FCEM - Shipping Finance

This study examines the impact of carbon risk on the cross-section of shipping stock returns. Using 355 firm-year observations for internationally listed ship-owning companies over the period 2011–2024, we document a strong positive relationship between carbon risk and subsequent twelve-month cumulative returns. Portfolio analysis reveals that firms in the highest carbon risk quintile earn up to 32% higher returns than those in the lowest quintile. The positive carbon risk–return relationship remains significant after controlling for a wide range of firm-level characteristics, alternative proxies for carbon risk –namely direct (Scope 1) and indirect (Scope 2) emissions–, and alternative return windows. Overall, the results suggest that investors may require higher expected returns to hold stocks exposed to elevated carbon risk. Our findings contribute to the growing literature on climate risk and asset pricing by providing evidence from a capital-intensive and globally regulated sector that is particularly vulnerable to environmental transition risk.

This paper investigates how maritime shipping companies adjust their capital structures – debt-to- equity (D/E) ratios – in response to market volatility and financial risks for the time period 2019-2024. The research focuses on the tanker and specialized gas carrier (LPG and LNG) segments due to their high exposure and sensitivity to regulatory shifts , the Covid-19 pandemic, geopolitical issues that have a direct effect on freight rates and global trade patterns. The maritime shipping industry is characterized by its capital intensive and cyclical nature therefore maintaining financial stability is a unique challenge for companies to navigate. Initially analyzing six publicly traded companies-two diversified, two tanker-focused, and two gas carrier-focused-the study employs both quantitative data from SEC filings and qualitative insights from industry reports. For the final publication, the scope will expand to 130 companies, incorporating an econometric time series analysis to examine the relationship between leverage and freight market fluctuations. Our preliminary findings reveal that companies in the tanker segment tend to maintain a more flexible leverage strategy, while companies in the specialized cargo and gas carrier segments maintain a higher leverage which can be attributed to long-term time charter agreements and less reliance on the spot market. Companies with a diversified fleet exhibit a mixed pattern regarding their capital structure depending on their fleet composition. The results indicate that capital structure theories such as the Trade-Off Theory, Pecking Order Theory and Market Timing Theory, only partially explain the financial strategies employed by shipping companies, further highlighting the need for tailored financial risk management strategies. 

This study examines whether and to what extent shipping companies were affected by the impacts of COVID-19 and the Russia-Ukraine war crises. We hypothesize that shipping companies are more likely to adopt more complex, sophisticated, detailed, and extensive management accounting systems due to the crises during the period under review (2019-2024). The study is informed by the answers of the executives of 713 shipping companies to an online structured questionnaire. Empirical evidence indicates that both crises impact the shipping industry, with COVID-19 being more influential. The results also verify that, under the pressure and consequences of the crises, entities developed, improved, and incorporated more complex and sophisticated methods and tools into their systems to overcome the challenges. At a second level of analysis, the results record the level of assimilation of each pillar of management accounting systems to shipping companies. The results of our study are essential for both shipping executives and academics. They shed light on how shipping markets react to periods of crisis. In parallel, contribute to the management accounting systems and international shipping literature by providing information on the level of their integration in the highly complex and volatile shipping industry. The analysis of shipping executives' perceptions is significant because it allows for a deeper understanding of the crisis's impact. Management accounting systems studies have focused on several industries, but empirical evidence in the shipping sector is lacking.

The paper discusses proactively managed real options under geometric mean reversion stochastics. The paper develops further the proactively managed real options theory that suggests that under dynamics with high uncertainty it may be optimal to act earlier rather than later, i.e., it contrasts the prediction of the traditional real options literature. The paper adds to the theory by studying the effect of mean reversion for geometric stochastics. The paper also adds to the literature by introducing the strategy of proactivity by "warm stacking" during inactivity. The theory is illustrated by cases from ocean industries. The maritime green transition often involves entry and exit decisions with substantial switching costs under high uncertainty. To proactively manage these switching costs by proactive investments in flexibility may facilitate a more cost-efficient green transition of the maritime industry. The paper presents an illustrative case of proactive investments to facilitate switching of between fossil and zero-emission fuels.

In this paper, we investigate how to successfully forecast the direction of an economic time series. Our study is performed by means of Monte Carlo simulations where the simulated data generating processes (DGP) are chosen based on estimates from freight rate agreement (FFA) data. The situation we have in mind is when a forecast should be used for a particular decision, e.g. should we go long or short in a financial asset, enter an interest rate swap or whether we should commence with a costly project or not. We show that there are situations where the benefit of correctly specifying a model has the cost of producing inferior forecasts for the future direction of the time series compared to reducing the data to an indicator variable before predicting it. In any of the cases we study, including the one with parameter values closest to a real world example from shipping, a very simple model where data is reduced from a price change to an indicator before the model is fitted, turns out to outperform a correctly specified model.