Must-know: Unmasking the true drivers of shipping stocks
Adjusting for seasonality
Since seasonality is well known throughout the industry, dry bulk shippers don’t always move with the BDI (Baltic Dry Index). To account for seasonality, one easy trick is to use year-over-year changes in the BDI and dry bulk shippers.
Interested in DRYS? Don't miss the next report.
Receive e-mail alerts for new research on DRYS
Dry bulk shippers and the BDI
The chart above shows the average and median year-over-year changes in prices for key dry bulk shipping companies against the year-over-year changes in the Baltic Dry Index, using weekly data points. We see a similar divergence that begins from June 2010 to the end of 2012, driven by commodity supply shocks, a sell-off in global markets, and later surges in supply and shipments. Excluding those periods, the relationship between the BDI and dry bulk shippers is a lot clearer than the one we saw in the previous article of this series. When year-over-year changes in the BDI rose, dry bulk shippers followed—and vice versa.
A clearer relationship
Notice that when we plotted the year-over-year changes in the BDI and the benchmark for dry bulk shippers, we saw a clear linear relationship with R squared of 0.48. A figure around 0.50 suggests year-over-year changes in the BDI don’t fully explain year-over-year changes in dry bulk shipping companies, but this is a better approach than the last one we tried.
Investors should keep in mind that the data above is a bit skewed around the first few months of 2010 and the last few months of 2009 (circled above). The skewness is driven by divergences between the BDI and the overall market conditions.
- The first few months of 2010’s data is skewed because the BDI rose during the first few months of 2009, even as the U.S. market fell and dragged dry bulk shipping companies along with it. So the BDI rose less than usual and dry bulk shippers rose more year-over-year.
- The last few months of 2009 are skewed, as global markets somewhat stabilized in late 2008 due to stimulus programs. However, the BDI remained weak. So year-over-year changes in the BDI towards the end of the 2009 were inflated.
If we exclude these “market” forces, the linear relationship between year-over-year growth would rise to as high, as R squared of ~0.81—a strong number that shows one variable can be used to mostly explain the other.
Why is the year-over-year change collapsing? Find out in the next part of this series.