By Noam Fraenkel, VP Data and Market Intelligence
Peak season or not, September just kicked off without a China-US GRI. It also kicked off with speculation on the mid-month GRI.
Since early July, peak season pricing speculations have been rife. But with freight rates becoming more accessible, shouldn’t we be moving away from speculation and towards providing analytic responses?
That’s why I recently dove into millions of Freightos Index data points. Some intriguing insights came up, specifically that peak season kicked off in earnest early last month, and that there is a typical pattern to how GRI impacts rates through non-peak months.
Take a look at the data.
The Lifecycle Of A (Non-Peak) GRI
The chart above shows this year’s price increases on China-US lanes, broken down by month. Every single month is split into four sections, each that includes both China-East Coast (Blue) and China-West Coast (Orange) rates. The first two bar of every month plot the percentage by which rates increased in the first five days of the month compared to a baseline of how the previous month ended (baseline). The second and third set of bars also cover five day intervals, with the fourth set of bars covering the rest of the month.
A marked pattern emerges. After each spike on the graph, the percentage against baseline slides during the course of the month, invariably finishing lower than baseline. On average, 9.0 percentage points are shed (10.4 for China-West Coast, 7.6 for China-East Coast).
In other words, while prices may increase in the beginning of the month, they will have shed around 9 percentage points by month’s end.
Which is interesting … but not earth-shattering. However, it does provide some interesting context for establishing Groundhog Day for peak season.
Last month, this pattern was disrupted. After the first five days of price increases, instead of sliding back, the rates actually increased (see the circle on the above graph).
Prices increased between Day 5 and Day 10, and again between Day 11 and Day 15, before finally dipping in the second half of the month. Even then, China-East Coast ended up 10.2% above baseline (incidentally the same place they were when the new GRI rates were loaded). China-West Coast fared even better, finishing nearly 23 percentage points up on baseline.
Basically, the dynamic of a slight dip in the last half of the month continued. But, unlike in previous months, prices kept rising for the first fifteen days, and the month ended with a net gain.
How Transparency Can Improve the Signal/Noise Ratio
Beyond traditional indexes (some of which suffer from potential reporting biases), there isn’t much in the way of available empirical evidence. That’s why each summer rate spike rouses speculation. And rightfully so, there’s a lot hinging on when peak season starts. But the speculation has the potential to generate more uncertainty than transparency. In many cases, an early month spike is just a short-lived GRI.
These uncertainties are frustrating. Freight rate opacity undermines forwarders and shippers efforts to make informed business decisions. This is, of course, one of the primary reasons we launched our international freight indexes, providing data for free where others charge.
Freight Rate Analytics Has Its Challenges
Getting industry experience may impart some understanding of trends, but data-driven analysis can go much further. That said, I encountered two key challenges whilst getting to a freight “Groundhog Day”:
- Some indexes rely on self-reporting by carriers and forwarders. This is tantamount a seller dictating the actual purchase price. With such a vested interest, it’s difficult to know what’s an actual price or merely a sought-after price.
- Pricing dynamics don’t neatly fit into seven day periods (or five day periods for that matter). With pricing and sales still largely manual, getting higher resolution insight is challenging. As online pricing and booking becomes more common, the industry as a whole can expect real-time reporting, just like the stock market.
What It Means?
Understanding the pattern of GRI impacts on freight rates, and determining when peak season starts, both add transparency. Transparency leads to better decision making, and is where the industry needs to head. There’s a sore need for real-time and agnostic indexes that can help level out market information.
And that’s certainly something that Freightos will continue to focus on.