Navigating Shipping Cycles with Behavioural Economics: Eastern Group (Pakistan | UAE) proposes a solution

1. Why the Shipping Market Never Sits Still

From tramp steamers in the 19th century to today’s 24 k‑TEU leviathans, the industry repeats the same four‑stage loop: trough → recovery → peak → collapse.

The mechanism is simple:

1. Freight shortages lift rates.

2. High margins trigger record new‑building orders.

3. Vessels deliver with a 24‑36‑month lag, flooding supply.

4. Rates crash, older ships head for scrap, and the cycle resets.

Illustration of Year-on-Year Fleet and Demand Growth from 2019 to 2025, highlighting fluctuations in fleet supply versus demand trends.

2. Behavioural Biases that produce the Shipping Cycles

Bias	How it plays out	Real‑world outcome
Overconfidence	Owners “know” the up‑cycle will last and over‑estimate future demand.	Mega‑orders at the very top of the market.
Competition neglect	Each player assumes rivals will stay cautious.	Collective oversupply.
Herding / band‑wagon	Seeing competitors order, the rest follow.	Orders cluster within a few quarters, magnifying the bust.

3. 2025 Snapshot: Data That Screams “Bias”

• 6-8% fleet growth vs flat demand – Maersk warns that rates have sunk to “unsustainable levels” because of overcapacity.

• Orderbook hangover – Analysts expect an 8 % capacity jump against 3 % demand growth across 2025, pointing to another rate slump.

Illustrative graph depicting the container freight-rate cycle from 2018 to 2025, showing a peak in 2022 followed by a decline.

4. A Behavioural‑Economics Toolkit: What Eastern Group suggests managers and owners of shipping companies should do to navigate these shipping cycles.

Rationality suggests that shipping company should buy/build ships at rock bottom prices, larger and younger, and to sell then the older and smaller ships at the market peak; go anti-cyclical. This will secure benefits from the economies of scale and from the economies of age (Goulielmos, 2023). But we don’t know when the bottom and the peak is. Therefore, shipowners must correctly, without any bias, read the market signals, to anticipate the demand and supply conditions despite the time-to-build.

How can a shipping company achieve that – Proposed innovation

A comprehensive and systematic approach to mitigating overconfidence bias in ship investments:

Multi-criteria, Multi-stage Hierarchy Process

Flowchart depicting a structured approach to mitigating biases in shipping cycles, highlighting financial, technical, market, and behavioral analyses with various strategic tools and methods.

adapted from (Rousos & Lee, 2012)

The process will be structured, time-consuming and staged, to introduce deliberate reflection points and checks for bias mitigation.

Stage 1: Financial Analysis

Shipping projects generally employ the discounted cash flow (DCF) tools to deliver financial evidence to invest or not. But DCF tools like NPV can account for a ‘weighted mixture’ of financial evidence, limiting its usefulness, as the decision to invest is affected by considerations that are not purely, or are not financial, at all (Rousos & Lee, 2012). Moreover, DCF calculation requires assumptions like the discount rate and future cashflows, which would be steeped in overconfidence bias in boom period. Therefore, beyond DCF, the company will incorporate methods like Monte Carlo simulations to model risk and uncertainty more realistically. While Monte Carlo simulations are also not immune to overconfidence-laden inputs, they still allow to account for uncertainty in market conditions, by running numerous scenarios with a range of outcomes. This contrasts with single-point estimates that may be overly optimistic or pessimistic.

In addition, break-even analysis to be used to understand the minimum operating levels required for profitability. During the peaks, a fleet with a lower break-even point will accumulate higher profits, enabling to survive in the depressed market and allowing acquisition of new ships at low prices.

All the three evaluation methods will go through Scenario Analysis (different market conditions), and Sensitivity Analysis (investment's viability to changes in key assumptions) to remove some of the overconfidence bias in the decisions. Sensitivity Analysis and Monte Carlo analyses can help counteract the representativeness heuristic by illustrating a range of outcomes based on varying inputs, rather than relying on a single, "representative" scenario. In addition, the financial models will include base rate analysis to contextualize how often similar investments have succeeded or failed historically, rather than relying on the most memorable or recent successes as representative.

Stage 2: Technical analysis

Ship investment decision will next consider technical analysis. Like other assets and commodity pricing, freight rates also follow predictable fundamental and psychological trends (Trkman, et al., 2010). Use of such analysis does show how capacity “overshoots” the market freight rates creating overcapacity proving that investors overlook the psychology aspects of freight rate movements (Mileski, et al., 2020). The innovative ship-investment decision-making process will make this an important factor to make ensure human emotions and psychology is taken out of the equation and decision is based on the charts with historical trends of freight rates and where they might be heading to in the near to medium term.

Stage 3: Market Analysis

Conduct comprehensive market demand forecasts, including macroeconomic trends and sector-specific dynamics. Analyse the competitive landscape, assessing both current and potential future competitors, and the current and future orderbook of new builds, and their current entry into the market supply. Real-time data is now available of age of every ship, and orderbook for coming years, therefore, shipowners now easily know the future situation of the fleet and the process will take this into consideration in the investment decision. An analysis of the fleet profile will help in the investment decision and perhaps remove the competition neglect bias due to overconfidence.

Stage 4: Behavioural Analysis

This stage has deliberately been left for last as we would want all the above analyses to go through further de-biased analysis and outlook. This de-biasing approach is embedded into the Market and Financial analyses with the mandate of using sensitivity analysis and scenario planning, which would include worst-case scenarios and stress-testing investment decisions against various market dynamics. However, since those analyses could already be riddled with overconfidence, additional de-biasing methods are proposed.

We will simultaneously engage independent external consultants to provide an unbiased view and counterbalance internal overconfidence. While there is no guarantee that external consultants will not be overconfident, as they might not want to give advice/feedback contrary to market conditions, however, them being outsiders does mitigate some of the risk of bias.

At this stage, we will implement a devil’s advocate approach where a team member will be assigned to challenge assumptions and decision-making processes and walks through past investment decisions and discusses how they have worked out historically.

Additionally, we will go through a “premortem process” (Klein, 2007); gathering a group of individuals who are knowledgeable about the decision and imagine that they are in the future, 5 to 10 years down, and the outcome of the ship investment was a disaster as the market turned downwards, even before the ships came into supply. They write a brief history of that disaster. The idea here is for the decision makers to be forced to think pessimistically in a time of market exuberance and overoptimism. De-biasing overconfidence improvements “…occur, when subjects get frequent feedback…encouraging people to consider more information and/or ‘an alternative’” (Ferretti, et al., 2016); the aim of this stage.

Staged Process with Deliberate Time Gaps:

I must accept that debiasing techniques only work to a limited extent to overcome overconfidence bias (Larrick, et al., 2007). Therefore, a comprehensive, multi-factorial, staged approach is required to attempt to eliminate the bias, at every stage, with a systematic and disciplined investment process. The time gaps between the stages additionally prevent hasty decisions driven by overconfidence or market euphoria, and allows time for reflecting on each analysis, considering new information, and revisiting assumptions.

Strategic implications and limitations of proposed innovation

While this innovation is proposed for one firm to eliminate overconfidence bias, if all shipowners start to follow this procedure and therefore engage in counter-cyclical investments, it could mitigate the advantages of it. Especially when the TinbergenKoopmans model explains that shipping cycles can occur even if the demand is not cyclical, owing to fluctuations in the supply of vessels alone (Karakitsos & Varnavides, 2014). While this is a possibility (though historical evidence suggests that owners will continue to buy-high, sell-low), I contend the oversupply affect would still be subdued compared to the present scenario of low-demand and high-supply.

In addition, one practical hindrance, despite the innovation, would still be the difficulty in financing a vessel in the trough period. A market trough may last several years with ships operating at near cost or at losses during that time. “In this situation they cannot have the real possibility of investing in new ships, even if reason tells to do so” (Scarsi, 2007). Market Share: Companies that do not adopt the process might gain a short-term advantage during boom periods by expanding. For example, Maersk at one point the top shipping line in the world is going to drop down to 3 rd place in terms of volume once all ordered ships are delivered2 .

Strategic Positioning: Competitors might observe and learn from the outcomes of the companies implementing the process, selectively adopting some of its elements while maintaining some level of aggressive investment behaviour.

Investors and Shareholders: Investors may favour companies that adopt the multistage process due to the potential for more stable returns. However, some investors might still be attracted to the potentially higher short-term gains from companies not using the process.

Regulators: Regulators might take an interest in the process as it could contribute to a more stable industry. However, they may also need to monitor for any unintended consequences, such as reduced competition.

To Account for These Reactions:

Flexible Implementation: There could be flexibility in how the process is implemented, allowing companies to take advantage of unexpected opportunities. However, this may lead to the very overconfidence and irrational exuberance that is being tried to mitigate.

Communicating Value: Companies following the process should communicate the long-term value of their approach to their customers, emphasizing stability, reliability, and sustainable pricing.

Industry Engagement: Engage with industry bodies to share the benefits and lessons learned from the process, potentially encouraging wider adoption, which could still yield some industry-wide benefits.

Collaboration with Financiers: Work with financial institutions to develop financing products that support counter-cyclical investment strategies.

Long-Term Contracts with Shipbuilders: Secure long-term contracts with shipbuilders that reflect the new investment strategy to mitigate the risk of a capacity shortage during upturns.

Conclusion

The shipping industry's cyclical nature is deeply rooted in overconfidence bias that leads to demand extrapolation and competition neglect. By mandating a multi-stage, time-gapped analysis process, the company can significantly reduce the likelihood of overconfidence bias influencing ship investment decisions. This comprehensive approach not only ensures a thorough evaluation of potential investments but also embeds a culture of disciplined and rational decision-making within the organization.

Critical analysis of this viewpoint though is that overconfidence leads to ‘anticipatory utility’ (Reyes, et al., 2022). “This increases errors, but the increased hopefulness helps individuals to work harder” (Brunnermeier & Parker, 2005). Future research should focus on investigating the impact of heuristic representativeness using the suggested framework to mediate the overconfidence and exploit this bias instead of trying to eliminate it.

Looking for a ship agent, liner agent, tramp agent in Pakistan and/or UAE? Ready to future‑proof your liner and tramp operations? Logistics and supply chain? Contact Eastern Group agency offices in Pakistan or UAE today and take advantage of our expertise, insights, knowledge and experience.

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