Data Source: EIA
"You don't need to be a rocket scientist. Investing is not a game where the guy with the 160 IQ beats the guy with 130 IQ." - Warrent Buffett
The crude oil price recorded its lowest ever value on
April 20 this year, when it went negative for the first time in its history.
What it meant in practice, despite being relatively
brief, the oil producers had to pay their buyers to store oil!
A few countries cashed in on the opportunity,
including China, the US and India; for some, the lack of storage facilities and
corresponding transport challenges got in the way, when the new-found fortune
amplified the enthusiasm for going on a buying spree.
On one hand, as far as buyers were concerned, it was
like discovering a fountain of fresh water in an arid land.
On the other hand, for investors and oil producers,
it was their worst nightmare.
The investors and producers have been relying on
model-based forecasts for decades, as that was the only way they can get an
insight into the future of the market, apart from the analyses of the experts.
Neither the mathematical models nor predictions of
the expert saw the oil crash coming. On the contrary, the predictions were a
steady growth, punctuated by random geopolitical events in the Middle East.
The above chart, based on data from the EIA, the US
Energy Information Administration, shows how crude oil price fluctuated over a
period of decades; in recent years, you can see the sharp dip on April 20th,
when the price crashed to an all-time-law.
No model predicted that dip; in future, modellers
may even refer to it – in retrospect, of course – as a one-off blip; these
models, however, cannot sweeps the flaws in modelling under the carpet, as the
latter cannot be ignored easily.
Based on what you see in the above chart, I may be
able to come up with mathematical formula to mimic its past behaviour of the
curve – except the price crash on April 20th.
If I say my formula is reliable enough to forecast
any future oil price on weekly basis, in the same breath, I claim to know every
single parameter that determines the oil price at any given time in future – an
impossible feat even if I know enough statistics and computer coding for the
process.
This is the danger of relying completely on
mathematical modelling; there are many latent factors at play: some modellers
simply do not know they exist; there are others who cannot quantify them even
if they vaguely identify them.
You ignore these undeniable facts and make
predictions from a mathematical model – only to find your investments go up in
smoke at some point, when you can least afford to happen on your watch.
It is not just models for oil price that received
the wrath of the investors recently. Those which predicted Coronavirus deaths
in the West are not very far behind them.
Of course, investors need a way to guess the oil
price in future for their businesses and which makes perfect in economic sense.
The results on many fronts, however, show that they need to take their instinct
on board too before making disastrous mistake.
Predictions from the models and individual instincts
are complementary, but neither is a substitute for the other.
