Over the past year, five inter-related ASGM developments have been: broadening the investment universe; adding and deleting models; accelerating trading speeds; proving the value of factor timing in risk-off regimes; and increasing use of alternative data. The latter is emphasised not only because it is fashionable, but also as Aspect’s approach is rather distinctive.

Investment universe and models

ASGM still has 13 macro themes and has introduced a 20% risk weighting in equity indices (in US, Canada, UK, Europe, Hong Kong, Japan, Korea and Australia) as an asset class. “Equities provided diversification, as they have near zero correlation to the existing asset classes of fixed income, currencies and emerging currencies,” explains co-PM, Anoosh Lachin.

The composition and expansion of ASGM’s investment universe is partly constrained by market liquidity and transaction cost considerations. Sovereign bond futures (with the largest risk weighting of 42%) are traded, on a mainly relative value basis, in eight countries: US, Canada, Japan, Australia, UK, Germany, France and Italy. But the two-year versus 10-year yield curve slope is only pursued in three of these countries, Australia, Germany and the US, where Aspect finds enough liquidity at both maturities. Similarly, only the US VIX future, which has an average risk weighting of 5%, is liquid enough. “It is ten times more liquid than the European VSTOXX, which is itself more liquid than the VDAX,” says co-PM, Asif Noor.

ASGM is by no means restricted to futures however. Currency forwards, which have a risk weighting of 33%, are traded in G10 countries, and seven emerging markets: Brazil, Mexico, Hungary, Poland, Turkey, Korea, and South Africa.

The addition of other over the counter (OTC) markets – interest rate swaps – to ASGM remains a long-term goal, mainly to access emerging markets interest rates that lack liquid (or any) futures markets. This has been delayed because fixed transaction costs result in a minimum trade ticket size that is too high for ASGM’s current level of assets. Some of Aspect’s larger programmes, such as the $4 billion Aspect Diversified, are trading rate swaps, and Aspect Alternative Markets trades many more OTC markets.

Adding and deleting models

Three new models are now trading equity index futures, at higher speeds than ASGM’s fixed income models. Currencies have seen some switches of models. In G10 currencies, a traditional macro indicator has been replaced by one more based on alternative datasets. In EM FX, a slower-moving, price-based model has been dropped, and a faster-moving, option-based model, has been added.

Accelerating trading speeds

The faster equity and currency models are one reason for shorter holding periods. Another is that new and alternative datasets, such as survey data and macro data, are working more swiftly. Trades are now placed twice a day, up from once a day. For instance, equity trades are initiated before Asian markets open, and the second batch of trades are placed during the European trading day. Aspect has a trading desk available, 24 hours a day.

The general trend of increasing the tempo of trading pre-dates these new models and data inputs however. Trading speeds have been steadily rising since the programme started in 2004; the average holding period has dropped by just over a day per year, from 45 to approximately 25 days.

Timing factors and dodging risk-off phases

ASGM normally has exposure to all asset classes and markets (bar the implied volatility calendar spreads, where exposure is more opportunistic) but is timing factor exposures. “Factor timing is mainly done for risk management reasons, and applies more to the slower models. The longer holding period models, which have lower information coefficients and worse hit rates, may not spot a world economic meltdown,” explains Noor.

ASGM have been factor timing for about ten years at Auriel, including in 2008. It takes three forms. “Regime conditioning, intra-model, is present in most models, to determine if the market regime is conducive to a particular asset class or country, based on external factors such as interest rates, inflation, sovereign risk or volatility,” says Noor. For instance, in 2018, a sovereign risk indicator helped to remove exposure to the Turkish Lira ahead of a sharp drop.

“Two other types of risk filters switch off individual models, or groups of models, depending on risk appetite. This is more about chopping off the left tail of the distribution,” he adds. They aim to avoid currency carry trade corrections. The risk filters throw up false alarms about 70-80% of the time, but transaction costs in currencies are so small that the models are able to rapidly re-enter markets, usually within a few days.

“When the risk filters get it right, such as in February 2018, then they really pay off by avoiding losses. Their alpha-adding capability is around 25%,” says Noor.

Alternative data

ASGM’s multi-factor approach, including valuation, sentiment and technical models – which vary by asset class – can use traditional or alternative data, or both.

Currently, approximately 80% of Aspect’s data is traditional and 20% alternative, but going forward, the share of alternative data is likely to grow as it makes up over half of future research projects. Less than half of the items on ASGM’s mid-2018 research agenda involve traditional data which include equity valuation models, more work on timing factors and expanding the fixed income investment universe to include swaps, as aforementioned.

The rest of the research projects involve what Aspect terms alternative data: macro-survey based indicators for EM FX; bank liquidity indicators; utilisation of NLP algorithms across various spectrums to build sentiment indicators across the asset classes.

Aspect began investigating alternative data 18 months ago. Aspect’s 80 strong research and development team includes a data team of data scouts, including some PhD students, who look for interesting datasets to discuss within alternative data groups. This is a centrally managed cross-departmental effort to triage alternative data for multiple teams and avoid duplication of effort. The group includes Aspect’s short-term trading strategy, the Aspect Tactical Opportunities Programme (ATOP), run by Dr Constantin Filitti. But Aspect’s medium term, price-based trend following strategies, are not expected to participate and nor is Aspect’s alternative markets strategy, which also uses traditional trend following models to trade alternative markets.

Hypothesis-driven research

Alternative data is new but does not change the philosophical basis of Aspect’s research process. “We remain very much hypothesis-driven, rather than machine-driven, which rules out unsupervised learning. Transparency is key, but hypotheses can be very eclectic and are not restricted to academic journals, newspapers and so on,” says Noor.

Discretionary judgement – rather than algorithms – also helps to determine which models are used. “Adding or deleting models is the most qualitative decision that Aspect is likely to make, based on their properties, and correlations to other models. We may reject models with a good back-test if they do not have broad based application,” he adds.

Alternative data is proving a fruitful research avenue. “The team are excited about the potential to use alternative data in macro strategies, including in slower-moving strategies such as carry, trend and value. Alternative macro data indicators, including data releases and revisions, are having a quite significant impact on performance, for both alpha and correlation,” says Lachin. Aspect expects alternative data models to generate several percentage points of alpha per year, and more as time goes by.

But alternative data is not a silver bullet solution by any means. Just as our interview in late 2017 pointed out how Aspect does not expect its alternative markets to generate stronger trends than traditional markets over time, Aspect does not envisage alternative data being a more productive or predictive raw material for research than traditional data. However, there is a wider breath of orthogonal datasets to investigate which may lead to better quality uncorrelated alpha models.  “The hit rate for both is that about one in twelve research prototype models turn out to be successful. Alternative data is not more informative, and hypotheses do not always work. There may even be more risk of over-fitting and noise clouding out signals,” observes Noor.

Alternative data also exhibits a similar “alpha decay” trajectory to traditional data. Noor draws an analogy from equity futures models. “20 years ago, one of the first models we used was earnings yield in equity futures or PE ratios. This was very easy and simple, and it worked well at the start. We then moved to weekly data, then sector-adjusted data, then forward-looking earnings yields. To stay ahead of the curve, we have to keep evolving and researching models. The same applies to alternative data and this is consistent with everything else we do”.

Data vendors

Buzzwords and techniques such as artificial intelligence, machine learning, deep learning/neural networks, cognitive computing, natural language processing (NLP), Big Data, and unstructured data, are all potentially used by Aspect’s data vendors. Aspect is increasingly tending to rent space in the cloud for its research data needs, but not yet for live trading.

Nonetheless, most data vendors provide long histories of trial data for testing, and Aspect spends much energy testing datasets, before deciding whether to buy them. ASGM also needs long enough data histories to test the data. Costs of data are rising – for both traditional and alternative types, as exchanges, index providers, and licenses cash in on the growing appetite for data.

As a relative value macro strategy, ASGM has special data needs that differ from those of strategies trading single corporate securities. “ASGM needs data to be cross-region, which can rule out US-centric data. Any data such as on credit card or bank account transactions would need to be aggregated up to the macro level to be useful,” points out Lachin.

Aspect expect their competitive edge to come through how they interpret and combine the data, rather than from sourcing unique data. It is very unlikely that Aspect would bind a data supplier to exclusivity. In fact, being offered data on an exclusive basis might even be a red flag. “We would be cautious and sceptical of data that is so good, its value is intrinsic rather than being based on any skill applied to it,” says Noor. Additionally, Aspect would fear that extraordinarily predictive data is more susceptible to decay. They would as well be suspicious that secret data might be of potentially dubious legality.

Dynamic currency hedging

Currency is a risk factor that global investors cannot avoid and can contribute a significant proportion of volatility risk for global equity portfolios – yet it is often neglected. Aspect are of the opinion that the extremes of ignoring currency risk, or hedging 100% of currency risk, are sub-optimal, as are fixed hedge ratios such as hedging 50% of currency risk, which can also consume substantial cashflows. Aspect has confidence in the predictive power of their models and find that their dynamic currency hedging strategies may not add any incremental volatility risk to a portfolio. Noor and Lachin have been running currency overlays for about 15 years since their Auriel days.

Aspect’s currency overlay strategy – managing around $1.8 billion in dynamic currency hedging – is a customised carve out of ASGM. It uses the same alpha models as the G10 and EM currency part of ASGM but is constrained by the client’s currency exposures and hedge ratio tolerances in addition to the objective of outperforming clients’ customised benchmarks, risk tolerances and so on. The objective is to both hedge and mitigate some currency exposures and add alpha through the currency markets. Aspect and clients work out what the FX exposures are, and how to tailor a bespoke solution that can include a mix of passive hedging and active currency management that is intended to be negatively correlated to the client’s benchmark, thus reducing overall portfolio risk.

Investor attitudes to it vary by region based partly on perceptions of how local currencies will perform in a risk off climate. “For instance, US investors pay more attention to currency risk partly because the US dollar has historically appreciated during risk aversion phases. In contrast, Australian investors are more likely to leave foreign currency risk unhedged, as the Australian dollar has historically depreciated during risk off periods,” says Reeve.

This diversification objective has nearly always been attained, as ASGM has generally been positive when stock-markets were negative. As with ASGM, the currency overlay strategy is based on multiple factors, with minimal momentum exposure, and so it is very lowly correlated to traditional, trend following CTAs. For instance, since 2016, ASGM has traded sterling from the long and short sides. The strategy was short of the pound, going into the Brexit vote, and later went long.

Fees for dynamic currency hedging can be little more than on passive currency hedging mandates, which Aspect also offer as part of a solution. The smallest ticket size is partly a function of the volatility target and is probably around $200 million. Aspect is developing a good dialogue with investment consultants about the offering.