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Forecasting Economic Recession through Share Price in the Logistics Industry with Artificial Intelligence (AI)

by YM Tang 1, Ka-Yin Chau 2, Wenqiang Li 1,* and TW Wan 1
1
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
2
Faculty of International Tourism and Management, City University of Macau, Macau, China
*
Author to whom correspondence should be addressed.
Computation 2020, 8(3), 70; https://doi.org/10.3390/computation8030070
Received: 8 July 2020 / Revised: 27 July 2020 / Accepted: 31 July 2020 / Published: 3 August 2020
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. View Full-Text
Keywords: artificial intelligence; share price; stock quote; long short-term memory; logistics; big data artificial intelligence; share price; stock quote; long short-term memory; logistics; big data
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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, 70.

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