Shipping Supply and Demand Data, Boffins, and Walking on The Water

Long-time readers, and followers on Twitter (where I am “freightboy1”) know that I live at the beach. We have a sandbar, where- at low tide, I can walk around and enjoy the cooling breezes. Part of the fun is “navigating” towards certain rocks, hidden even at low tide- mini ledges for me to stand on and survey the shipping activity on Long Island Sound, mostly tugs and barges delivering refined petroleum products. This type of “navigation”, me- not the tugboats, is easy- I can line up markers, like certain points on the beach, in two directions; magically, I can “steer” to the hidden rocks where the markers intersect. Fellow beach goers are very impressed as I walk on the water- standing on the rocks.

Contrast my brand of wayfinding with the dilemmas facing experts, boffins and others trying to predict the course for the shipping markets. Readers are also aware of the tag-line I repeat when asked about prospects for a particular shipping sector, which is: “My crystal ball broke many years ago.” With a few exceptions, maritime markets skew towards oversupply in a macro sense. In the short term- going out a few months, maybe there is something to be done now. Of course, the devil is in the details.

Supply should be easier to measure- but actually it’s not. Analysts could deal with slow steaming, adjusting average speeds that would reduce “deliverability”, a measure of effective supply. But then, you’d have second derivatives- like owners’ reactions to fuel prices or responses to inflections in hires.   Ship tracking technology such as AIS (Automatic Identification System), feeding data into databases that can then be crunched with powerful analytics with a splash of artificial intelligence (AI), enables analysts to bypass all these derivatives and draw inferences from the actual data.

Demand should be easy to measure also, certainly after the fact. More economic activity at the receiving end creates increased demand for raw materials, and freighting. Seems easy enough. But, there to, there are wrinkles. In stronger markets, ships are taken on period charters, amplifying demand (think of it as a different type of “substitution” effect. Again, infusing AI, a feedback loop could be infused into the demand side of models to factor this in. AIS has a role to play here, with the new age boffins using satellites to detect levels in oil storage tanks (part of the demand equation). Since “draft” readings on AIS feeds are a manual input (though the technology exists to easily automate such data), the satellites also have a role in gauging whether vessels are fully loaded, somewhat loaded, or in ballast. Gone are the days of old geezers (retired sea-captains, perhaps) prowling the docks in Australia and China, and reporting back on the heights of iron ore piles.

So, supply and demand- far more complicated than navigation along my sandbar, have the potential for a much more rigorous analysis probing with new data tools. For charterers, time charter operators, and owners, the data explosion could yield some improvements in their implementations of shorter term strategies- which I define as going out a few months. Indeed, a number of efforts are underway to gather data, crunch it, and then offer an information product for the vessel chartering communities. But, that’s as far as it goes; to consistently attract investors to the business, much more stability will be required (and valuations skewed towards cash flows). The data boffins need to make their money, and investors with fat pockets (and perhaps a motive of diversifying their sectoral holdings)  are fair game. But shipping investors should not confuse this profusion of data with improved ability to predict the ups and downs of shipping share holdings, which, as is well known (nothing original here, sorry), respond to much broader forces tied to macro-economics and market sentiments, over timeframes of longer than a few months.