Please see the below article from Legal & General received yesterday afternoon:
Froth. Nice on coffee, less nice on financial markets. While one variety can leave an embarrassing yet somewhat endearing moustache if not tackled properly, the other is a sign of late-cycle dynamics that can leave investors looking far more foolish if ignored. With stories of a New Jersey deli with $35,000 in sales over the past two years combined being valued at $100 million on the stock market and a cryptocurrency started as a joke being valued at $50 billion – Dogecoin is now the fifth-largest cryptocurrency by value – one might be forgiven for thinking that things have gone a bit too far.
One area of the market on our ‘froth watch’ has been special purpose acquisition companies, or SPACs for short. SPACs are listed companies with no commercial operations that exist to raise funds to acquire private companies, thereby making those companies public without the traditional IPO route. While by no means a new vehicle, a record 277 SPAC new issues were completed in the first quarter of 2021, with the SPAC index outperforming the S&P 500 by over 50% between July 2020 and February 2021.
Since then, SPACs’ fortunes have reversed, giving back much of that outperformance and there being only six new issues in the second quarter so far. Initially, a slowdown in retail flows was blamed. But the main factor has been a number of statements by the SEC that have created uncertainty around SPACs’ accounting treatment.
SPAC activity may well return, but SEC scrutiny and regulatory risk will remain and should serve to dampen future activity. Either way, the money raised by SPACs over the past few years is still out there looking for acquisition targets. So while one patch of froth has been blown away, temporarily at least, no doubt others are brewing, waiting to leave a stain on investors’ lips.
Take a breakeven
The gradual rise in inflation expectations (as measured by US 5y5y inflation swaps) over the past year has been a tide that has lifted most boats, not least nominal interest rates.
For inflation expectations to continue to rise, at some point there needs to be a sustained rise in realised core inflation. A near-term jump in core inflation is almost certain due to base effects from last year’s lockdowns and some additional normalisation as economies reopen. There are also other knock-on effects – such as supply-chain disruption and lean inventories – that could lead to an overshoot.
From the summer onwards, the picture is less clear. The risk for growth assets is that expectations become detached from reality, and the Federal Reserve (Fed) at some point has to step in to maintain its own credibility.
We believe the time has come for us to start leaning against the momentum in inflation expectations, and as such we have entered a short US inflation position. This is not to say that we think we are timing the peak; as discussed above, we expect a pick-up in US inflation and labour-market data from here. But we also expect the Fed to cap inflation expectations in the not-too-distant future, and are waiting patiently for the central bank to reveal its hand. We’d rather be too early than too late given that the recovery in inflation expectations has gone hand in hand with rallying credit and equity markets.
A new joiner in our team, who has a background in data science, recently voiced some confusion having heard from several team members about the need to ‘avoid data mining’. From a data scientist’s perspective, the confusion is warranted: data mining is a key component of machine learning, and refers to extracting information from patterns in large datasets. Why, then, does the term have a negative connotation when used in our team?
In an investment context, data mining means ‘finding relationships in historical data that appear causal, and building a model that assumes a continuation of that causation, where in reality that relationship was coincidental and unlikely to persist’. It is relatively easy to over-parameterise a model and to endlessly tweak it to get the best possible backtest; it is almost certain that such a model will underperform in the future when those specific circumstances do not repeat themselves.
The distinction we make between Alternative Risk Premia (ARP) strategies and the much broader universe of quantitative strategies is that for ARPs there must be either a behavioural or structural rationale as to why the strategy should work in the future, rather than being just a combination of signals that happen to have worked well together in the past and might work in the future. There are all sorts of reasons why that might not play out (e.g. crowding of the strategy, or a regime change that causes a structural shift and a breakdown of some prior imbalance), but where possible we try to rule out data mining as the cause of future failure by intentionally keeping the testing/design process as simple as possible.
For many applications of machine learning this is not an issue; data mining is appropriate where the output, rather than the model process, is the only thing that matters (e.g. improved cancer diagnosis) and the inputs are relatively stationary and unlikely to see a structural break (e.g. a large sample of humans). Financial time-series data are rarely as well behaved, and so we have to be extremely careful about how we make inferences.
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