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A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction
Journal of Big Data ( IF 8.1 ) Pub Date : 2021-01-09 , DOI: 10.1186/s40537-020-00400-y
Isaac Kofi Nti , Adebayo Felix Adekoya , Benjamin Asubam Weyori

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.



中文翻译:

基于深度神经网络的新型多源信息融合预测框架

由于公众情绪,经济因素等多种因素,股票市场非常不稳定和动荡。每秒从不同来源生成数PB的数据量,这会影响股票市场。将这些数据源(因素)公平有效地融合到情报中,有望在股市上提供更好的预测准确性。但是,将来自不同数据源的这些因素整合为一个用于市场分析的数据集被视为具有挑战性,因为它们采用不同的格式(数字或文本)。在这项研究中,我们提出了一种基于混合深度神经网络架构(卷积神经网络(CNN)和长短期记忆(LSTM))的新型多源信息融合股票价格预测框架,称为IKN-ConvLSTM。恰恰,我们设计了一个预测框架,以整合来自六(6)种异构来源的与股票相关的信息。其次,我们使用CNN构建基础模型,并使用随机搜索算法作为特征选择器来优化我们的初始训练参数。最后,通过使用来自基本模型的调整后的参数(特征)对堆叠的LSTM网络进行微调,以提高预测精度。我们对加纳证券交易所(GSE)的股票数据(2017年1月3日至2020年1月31日)进行了实证评估。结果显示,与不同的数据集相比,合并后的数据集的预测准确度达到98.31%,特异性(0.9975),灵敏度(0.8939%)和F评分(0.9672)。根据研究结果,

更新日期:2021-01-10
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