10 Ways to Combine Quant and Fundamental Approaches that Work (and 10 that don't)

Can quantitative and fundamental approaches be successfully combined?

In my estimate, this has been a top 5 industry question for a long time, including this conference at which I’ll be speaking at tomorrow

The short answer is: Yes

  • More-so, I believe quantitative approaches cannot work without being guided by fundamental principles and insightful questions. Even if the model is fully technical or based on machine learning techniques, there still exist underlying ideas and assumptions on which it is built.

  • At the same time, I believe fundamental analysis can only work over a long time-frame when expressed via some unique, structured and evolving framework.

  • However, I don’t think that a 50/50 approach can work because of the different nature of the two investing methods: one attempts to forecast baskets of stocks, while the other attempts to forecast individual stocks.

The following list of observations is based on my experience of successes and failures of mixing quant and fundamental approaches

Collaborative Approaches That Can Work

  • Fundamental portfolio managers can be great at formulating an investment framework

  • Quants can be great at building and testing it

  • Fundamental analysts can be great at asking powerful questions

  • Quants can great at answering them

  • Fundamental analysts can have deep sector and industry knowledge

  • Quants can be great at modeling it with large amounts of data

  • Fundamental analysts can recognize a strategic ‘aha’ moment for a business

  • Quants can find ways to measure it across time and other companies

  • Fundamental analysts can be great at ‘connecting the dots’

  • Quants are good at removing emotions in constructing the final portfolios

Approaches That Don’t Work

In cases when Quants support a Fundamental Portfolio

  • Asking quants to build ‘screening’ models from which fundamental analysts pick stocks (the hit-ratio is too low for this to work)

  • Asking quants to justify a factor definition to a fundamental analyst (it will always sound too primitive or datamined)

  • Asking quants to explain the model on any given stock (too much noise)

  • Asking quants to generate alpha by giving them an ‘off the shelf’ academic paper (great quant is based on innovation)

  • Looking for consensus between quant models and fundamental opinions

In cases when Fundamental Analysts support a Quantitative Portfolio

  • Asking fundamental analysts to rank stocks to be used by as a factor

  • Allowing fundamental analysts to over-ride models, change weights, impose tilts, remove individual names in the final portfolio

  • Asking fundamental analysts to correct their biases

  • Asking fundamental analysts to come up with a factor

  • Vague attribution of decisions, including the cost of rejected ideas (type 2 errors)

Some “Final Questions”

  • Can fundamental analysts respect quant’s reliance on the law of averages even if many times on individual stocks the output looks like rubbish?

  • Can quants respect fundamental analysts’ depth of analysis even if many times it is not correct?

  • Can fundamental analysts be open to mentoring and sharing their thought process with quants

  • Can quants be honest about the balance of data-mining and innovation that they strive for?

  • Can each side become great at what it does rather than try to win over the other or strive for consensus?

PS. thank you Alex, Kei and Sheedsa for your inputs.