Waves in Ship Prices and Investment
Executive Summary — Dry bulk shipping is a highly volatile and cyclical industry in which earnings, investment, and returns on capital appear in waves. In this paper, the authors develop a model of industry capacity dynamics in which industry participants have trouble forecasting demand accurately and fail to fully anticipate the effect that endogenous supply responses will have on earnings. The authors estimate the model using data on earnings, secondhand prices, and investment in the dry bulk shipping industry between 1976 and 2011. Findings show that returns to owning and operating a ship are predictable and closely related to industry-wide investment in capacity. High current ship earnings are associated with higher ship prices and higher industry investment, but predict low future returns on capital. Conversely, high levels of ship demolitions-a measure of industry disinvestment-forecast high returns. Key concepts include:
- Real-world economic agents may repeatedly underestimate the power of long-run competitive forces, particularly in markets-such as industries with long time-to-build delays-where feedback is delayed and learning is slow.
- The annual realized returns to owning a ship vary enormously over time, from a low of -76% between December 2007 and December 2008 to a high of +86% between June 1978 and June 1979.
- Cycles in investment, lease rates, and secondhand prices are connected to predictable variation in the returns to ship owners.
- Heavy investment during booms predictably depresses future earnings and the price of capital, leading prices to overshoot their rational-expectations levels.
We study the returns to owning dry bulk cargo ships. Ship earnings exhibit a high degree of mean reversion, driven by industry participants' competitive investment responses to shifts in demand. Ship prices are far too volatile given the mean reversion in earnings. We show that high current ship earnings are associated with high secondhand ship prices and heightened industry investment in fleet capacity but forecast low future returns. We propose and estimate a behavioral model that can account for the evidence. In our model, firms over-extrapolate exogenous demand shocks and partially neglect the endogenous investment responses of their competitors. Formal estimation of the model confirms that both types of expectational errors are needed to account for our findings.