Forecasting economic recession through share price in the logistics industry with artificial intelligence (AI)

Y. M. Tang, Ka Yin Chau, Wenqiang Li, T. W. Wan

Research output: Journal article publicationJournal articleAcademic researchpeer-review


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.

Original languageEnglish
Article number70
Pages (from-to)1-12
Issue number3
Publication statusPublished - Sep 2020


  • Artificial intelligence
  • Big data
  • Logistics
  • Long short-term memory
  • Share price
  • Stock quote

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)
  • Modelling and Simulation
  • Applied Mathematics

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