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http://hdl.handle.net/10397/88328
Title: | Forecasting economic recession through share price in the logistics industry with artificial intelligence (AI) | Authors: | Tang, YM Chau, KY Li, W Wan, TW |
Issue Date: | Sep-2020 | Source: | Computation, Sept. 2020, v. 8, no. 3, 70 | Abstract: | Time series forecasting technology and related applications for stock price forecasting are gradually receiving attention. These approaches can be a great help in making decisions based on historical information to predict possible future situations. This research aims at establishing forecasting models with deep learning technology for share price prediction in the logistics industry. The historical share price data of five logistics companies in Hong Kong were collected and trained with various time series forecasting algorithms. Based on the Mean Absolute Percentage Error (MAPE) results, we adopted Long Short-Term Memory (LSTM) as the methodology to further predict share price. The proposed LSTM model was trained with different hyperparameters and validated by the Root Mean Square Error (RMSE). In this study, we found various optimal parameters for the proposed LSTM model for six different logistics stocks in Hong Kong, and the best RMSE result was 0.43%. Finally, we can forecast economic recessions through the prediction of the stocks, using the LSTM model. | Keywords: | Artificial intelligence Big data Logistics Long short-term memory Share price Stock quote |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | Computation | EISSN: | 2079-3197 | DOI: | 10.3390/COMPUTATION8030070 | Rights: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). The following publication Tang Y, Chau K-Y, Li W, Wan T. Forecasting Economic Recession through Share Price in the Logistics Industry with Artificial Intelligence (AI). Computation. 2020; 8(3):70, is available at https://doi.org/10.3390/computation8030070 |
Appears in Collections: | Journal/Magazine Article |
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Tang_Forecasting_economic_recession.pdf | 1.04 MB | Adobe PDF | View/Open |
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