Actively managed investment funds bring the expertise of professional portfolio management to the masses. So, how do the stock pickers of equity funds decide where to invest their clients’ money?
Well, it depends on the fund, but many of the approaches fall into two categories: traditional human-led bottom-up investing and the wide spectrum of algorithm-led quantitative styles.
While many quant funds seek to trade at the speed of light, or use statistical arbitrage to leverage their computing power, others use fundamental decision-making tactics similar to traditional bottom-up funds, but with the added power of AI or machine learning and algorithmic data processing.
Daniel Mahr: Fundamentally driven quant funds are really just, at the end of the day, a rules-based expression of human decision making…
So, what’s the difference between these two styles? And how do they compare throughout the portfolio life cycle?
Daniel Mahr: A fundamental quant is one that uses a lot of the same tools as a traditional portfolio manager in terms of evaluating opportunities for the portfolio, but where the implementation is done through a systematic process informed by data.
On the traditional bottom-up fundamental side, stocks are typically screened for specific characteristics, and then that subset is evaluated by analysts and portfolio managers in intensive detail, who then select which companies for the fund to buy or sell. Considerations can include key ratios such as price-to-earnings, earnings per share and debt-to-equity, along with analysis of companies’ competitors, sectors and current events that may impact performance. Some are concentrated while others may be more diversified.
The goal is often to create a portfolio of high-conviction ideas that aligns to a very distinct underlying investment philosophy. On the quantitative side, mathematical models are used to assess stocks, applying an objective, numbers-based approach to the same characteristics. Typically this approach allows you to evaluate a much larger investment universe, and utilize a vast array of data from a number of different sources.
Daniel Mahr: While we may use very different tools to evaluate companies along those dimensions, at the end of the day, we’re trying to do a very similar thing as traditional portfolio managers in terms of trying to find the best companies to put into our portfolios.
Once stocks are selected, a manager will continue to adjust its holdings. For traditional fundamental managers, this can mean adjustments based on their experience and judgment of company-specific qualitative research. Is the company’s executive management team effective? Are their earnings expectations strong? What are their competitors doing? Quant managers take a similar approach, but seek to make it objective.
Daniel Mahr: If we’re looking at valuation, well, valuation is typically expressed in a numerical sense, but if we want to look at a softer quality of a company, such as their economic moat, a quant investor is going to have to figure out a way to express that concept numerically.
Pulling in data across a far wider range of companies, quant funds have the benefit of scale, making more decisions, quickly and with conviction.
Daniel Mahr: By virtue of having the inputs to the process all be quantitative, they can be recalculated every day, and the portfolios can respond immediately when circumstances change at a company.
It can be difficult for financial advisors to know exactly why a traditional bottom-up portfolio manager makes a particular call. With MDT, however, there’s always a paper trail.
Daniel Mahr: It’s the data that’s driving it, and you can always dig in and see precisely what is driving a decision.
While the two styles have plenty of crossover, it might be quant’s missing element that makes it so favourable.
Daniel Mahr: That’s really an advantage of the quant approach, is the lack of emotion that it is able to display in terms of the companies that it invests in.
