Shipping Market Forecasting Considering the Individual Investor Emotions: A Novel Twofold Partial Swarm Optimization Based Stacked Long Short-Term Memory Model

Abstract

ABSTRACT Purpose The general purpose of shipping market prediction is to help shipping analysts design a strategy to make shipping strategies. Previous studies have pointed out human emotions has an obvious impact on markets, however, few research focus on utilizing human emotions to improve the shipping market forecasting issue. This paper fills the gap in the literature of considering industrial individual emotions to shipping market prediction issue Methodology We proposed a conceptual framework of an emotion-based shipping market prediction (ESMP) system focused on considering the multidimensional emotions of individual investors. To make it precisely, a two-fold Particle Swarm Optimization (TFPSO) algorithm is proposed conduct the shipping forecasting under the framework of ESMP, with advantages of automatically finding the architecture and hyperparameters of the Deep Long Short-Term Memory (DLSTM) simultaneously in the shipping market forecasting the values of the Baltic Dry, Dirty Tanker and Container indices of ocean transportation in the world ocean during the crisis period 2010–2022 Findings The prediction accuracy of the ESMP was higher than that of the models using the conventional factors in all learning periods of the study, and it was determined that the results complement the sentiment indicator employed to predict the shipping indices. Additionally, the accuracy of the proposed method is superior to conventional neural network models in all used error metrics. Additional Mann–Whitney U test on MSE difference between TFPSO-DLSTM and compared models demonstrates that the significant advantages boosted by TFPSO-DLSTM Originality Shipping sentiment has provided further proof that shipping decisions are significantly driven by emotions. our study shows that individual shipping investors’ sentiment are able to improve shipping rate forecasts significantly, although the magnitudes of the improvements are relatively small from an economic point of view Research implications This study undertook the initiative to procure BDI, BDTI, BCTI, COVID-19 data and shipping sentiment index data through in-depth interviews to provide first-hand perspectives into whether shipping sentiments impact the shipping market trend. It builds upon existing literature on the present stance of deep learning models, which largely relies non-sentiment factors. The study also extends prior literature on hyperparameter searching methods by highlighting the structure of searching method. Besides this, the contribution of this study also aligns with the prior discussions on evolving methodologies in temporal forecasting research Practical implications This study implies that shipping sentiments enables to make better shipping marketing trends, and foster sustainable growth. Shipping fear index serves for measuring investors’ attitude and mood toward shipping markets in terms of the general and certain sectors or assets, possibly promoting the movement of price and providing long-term investors and active traders with trading or arbitrage opportunities. Due to the complexities and regulations of the shipping market, it is essential to collaborate exclusively with shipping sentiment indices with artificial intelligent model in shipping market forecasting research