HomeSolutionsInsightsNewsroomPeopleContactLogin

Acclaimed macro analytics
Portfolio Construction & Risk Management
Trading Solutions
Macro Models
Thematic equity baskets

Announcements
Press
Blog

None have yet to cross the 65% threshold denoting a new macro regime &, as such, their valuation signals come with a health warning. However, the valuation picture is consistent – the BRL looks rich to model.

Looking at the crosses with the biggest jumps in model confidence a clear pattern emerges. AUDBRL, CHFBRL, BRLJPY, BRLCLP have some differences but share the fact that risk aversion is the single biggest driver. Even versus the Chilean Peso, the current pattern shows the Real as the high beta asset that needs VIX & other measures of risk aversion to stay low.

Combining valuations with the key driver of this new regime, suggest the Real could be an efficient vehicle for any risk off scare.

Indeed, RETINA™ has triggered a bearish momentum signal on BRLCLP. The recent uptrend looks overbought & is showing signs of rolling over.

The chart above shows XLE spot price (white) versus macro fair value (red). The recent divergence has opened up a +0.8 sigma (+12.4%) Fair Value Gap. But while macro valuation is rich to model, RETINA™ continues to show the uptrend is in good health. Equally, crude oil - unsurprisingly the biggest single macro driver - retains good momentum.

XLF is just 0.3 sigma from model, i.e. in line with macro fair value. The momentum picture, however, is more mixed. Relative to XLE, the uptrend looks less secure.

Both XLE & XLF model value have started to roll over. It is modest thus far, but worth watching. Today’s event risk could be the catalyst for the next big move. Qi users can align macro fundamentals & trend/momentum dynamics in real time. Signal from noise™

Qi's credit models are in strong macro regimes & valued in line with macro fundamentals. Our Credit Watchlist shows all bar two (EM High Yield & US Municipals) have high model confidence; & all are essentially within 0.5 sigma of macro-warranted model value.

High Yield spreads in both US & Europe are especially notable – macro explanatory power of 90% plus & almost exactly in line with fair value.

No valuation edge in credit itself then, but what about other asset classes?

The Fed’s aggressive policy response in March 2020 resulted in a significant rise in credit’s importance as a factor for risky assets. Given its role as a primary driver, a core Qi theme throughout last year was that, as long as the Fed back-stopped credit markets, any pullback in US equities was a dip buying opportunity. Is the opposite true now?

The chart above shows the S&P500’s independent sensitivity to US High Yield spreads. In Q4’20, a one standard deviation tightening in US HY spreads (in isolation, every other factor held constant), was consistent with a 1% rally in SPX.

The equivalent move today is just a 0.2% gain. On this metric, equities still want tight credit spreads but the sensitivity is a fifth of what it was. Last week’s announcement isn’t the huge game changer it would have been in 2020.

AUDZAR is especially notable. Prior to Friday’s US Payrolls, the FVG was at a 1y low. Even with the recent bounce in spot, the valuation gap stands at -1.1 sigma (-4.2%). Back-testing a buy the dip strategy at a FVG of -1.1. sigma since 2009 produces a 79% hit rate & +1.8% average return.

While the valuation gaps are less extreme, the Rand is expensive to model on USDZAR, CADZAR & EURZAR as well. Of those, CADZAR’s current -0.7 sigma FVG back tests the best with a 63% hit rate & +0.4% average return.

The macro profile is similar across all 4 crosses. Risk off (wider credit spreads, higher VIX) plus weaker domestic growth (Now-Casting tracking GDP growth for SA) dominate the regime & are Rand negative. Put another way, on current FVGs, the Rand has priced a fair amount of good news on risk appetite & growth.

It is also noticeable how important the Rand is for South African equities currently. The sensitivity of the iShares ETF tracking MSCI South Africa to Trade Weighted ZAR has been rising strongly since mid March, & the currency is now the biggest positive driver.EZA is very close to macro fair value so equity investors need to monitor fx shifts.

By George Hatjoullis:

One of the first things that I confronted as a young finance academic (this is pre-history) was the impossibility of running a portfolio of stocks selected for their individual merits. The reason was, once the portfolio had more than a certain number of stocks, the specific risks of individual stocks become largely irrelevant. This is the whole point of diversification. Unless the portfolio holdings are skewed towards one or two stocks the portfolio would be defined by its systemic risk; the risk that cannot be diversified away. If the portfolio consists, say, of bank stocks, then its risk characteristics would accord with those factors that impact all banks. The portfolio risk would be dominated by macro factors such as GDP growth, Inflation, interest rates, credit spreads, and so on. If you check the top ten holdings of any of your funds you will notice it is rare for any individual stock to constitute more than 2-3% and the holdings usually exceed 50 stocks. Every portfolio is effectively a macro portfolio even when it purports to be bottom up. However you pick the stocks, you usually end up with something that is largely driven by macro factors. For those that doubt the veracity of the paragraph take a look at Chapter IX of Portfolio Analysis by J.C. Francis and S. H. Archer, Second Edition, Prentice-Hall, 1979. I am sure there are more up to date discussions of this point but this was the Chapter that made me think back in 1979.

If we accept the premise of the above paragraph then this has profound implications for portfolio management and the wider fund management industry. Portfolio outcomes are determined by a well defined and finite set of macro factors. Macro has always been the only game in town. Portfolio outcome variation has been driven by selecting stock portfolios which were unwittingly weighted towards specific macro factors but attributed to marketing objective of the fund. The understanding of this issue has slowly emerged and we have seen much written about factors in terms of value, momentum etc. However useful these broad categories might have proven they are not clean macro factors. Some banks are clearly value stocks but others momentum or growth stocks. The need for a well defined set of macro factors that could capture portfolio differences remained. One attempt to fill this need is a company called Quant-Insight (https://quant-insight.com), and in which I own a small shareholding.

QI offers a set of independent and finite macro factors against which most diversified portfolios can be characterised. QI tells you what you have in terms of well defined factors such as growth, inflation, credit spreads, interest rates etc. QI tells you if the value of your portfolio (or individual stocks or other portfolios) is out of line with the historical relationship with the set of macro factors. QI tells you how strong is this relationship and when the portfolio relationship to macro factors may be changing. QI cannot tell you how these macro factors will evolve (nothing can) but it can show you to which factors your portfolio is presently exposed. QI can also remind you that whatever you think you have what you actually is some weighting of macro factor exposure. It may be that you can achieve a more efficient portfolio with exactly the same factor weighting. A more efficient portfolio would offer a greater return for the same level of factor risk.

Read the George Hatjoullis blog

Alongside such political factors, bottom up analysis remains critical. Some carriers are promoting free flights for the vaccinated; others are successfully adapting from passengers to cargo in order to boost revenues. But it is notable in the curated Airlines Watchlist below that every single carrier Qi models - in US, Europe & Asia - is in a strong macro regime.

The other standout is how close to model fair value every single carrier is. They are all essentially within 0.5 sigma of model. Macro matters for airlines & currently they are behaving in line with macro fundamentals. Watch the relevant macro factors for the next big move.

While model confidence is uniformly high, there is a wide dispersion of macro regimes. Lufthansa is a high beta inflation play with crude oil & inflation expectations accounting for around a third of model explanatory power.

A desire for credit spreads to remain tight dominates all four US airlines – SouthWest, Delta, United & American.

Asian carriers Quantas & Singapore have the most diverse range of drivers but display a notable bias to domestic economic & financial conditions.