A New Stock Price Forecasting Method Using Active Deep Learning Approach
A New Stock Price Forecasting Method Using Active Deep Learning Approach
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DOI：
10.3390/joitmc8020096
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发表时间：
2022-05
影响因子：
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通讯作者：
Khalid Alkhatib;Huthaifa Khazaleh;H. Alkhazaleh;A. Alsoud;L. Abualigah
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作者：
Khalid Alkhatib;Huthaifa Khazaleh;H. Alkhazaleh;A. Alsoud;L. Abualigah
股票价格预测是一个重要的研究领域，因为它对个人，公司和政府的利益至关重要。本研究探讨应用新方法预测特定公司调整后的收盘价。一组新的特征用于通过创建六个特征集（包括高、低、量、开、HiLo、OpSe）而不是传统的四个特征集（高、低、量、开）来提高给出更准确结果的可能性，同时减少损失。该研究还通过使用不同大小的数据集（Apple，ExxonMobil，Tesla，Snapchat）来研究数据大小的影响，以促进开放式创新动态。还考虑了商业部门对损失结果的影响。最后，该研究包括六个深度学习模型，MLP，GRU，LSTM，Bi-LSTM，CNN和CNN-LSTM，以预测股票的调整后收盘价。使用的六个变量（高，低，开放，容量，HiLo和OpSe）根据模型的结果进行评估，显示出比利用原始特征集的原始方法更少的损失。结果表明，使用新方法改进了基于LSTM的模型，尽管所有模型都显示出比较结果，其中没有模型显示出更好的结果或连续优于其他模型。最后，添加的新特征对预测模型的性能产生了积极影响。
Stock price prediction is a significant research field due to its importance in terms of benefits for individuals, corporations, and governments. This research explores the application of the new approach to predict the adjusted closing price of a specific corporation. A new set of features is used to enhance the possibility of giving more accurate results with fewer losses by creating a six-feature set (that includes High, Low, Volume, Open, HiLo, OpSe), rather than the traditional four-feature set (High, Low, Volume, Open). The study also investigates the effect of data size by using datasets (Apple, ExxonMobil, Tesla, Snapchat) of different sizes to boost open innovation dynamics. The effect of the business sector in terms of the loss result is also considered. Finally, the study included six deep learning models, MLP, GRU, LSTM, Bi-LSTM, CNN, and CNN-LSTM, to predict the adjusted closing price of the stocks. The six variables used (High, Low, Open, Volume, HiLo, and OpSe) are evaluated according to the model’s outcome, showing fewer losses than the original approach, which utilizes the original feature set. The results show that LSTM-based models improved using the new approach, even though all models showed a comparative result wherein no model showed better results or continuously outperformed other models. Finally, the added new features positively affected the prediction models’ performance.