Valuation mismatch and shipping q indicator for shipping asset management

ABSTRACT

This paper aims to develop an adaptation of the Tobin Q investment model for the shipping asset management in order to monitor valuation mismatch and bubble pricing of shipping assets. In this circumstance, the market prices of various shipping assets (e.g., Capesize or Panamax dry bulk carriers in different age profiles) are compared to the measured long-term asset value with second-hand ship prices. The mark-to-market prices of shipping assets are led by current market trends and freight rates, while the long-term asset value is estimated by using past data under certain assumptions (mean reversion, trend reversion). The discrepancy between market prices and the long-term nominal value of a shipping asset reflects any mispricing, which in turn sheds light on investment timing and market entry-exit decision.     

Heuristic estimation of container stacking and reshuffling operations under the containership delay factor and mega-ship challenge

This paper addresses a highly researched area, the reshuffling problem in ports, using a new paradigm-modified containership service order in light of credit risk assessment. Container stacking and reshuffling operations can cause ship delays and additional risk. In deep-sea terminals, outbound containers are tightly stacked according to the retrieval sequence. Due to lack of space, terminals stack containers in multiple tiers. This means any delay in the arrival of a ship can impose extra handlings and reshuffling of containers delaying future cargo handling. This paper addresses the reshuffling problem with a concept similar to the credit scoring and rating of creditworthiness used in the banking industry. By utilizing this comparison to the banking credit risk concept, a heuristic estimation model is proposed that illustrates the side effects of unscheduled modifications in containership service order. Further, the mega-ship trend amplifies the reshuffling debate. Probability of delay, reshuffles given delay, and call size at delay are introduced as the three-point risk metrics of the model. Numerical simulations illustrate the functionality to develop terminal stacking strategies as well as emphasize the mega-ship phenomenon and its side effects on terminals (i.e. yard operation deadlock).