataset. Based on the study outcome, it can be concluded that efficient information fusion of different stock price indicators as a single data source for market prediction offer high prediction accuracy than individual data sources.