5 reasons why fundamental managers should use quant tools 

Quantitative tools for investment management, such as risk and return models and optimizers, have been around for decades, but many portfolio managers who take a fundamental approach to investing at best remain skeptical of their value. Some may even refuse to use the tools.

In this short missive, we set out to convince the skeptics that these quant tools can be useful, even without full-scale adoption. Here are five advantages that non-quants should know about quant tools:

1. Quantitative tools provide a “second opinion”

Quant tools enforce discipline. Risk models signal that it is time to rebalance to stay within a required risk budget. Return models may signal that an asset has become overvalued and it is time to trim or sell the position. These models can help managers separate the company from its stock and avoid the problem of “falling in love” with a name. I read a recent paper by William Bernstein published in Advisor Perspectives¹. In it, he relates a famous quote from Charles Ellis: “one wins on Wall Street by being smarter, harder working, or more disciplined than competitors.” The first two propositions, he points out, are nearly impossible; there’s always someone smarter and harder working than you. That leaves us with discipline, as suggested by rigorous models, as an approach managers can count on to help stand out from the crowd and win against competition.

2. Quantitative tools help in understanding portfolio risk

Quant tools can help us understand the risks we are taking, ones that go beyond knowing the markets or individual securities we invest in really well. One example of the benefit can be found in the typical 60-40 equity-bond allocations that are de rigueur for many investors. The benefit of such an allocation was formulated at a time when stock and bond prices were negatively correlated. But in recent years stocks and bonds have moved together, as lower interest rates of course drove bond prices higher but were also viewed as positives for equities. The benefit of diversification was lost, and through much of the 2000s, investors using multi-asset-class risk models may have recognized that they were not as well diversified as they thought, particularly when everything went south at the same time.

Another example might be for a manager who relies on sector overweights and underweights. A portfolio that is overweight Utilities and underweight Health Care, for example, is likely to have less volatility and active risk than one that overweights Technology and underweights Energy. The manager may be comfortable with that risk, but they should know it is there.

3. Quantitative tools align with clients’ analyses and mandates

Using ex-ante risk analysis and ex-post factor performance attribution can help fundamental managers speak the same language as intermediaries such as investment consultants and their direct pension fund clients. They also help asset owners compare performance of managers according to the same criteria and show that the manager is doing what they are expected to do. A fundamental manager who focuses on stock picking can use both to show that the portfolio does, in fact, take stock specific bets and that those bets paid off as expected. An asset owner can get factor exposures much more cheaply, so a discretionary manager may want to show they are earning their fees with good stock selection and not just factor bets.

4. Quantitative tools can help build efficient portfolios

Optimizers can seem like a black box, where a manager puts in expected returns, the optimizer cranks them with other inputs and spits out a portfolio with unintuitive holdings. But that is just a myth! Simply put, an optimizer can examine hundreds or thousands of combinations of assets to tell us which one gives the best return for the risk we are willing to take, or which one yields the lowest risk for a given return target. The optimizer will ensure that the expected risk contribution of each asset is commensurate with its expected return. Portfolios built using heuristics are much more likely to be inefficient.

5. Finally, quantitative tools can enhance performance

Risk models and performance attribution also highlight all the bets a manager is taking, some of which may be unintended (seen through risk analysis) and have a negative payoff (highlighted in return attribution). Identifying these potential drags on performance can help a manager avoid them, while at the same time maintaining their conviction on individual names. See our case study on a how quantitative tools can improve a portfolio’s returns for more detail, but the bottom line is that by acknowledging and avoiding unintended exposures managers can substantially improve their performance without diluting their convictions.