A novel hybrid ARIMA-LSTM model for maritime shipping stock forecasting: comparative evidence against statistical and machine learning benchmarks

Kelly Gerakoudi, Dimitrios Georgoulas and Peter J. Stavroulakis

Maritime Business Review, 2025, vol. 11, issue 1, 2-20

Abstract: PurposeThe sale and purchase of financial commodities has gained importance in recent years, leading to a growing and diverse interest in shipping companies’ investment. However, due to the shipping sector’s idiosyncratic features, investors and researchers constantly seek novel and more accurate forecasting techniques.Design/methodology/approachThis research involves comparing a traditional econometric model, ARIMA, with decision tree regression, random forest model and an LSTM neural network model to predict stock prices for six maritime companies. Additionally, it proposes a hybrid ARIMA-LSTM model that leverages the strengths of both approaches.FindingsThe LSTM model outperforms the ARIMA model in all six cases, based on MAE, MSE and MAPE metrics. In addition, ARIMA dominates decision tree regression and random forest models, in the majority of cases. It also demonstrates the superiority of the proposed model. The study demonstrates that models can conduct highly accurate predictions of maritime stocks using only their past values despite their strong dependence on macroeconomic factors.Research limitations/implicationsThe findings extend the application of LSTM models and artificial neural networks to the prediction of an asset type that has been underexplored in the literature. Additionally, the proposed model can be applied to the prediction of other types of stocks.Originality/valueWe highlight the type of connection between a maritime stock and its past values. Additionally, we propose a new hybrid model for predicting the daily prices of maritime stocks and a strategy for selecting a forecasting model based on user requirements for accuracy and preparation time.

Keywords: ARIMA; Deep learning; LSTM; Shipping industry; Stock price forecasting; ARIMA-LSTM (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/mabr/article/11/1/2/127081 ... M-model-for-maritime (text/html)
https://www.emerald.com/mabr/article-pdf/11/1/2/10044493/mabr-04-2024-0035en.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:mabrpp:mabr-04-2024-0035

DOI: 10.1108/MABR-04-2024-0035

Access Statistics for this article

Maritime Business Review is currently edited by Chin-Shan Lu and Tsz Leung Yip

More articles in Maritime Business Review from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().