Man and machine: The combination of AI and investment expertise helping generate alpha

Predicting the future is hard, but if you don’t understand the present it's virtually impossible.

In today’s increasingly complex world, discretionary managers have to distill a huge number of potential market-moving variables, ever greater geographical inter-connectedness and cross-asset correlations into a coherent view and enhanced performance. Moreover, while the discretionary manager’s job has become harder, cheaper and ever more sophisticated, processing power means the machine (passive funds) have become increasingly prevalent. Fund managers face the twin dilemma of pressure to perform and pressure on margins. Using AI and machine learning to arm discretionary managers with additional insights will be the only way to improve the bottom line and deliver the alpha they seek.  

With the shake-up following MiFID II, we are witnessing a race to the bottom in the price of written research. At the same time, access to ‘thought leaders’ comes at a skyrocketing price. Any environment where a product is being driven down to loss-leading levels cannot reflect well on the quality of that product. One objective of MiFID – to reduce the amount of writtenresearch landing in investors’ inboxes – may have resulted in a smaller pool of ineffectual content.

Separating fact from fiction 

At a time when research consumption is under constant re-evaluation, what the industry needs is not more of the same subjective opinion-based research from various ‘talking heads.’ What is needed is a fact-based, smarter approach to the investment process. 

This is where the combination of investment expertise with high-quality data, proprietary algorithms and AI-driven machine learning models come into their own: to help investors make sense of complex trading environments and make better investment decisions.

This is the reason Qi was set up by a group of investment professionals from blue chip investment banks, asset managers & hedge funds who understand how to take best-in-class AI & adapt their insights to the reality of day-to-day trading in financial markets.

AI-driven, outcome-based investing has arrived

We help active managers evaluate and validate investment decisions. Unlike subjective ‘talking heads’, Qi’s algorithms empirically present the macro characteristics of asset prices. The models provide actionable ideas in a user-friendly way to help discretionary investors generate alpha across all asset classes.  

The numbers speak for themselves: Qi baskets are up 50%

Equity baskets can create a level playing field for research while also addressing the common challenge: how to trade a macro theme like rising interest rates, for example. Not the equity baskets of old, based on naïve one-dimensional correlations or discretionary assessment, but the next generation version. 

Powered by best-in-class algorithms, baskets can empirically identify the single stocks most sensitive to a chosen macro theme such as:

  • A broad reflation dynamic 
  • Equities sensitive to higher yields
  • Currency shifts 
  • Higher inflation expectations AND
  • Increased expectations of an end to Quantitative Easing.
  • Distilling multiple macro factors into one basket results in a more efficient trading expression. 

Furthermore, equities often have a higher beta to the chosen macro theme than the underlying factor itself; for example a stock may move 10% for a 1% gain in oil. And equity baskets tend to offer a better Sharpe ratio.

Qi has derived a number of baskets including:

  • European equities which benefit / suffer from a stronger EURO currency; 
  • Baskets of US & European equities that outperform / underperform during bouts of interest rate volatility;
  • A basket sensitive to UK economic growth; 
  • A basket of pan-European stocks exposed to political turbulence in Italy.

The first of those – a basket of single names that benefit/suffer from a stronger EURO currency – is shown in the chart below. Both the Qi basket (green) & the broader Euro Stoxx 600 (yellow) are indexed to 100 as of Jan 1st 2017. You can immediately see that while the broader European equity universe returned around 3.5% by the end of q1 2018, Qi’s basket was up over 50% which provides a very practical example of how to combine best-in-class machine learning with investment experience and expertise.

Source: Bloomberg

Quant Insight Limited is an appointed representative of Duff & Phelps Securities Ltd, which is authorised and regulated by the financial conduct Authority. The Information provided regarding returns is based on past performance and is therefore not a reliable indicator of future returns