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This research analyzes the cycle of the dry bulk shipping market (DBSM) as a representative of spot and period charter rates in dry bulk shipping to develop strategies for investment timing (i.e. asset play) and fleet trading (chartering strategy).

Spectral analysis is a numerical approach to extract significant cyclicality, which may be utilized to develop trading strategies. Instead of working with a single dataset (univariate), a system approach can be utilized to observe a significant shipping market cycle in its multi-variate circumstance. In this paper, a system dynamics design is employed to extract cyclicality in the DBSM in its particular industrial environment. The system dynamic design has competitive forecasting accuracy relative to univariate time series models and artificial neural networks (ANNs) in terms of forecasting outcomes.

The results show that the system dynamic design has a better forecasting performance according to three evaluation metrics, mean absolute scale error (MASE), root mean square error (RMSE) and mean absolute percentage error (MAPE).

Cyclical analysis is a significantly useful instrument for shipping asset management, particularly in market entry–exit operations. This paper investigated the cyclical nature of the dry bulk shipping business and estimated significant business cycle periodicity at around 4.5-year frequency (i.e. the Kitchin cycle).

The occurrence and unpredictability of speculative bubbles on financial markets, and their accompanying crashes, have confounded economists and economic historians worldwide. The purpose of this paper is to diagnose and detect the bursting of shipping bubbles ex ante, and to qualify the patterns of shipping price dynamics and the bubble mechanics, so that appropriate counter measures can be taken in advance to reduce side effects arising from bubbles.

Log periodic power law (LPPL) model, developed in the past decade, is used to detect large market falls or “crashes” through modeling of the shipping price dynamics on a selection of three historical shipping bubbles over the period of 1985 to 2016. The method is based on a nonlinear least squares estimation that yields predictions of the most probable time of the regime switching.

It could be concluded that predictions by the LPPL model are quite dependent on the time at which they are conducted. Interestingly, the LPPL model could have predicted the substantial fall in the Baltic Dry Index during the recent global downturn, but not all crashes in the past. It is also found that the key ingredient that sets off an unsustainable growth process for shipping prices is the positive feedback. When the positive feedback starts, the burst of bubbles in shipping would be influenced by both endogenous and exogenous factors, which are crucial for the advanced warning of the market conversion.

The LPPL model has been first applied into the dry bulk shipping market to test a couple of shipping bubbles. The authors not only assess the predictability and robustness of the LPPL model but also expand the understanding of the model and explain patterns of shipping price dynamics and bubble mechanics.

The authors employ the vessels that comprise the dry bulk segment of the maritime industry and examine how market sentiment affects the herding behavior of shipping investors in a real asset market.

The authors employ a threshold regression model to examine how changes in market sentiment can affect herding behavior in oceanic dry bulk shipping.

The results show that the behavioral aspect of investing, measured through intentional and unintentional herding, contrary to the results for financial markets, is affected by sentiment on the buy side (newbuildings) but not on the sell side (scrapping). Furthermore, the authors provide evidence that when market sentiment is negative, investors tend to follow market leaders (intentional herding), while, when sentiment is positive, unintentional herding leads to common investment practices among shipping investors.

The results have significant implications both for academics and for practitioners since they reflect a clear distinction of the pattern of investment decisions for real assets, compared to financial assets.

Energy groups are cargo owners with large amounts of energy sources (such as coal) to transport. To achieve a satisfactory tradeoff between the reliability requirements of the sea transportation process and the need to control the investment cost, they usually set up a self-owned fleet supplemented by a chartered fleet. This paper aims to investigate the best fleet structure and to evaluate the investment scheme under volatile circumstances in the shipping market.

The authors construct a mathematical model to determine the ratio of the self-owned fleet to the total fleet to minimize fleet operating costs. The volatility of both freight rates and oil prices is taken into consideration. The CPLEX solver is used to empirically analyze real data from an energy group in China, and the ship investment plan is evaluated considering the technical and economic feasibility.

If the ratio of the self-owned fleet to the total fleet is increased to the optimal of 90.40%, the total operating cost is reduced by 33.98%. Thus, the energy group should increase its capacity with a Panamax vessel of approximately 82,000 DWT. Purchasing a 5-year-old secondhand ship and building a new ship both have good investment return indicators.

For cargo owners engaging in transporting bulk cargo domestically in China, the suggested fleet ratio can provide a reference with a universal application scale, given the boundary economic conditions (including the volatility of freight rates and oil prices in the shipping market) in the paper.

Since 1962 the world trades in bulk commodities as a whole have undergone two great changes: a large increase in the volume, and a large increase in the distance of the average route. In these years, trade in the five main dry bulk commodities doubled in tonnage, and in crude oil trebled in tonnage. During the same period, the length of the average route for the former rose from 3,400 miles to 4,650 miles, and for the latter from 4,500 miles to 6,750 miles. In short, three to five times as much ship capacity was required in 1973 as in 1962.

In the shipping industry, both sales and purchases of second-hand ships and freight transport services are prevalently tailormade and traded with intense bilateral negotiations. Price bargaining is the key step of this negotiation process and plays a crucial role in determining mutually agreed prices. Despite its cruciality and applicability, the price bargaining has yet received due conceptual and/or theoretical attention in the shipping literature. This paper attempts to conceptually examine the role of bargaining in shipping transaction prices and subsequently puts forward directions for future research. In doing so, the paper focuses on two types of transactions taking place in shipping markets: asset market trading of second-hand vessels and service market trading shipping freights.

The paper begins with a systematic literature review of price bargaining in the field of economics and management disciplines from a game-theoretic perspective. This approach does logically lead to the establishment of a conceptual framework for price bargaining in shipping sub-markets as a step toward having taken into consideration a variety of heterogeneities commonly present in trading activities and market dynamics.

A set of research areas has been consequently identified where price bargaining and mechanisms for the shipping freight and asset markets could be further explored and analyzed in a way to make better pricing decisions under a more tangible framework.

One of the critical challenges when using bargaining mechanisms to make a decision on pricing shipping services and assets is how to operationalize the study for empirical investigation as some of the factors are internal information of the players and are not adequately revealed to externals: that is, an imperfect information sharing case. The current study aims, however, not to conduct an empirical analysis but to initiate a conversation among maritime economists by bringing their attention to this not-yet fully explored and potentially impactful field of research and by asking them to treat bargaining from a perspective for pricing shipping assets and services. It is claimed that, by doing so, one could better understand price differences between individual contracts.

This study would be considered the first of its kind to provide a detailed survey of the bargaining theory and models from a game theoretical perspective as a theoretical lens to understand its importance and relevance in pricing shipping assets and services. It also provides a simplified operational case on utilizing bargaining in practically pricing freight services.

This study aims to propose a theoretical model to characterize the optimal forward freight agreement (FFA) procurement strategies and investigate the determinants of FFA trading activities from a new cross-market perspective.

A two-step model specification is used to empirically test the theoretical results for the Capesize, Panamax and Supramax sectors. It is found that spot demand has a positive relation with FFA trading volume for all three sectors. Moreover, spot demand volatility has a negative relation, while the correlation between spot demand and spot rate has a positive relation with FFA trading volume for the Capesize and Panamax sectors.

The results show that the expected spot demand is scaled by a “quantity premium,” which is the product of a demand covariance term, a demand riskiness term and a demand volatility term. This can be used by the traders in the FFA market to construct their hedging strategies.

The purpose of this study is to examine the impact of the corona virus (COVID-19) pandemic on stock returns of listed cargo shipping companies.

The author employs the events study methodology to examine this phenomenon. A sample of 49 listed cargo shipping companies in the container, dry bulk and tanker sub-sectors from Asia, North America, and Europe was selected and their daily closing stock prices from 1st January 2020 to 31st December 2020 were utilized.

The results reveal that there was an overall negative overreaction to the announcement by World Health Organization (WHO) that declared COVID-19 a pandemic. The approvals of USD 857 billion stimulus package by the European Union (EU) and Pfizer vaccine by Food and Drug Administration (FDA) in USA received slight positive reactions. The Greek, Singaporean and Taiwanese shipping stocks were the least affected stocks as their respective shipping industries remained resilient during 2020.

This study provides evidence to confirm the fact that COVID-19 has affected stock markets; however the impact is un parallel among cargo shipping stocks of different countries.

The majority of studies have conducted country level analyses of the COVID-19 and stock market performance phenomenon. However, there have been sectoral disparities in terms of their susceptibility to economic shocks from COVID-19. This study's focal point is on the cargo shipping sector which synonymous with other sectors has not been immune to the current pandemic. The study also extends the timeline of events to incorporate those from June to December 2020.