Once associated primarily with institutional investors, quantitative investing has become more accessible to retail investors in the past 20 years through exchange-traded funds and mutual funds. Quant investing relies on mathematical models, statistical techniques and computer algorithms to digest large datasets, identify patterns, and help managers make investment decisions. While this type of investing typically involves advanced modeling techniques, including tools within the expanding fields of machine learning and artificial intelligence, that doesn't mean humans aren't involved.
Federated Hermes MDT Advisers (MDT), for instance, uses machine learning to parse vast amounts of data and seek a multitude of equity opportunities in an unbiased way. The investment team designs, oversees and continuously looks for ways to improve upon the various tools in its investment process. The entire process is transparent, and the reasoning behind investment decisions is clear.
"Our quant process is not a 'black box,' and trades are reviewed by members of the investment team prior to implementation," said Scott Conlon, investment director for MDT Advisers, the active quantitative equity arm of Federated Hermes.
Colon said the firm believes its quant investing process is clearly defined, testable and more transparent than traditional fundamental investment processes in many ways.
"When analyzing a traditional stock picking portfolio manager, it's sometimes difficult to determine that the approach can be repeated with success over time. You could have a portfolio manager who benefited from a hunch that proved successful," Colon said. While past success is never a guarantee of future results, quantitative investing seeks to provide a repeatable process.
One potential advantage of quant investing models is that they are testable using historical market data, allowing investors to understand how a particular strategy may have worked over a long period of time and what the outcomes may have been to an investor in terms of return and risk, Conlon said.
Another potential advantage of quant investing is the ability to achieve greater diversification by investing across a range of styles. Traditional fundamental strategies often incorporate a specific focus that aligns with the manager's philosophy, such as quality, value, small cap or dividends, to narrow the field of potential investments. If the environment for one of those styles is out of favor, returns may suffer.
By contrast, Colon says quant strategies can be built more flexibly. MDT aims to build portfolios that invest in many different types of companies with varying return drivers, so that the portfolio has the potential to not just outperform over time but to deliver more consistent performance in many market environments.
The quant investing process also offers portfolio managers additional avenues to manage investment risk, when compared to traditional fundamental approaches.
For example, MDT's quantitative managers don't necessarily have a view as to which sectors or factors -- for example, value, growth, small cap, energy - will outperform at any given time. Therefore, they use sophisticated risk modeling to try to avoid the risks of being overconcentrated in a particular sector or factor. Instead, the portfolios' active risks are allocated to stock picking.
"Stock forecasts and portfolio positions are updated daily, enabling our strategies to adapt to take advantage of timely market opportunities. This active approach is designed to help ensure our clients' portfolios always reflect our most current, best, bottom-up ideas," said Conlon.