Mahmood Noorani, CEO and Co-Founder of Quant Insight (Qi), is an experienced macro investor with a background as a rates derivatives trader, macro prop trader and portfolio manager at a macro hedge fund. His research of quantitative methods was motivated by a conviction that traditional macro investment processes no longer worked, that market structure was changing and that a new approach was needed to understand financial market dynamics. Mahmood was educated at the London School of Economics and Political Science. He started his career at Morgan Stanley and has since worked for firms such as BlueCrest Capital, UBS, Credit Suisse and Citi.
Quant Insight is a fairly well-known name in the US and Europe and is used extensively by several leading hedge funds. Asia, however, is a new market for you. For our Asian readers, can you introduce Qi and what the firm does?
Qi is a premium quantitative macro tool designed to help fund managers across asset classes better understand markets and make more informed investment decisions. It is the combination of best-in-class mathematics and programming, aligned with experienced market practitioners from various blue-chip buy- and sell-side firms. In short, the Qi algorithms show when an asset is in a macro regime, how those regimes shift, how factor leadership can change and provides a valuation overlay – when an asset is rich or cheap versus its macro environment. All derived by the independent relationship between asset price and macro factors such as growth, inflation, financial conditions and risk appetite.
What does that mean in practice? How do portfolio managers use Qi in their day-to-day processes?
In several ways. The common feature across all use cases though is Qi saves PMs’ time. In their need to track multiple securities, sometimes across asset classes and across time zones, PMs spend hours watching screens, reading subjective opinion-based research on websites, emails and long pdf documents. Data overload and the wall of noise they face are a complete time sink. First and foremost, Qi is about extracting actionable signals. Some more concrete examples: For the stock picker, Qi can identify when a company is being driven by macro factors. Many a time a bottom-up PM can pick the right stocks but see their trade blow up because of an exogenous macro shock they didn’t see coming. Analysing company fundamentals can leave you blind to macro risks. Qi identifies when a single name is macro-driven or a function of micro factors. When company fundamentals dominate, it’s business as usual. But, when it is a macro bet, what is the key exposure? Is it essentially a play on currency strength/weakness, or a function of crude oil prices or the next central bank policy announcement? Moreover, to what degree has the stock price lead or lagged the move in those macro factors? For multi-asset, absolute return investors Qi provides a framework to empirically stress test scenarios they care about. Which Asian equity indices are most sensitive to a slowdown in Chinese GDP growth? Which Asian currencies are most vulnerable to a bout of “risk off”? Moreover, in addition to a pure screening process that ranks sensitivity to macro factors, users can again add a valuation overlay. In just a couple of clicks, an Asian equity PM could screen single stocks for their sensitivity to a sharp move in USD/CNH FX for example. And then see which names are rich or cheap to macro fair value. Sensitivity plus valuation = optimal hedging.
Is it about generating trade ideas? If so, how can investors test their efficacy? How good are your signals?
Yes, Qi can be a valuable source of ideas for PMs but it is much more than that. Earlier I stressed the time-saving element of Qi. On one level it is a tool to boost productivity. I should also stress its role as a differentiator. All PMs think about diversification in their portfolios. Very few diversify their pre-investment process. Macro especially is home to a lot of subjective opinions, usually delivered in long wordy documents. Some of that is insightful. An awful lot is low quality and a waste of time. Qi identifies empirical relationships and highlights dislocations in a simple, easy-to-understand way. Why not add a quantitative perspective to your existing discretionary investment process? If you disagree, it is, at a minimum, a sanity check that asks a healthy question – does the size of my position reflect my conviction level? If you agree and the qualitative and quantitative views are aligned, you have the best of both worlds! Size up! Then in terms of the efficacy of our signals. We have a back-testing rig enabling PMs to go back 12 years and see how successful a certain fair value gap – the difference between macro-warranted fair value and spot price – has been historically. It varies across asset classes but, on aggregate, our hit rates are around 65% showing there is genuine signal power in using macro. Qi is a product of intellectual property from professors at Cambridge University, Princeton and Harvard. That IP has been road-tested by people who understand markets and risk; former PMs at funds such as Brevan Howard and BlueCrest. The result is a premium product and that can be seen in the back-test results.
How do your clients use your work?
We have a web portal that users can customise so the assets they care about are front and centre. It also enables PMs to run those stress tests we discussed earlier. Our data can also be ingested via API. We tend to find systematic funds prefer this. Most have a plethora of momentum-based signals. Few have robust macro signals and they are constantly searching for diversification of signal. Risk managers also like the flexibility to look at fund risk from a fresh perspective. What if a collection of single stocks all chosen for valid, bottom-up reasons, together roll up to a large exposure to, say, high yield credit spreads? Is that an exposure the risk manager was aware of and is comfortable with?
Finally, what’s new? Any pending projects or innovations you can share with us?
All the above we firmly believe add value to any fund’s investment process. But in our desire to cut through the noise and really save people time, we’ve recently rolled out RETINA which stands for Real Time Notifications & Alerts. A PM identifies the universe of assets they care about. Set their signal parameters – how rich or cheap to macro fair value they want the security to be in order to be notified. Then RETINA simply pings them when that asset is at that pre-defined level. PMs get a pop-up on channels like Symphony, Slack or email alerting them that stock X is cheap or FX cross Y is rich versus the macro environment. Moreover, we’ve expanded the signals. In addition to pure macro valuations, we’ve added a trend and momentum picture. When is an asset on the cusp of a new uptrend or downtrend? When is an existing trend losing momentum and in danger of turning? It is, in effect, an off-the-shelf CTA trading strategy. And when aligned with our existing macro valuation framework, an incredibly powerful combination. And all pushed to you in a user-friendly way.