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Forecasting Stock Prices For Maritime Shipping Company In Covid-19 Period Using Multivariate Multi-Step Multi-Step Convolutional Neural Network - Bidirectional Long Short-Term Memory

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  • Ahmad GHAREEB
  • Mihai Daniel ROMAN

Abstract

The COVID-19 pandemic has triggered a global crisis in health and economic sectors, causing profound impacts on sea transport and trade. This research paper investigates the ramifications of the pandemic on maritime shipping prices, and, hence, their subsequent influence on the stocks of shipping companies. This global upheaval disrupted international trade significantly, resulting in an unprecedented demand surge for shipping services and a substantial spike in freight rates. This study is intended to propose a predictive method based on Multivariate Multi-step convolutional neural network - Bidirectional Long Short-Term Memory (Multivariate Multi-step CNN-BiLSTM) networks in order to forecast the prices of three of the most prominent stocks of big organizations operating in maritime transport. The proposed method is composed of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), where the research utilizes CNN to help for feature extraction from the inputted data, alongside BiLSTM that forecasts the closing stock price for the upcoming five days, utilizing the extracted feature data. Hence, stock price prediction can be realized by applying a novel optimization strategy, which was founded on the Multivariate Multi-step CNN-BiLSTM model and utilizing the Adam optimizer. Prediction accuracy can be assessed by incorporating four metrics into the system: Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, and Median Absolute Percentage Error. Experimental findings demonstrate that Multivariate Multi-step CNN-BiLSTM yields the most dependable stock price forecasts with the highest accuracy. The proposed prediction method, correctly applied, can yield economic benefits at the macr and micro levels; the prediction accuracy can help policy makers make better future outlook estimates in relation to inflation, gross domestic product (GDP), and unemployment levels that might be impacted by the volatile, uncontrolled, or unexpected fluctuations of stock prices of some leading economic sectors that are closely connected to global shipping and supply chain operations; thus, leading to serious impacts at the microeconomic level in relation to costs, supply and demand, and behavior of individual consumers and companies.

Suggested Citation

  • Ahmad GHAREEB & Mihai Daniel ROMAN, 2025. "Forecasting Stock Prices For Maritime Shipping Company In Covid-19 Period Using Multivariate Multi-Step Multi-Step Convolutional Neural Network - Bidirectional Long Short-Term Memory," Eastern European Journal for Regional Studies (EEJRS), Center for Studies in European Integration (CSEI), Academy of Economic Studies of Moldova (ASEM), vol. 11(1), pages 6-24, June.
    • Handle: RePEc:aem:journl:v:11:y:2025:i:1:p:6-24
    DOI: https://doi.org/10.53486/2537-6179.11-1.01
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    References listed on IDEAS

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    1. Cao, Jian & Li, Zhi & Li, Jian, 2019. "Financial time series forecasting model based on CEEMDAN and LSTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 127-139.
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation

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