Asset Allocation via Artificaile Intelligence, a prospective view

1. Page 1
Credit Quantamental Insights
Artificial Intelligence at the Service of Asset Managers and Decision Makers
By EyeHigh
16th April 2020
A CREDIT VIEW THROUGH Ai LENSES
We have questions, but no expert
capable of answering them
systematically and consistently.
So, lets create an artificial one.
We want this expert to be able to
answer even within periods of
huge uncertainty and turmoil. The
current situation goes beyond
that, as no comparable
precedents in history are
available.
Our expert revolves around the
Output Gap concept coupled with
the Federal Reserve rate
decisions. There is ample
documentation available of why
this is an elegant simplification for
asset allocation.
This would be what we want to
ask: What would happen to credit
spreads and treasuries if 1) OG
remains below zero, 2) there is a
strong activity reaction to
monetary and fiscal stimulus and
3) the Fed reverts the emergency
rate cuts after 5-7 months?
Chart 1 provides historic context
to our set of conditions. Chart 2 &
3 are just the latest segment
zoomed out, with the red area
highlighting our hypothetical
future (12 periods as of mar20).
These 3 conditions interrelate
non-linearly through time and
pose a non-trivial problem for a
human mind. This is where Ai
non-linear capabilities are
required.
AI COMES TO THE
RESCUE…
But only if you ask the
right question.
When we think about
markets, we usually stack up
condition upon condition…
but we are seldom able to
correctly quantify.
This is not a problem suited
for traditional statistics or
selective/biased chart
inspection. Instead for AI it is
just business as usual. A Self
Organizing Map (SOM) will
do fine.
So, let us try with credit, a
complex asset, in which
liquidity is an issue in
stressed times, and where
we have less margin for
error.
Credit is not only subject to
strong non linearities but
also requires to be broken
down into two problems
Credit yield = Treasury yield
+ Credit Spread.
Source: Moody’s BAA rate
and 10y US govt rates. BAA
rates represent the credit
asset class in general.
Conclusions must be
adapted to the credit quality
and duration of interest of
the reader.

2. Page 2
Credit Quantamental Insights
Artificial Intelligence at the Service of Asset Managers and Decision Makers
By EyeHigh
A CREDIT VIEW THROUGH Ai LENSES
Chart 4 shows our takeaways after
asking the algorithm for that
specific sequence of events:
1) The initial spread widening and
Treasury yield compression tends
to revert after the initial shock.
Still there is a diminishing but
positive upside risk to spreads
while the economy is still
deteriorating (period 3
corresponds to jun20 in Chart 3).
2) Through periods 4 to 6 growth is
above potential (OG less negative)
and spreads improve, a typical
recovery pattern.
3) The Fed starts raising rates in
period 7, acknowledging the
recovery is in place. At that point
the long end of the Treasury curve
is subject to an additional source
of upward risk. Here, spreads and
yields move in opposite directions
for a while.
4) Since governments are in
desperate need to issue debt to
pay for the fiscal packages, yields
upside risk ought to be in fact
much higher. The algorithm has no
knowledge whatsoever of this, and
therefore it is important to
mention it.
Chart 5 answers our final question.
Taking as much macro information
as possible, do underlying macro
forces pressure credit spreads up
or down for the next 3-4 months?
For the time being the algorithm,
in a totally aseptic fashion, tells us
that credit spreads are subject to
an upwards though moderating
risk.
MACRO SCENARIO
Our macro scenario
purports an economic
impact like the one that
took place during the 2008
crisis.
This implies that the OG
contraction reaches almost
the same depth (negative
OG). In turn, it recovers a
lot faster provided the
unprecedented size of the
monetary and fiscal
packages.
Both assumptions might be
wrong, but what we want is
to set the playfield for our
thinking.
Fed rate increases follow
the usual pattern when a
recovery takes place. At
some point “emergency
cuts” must be reversed.
A FINAL QUESTION
In a last-ditch effort, we
asked the algorithm a final
question: Are macro factors
pressuring up or down
credit spreads for the next
few months? Notice that
answering this requires tens
of indicators in a time series
fashion. The question when
correctly posed can be
interpreted as an
underlying force or risk.
Chart 5: BAA Spread

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