Shipping is a derived demand and strong fundamental analysis is crucial to providing maritime research that will add value. Just high-level summaries of the key statistics and generic, top-down regional models will not suffice.
Since exiting shipbroking five years ago, our oil and tanker modelling frameworks have become more complex and granular. Our global model maintains monthly mass balances for crude, NGLs and eight product grades in 185 countries individually, and is now evolving into refinery level analysis, as well as to specific oil fields. The framework evaluates different GDP growth scenarios and their impact on demand for the eight product grades in each country, by examining income & price elasticities and other structural factors. The resulting mass balances drive oil price forecasts that influence demand outlooks and refining operations.
We believe that this level of granularity is the minimum required for proper tanker analysis, and that it has to balance, while addressing the numerous inconsistencies in the oil data.
As the models became more complex and the data sets grew massive, Excel and its VBA macros failed to keep pace. Consequently, we migrated to the programming language Python and to database software SQL, which are significantly faster and can handle the larger data sets required to balance global oil flows with this level of granularity.
Methodology — A few key differentiating elements of our methodology:
– Testing across a variety of macroeconomic scenarios in 185 countries
– Country demand forecasting for eight grades, driven by macro scenarios and price elasticities
– Country (and refinery) crude runs and yields by macro scenario, applying comprehensive libraries on individual refinery configurations and crude assays
– Examining impact of non-crude liquids bypassing the refining system
– Scenario analysis of rig counts, rig productivity and field decline rates on US tight oil production
– Balancing global liquids supply, crude runs and inventories across 185 counties and five PADDs
– Fleet productivity forecasts based upon bunker prices, fleet speeds, port delays and trade patterns
– Tanker supply forecasts based upon ordering, slippage and demolition scenarios
– Supply scenarios based upon vessel earnings/prices trends
– Analysis of clean tankers in dirty trade
– Evaluation of chemical tanker supply/demand
– Combined analysis of clean and chemical tanker supply/demand
Output – In addition to providing all of the underlying oil fundamental and tanker demand metrics, the modelling methodology provides forecasts for:
– Sector fleet utilisations, for each macro scenario
– Sector spot and period earnings
– Newbuilding and secondhand vessels prices, by sector