Thanks to advances in computing power, today’s quant is not the quant of old.
Over the past century, quant has evolved from a theoretical concept into a practical investment approach. Ideas once confined to academia now underpin many successful strategies such as Federated Hermes MDT US Equity. Yet, there have also been notable failures, leading to scepticism in some quarters.
We believe today’s quant is fundamentally different from its earlier iterations. Lessons from past crises—like 2007’s “quant quake” and the 2008 Global Financial Crisis—highlighted the risks of excessive leverage and concentration during volatile markets. These events underscored the need for diversification, both across and within factors like value, size, and momentum. Rather than relying on single formulations, strategies now incorporate multiple versions of each factor.
Quants also look beyond traditional informational inefficiencies. Behavioural factors, driven by human emotion, play a significant role in market mispricing. Encouragingly, recent volatility hasn’t triggered similar quant-led crises, suggesting that investors have absorbed past lessons.
Moreover, advances in computing power and data availability have transformed quant. Today’s quant is vastly more capable, with access to insights unimaginable a decade ago.
Data! Data! Data!
Consider data: terabytes now fit in your pocket, and storage costs have plummeted. Since Clive Humby’s 2006 declaration that “data is the new oil,” digitisation has made data easier to collect and more central to the economy. Traditional datasets remain, but we now tap into deep, novel sources—like real-time credit card transactions or satellite images of parking lots worldwide.
If data fuels quant strategies, we live in an age of superabundance.
Superhuman Insight
Yet data alone isn’t enough. Interpretation is key—and here, machine learning and advanced analytics shine. These technologies extract insights beyond human capability. Machines can count cars globally and update forecasts daily. Real-time data—like truck movements from factories—offers unbiased insights that company statements may not.
A fusion of data and understanding
The active quantitative equity arm of Federated Hermes, MDT Advisers (MDT), for instance, uses machine learning to parse through 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 are clear.
This data-focused approach truly comes into its own when melded together with the savvy of an investment team that understands how different factors can drive alpha at a company level.
At MDT, one recent example of this kind of human/data fusion came about when we incorporated our new ‘economic moats’ factor into our modelling.
An concept widely associated with Warren Buffett, the idea of ‘economic moats’ investing is to find companies that have developed advantages allowing them to defend their profitability against encroaching competition.
A strong brand identity might be one example of this; ownership of robust patents might be another. Coca-Cola is a notable example of the former; pharmaceutical companies are an example of the latter.
Creating lasting value
But do share prices always reflect the value to be found in wide-moat firms? One reason to suspect that they might not be is how standard accounting treatment categorises these expenses. The expense of ‘moat building’ is generally counted in the here and now rather than being capitalised over a period of years. This lowers a firm’s current income and thus appears at first glance as value destruction. However, a more enlightened accounting treatment appreciates that such spending can create lasting value for the firm and improve profitability for many years to come.
When we adopted the ‘economic moats’ factor in 2023, the MDT team sought to capitalise on a company’s spending on potential moat-building activities. To do this, we aggregate estimated moats across a company’s industry to smooth out inequalities in individual company data reporting and call the resulting factor Industry Moat.
We believe the Industry Moat factor can help us most in improving stock selection among highly out-of-favor stocks—which are not precisely the same thing as value stocks, although there tends to be a substantial overlap. Our research has indicated that stronger return potential among out-of-favour stocks is associated with those having a wider economic moat. In other words, companies in narrow-moat industries that offer commoditised goods or services with low value-added features are less likely to rebound from a negative shock than those in wide-moat industries such as software, retailing, pharmaceuticals.
We believe the Industry Moat factor works particularly well within our investment process. Companies with high market sentiment, those that are beating earnings estimates quarter after quarter, or those with other strong quality characteristics are typically already well appreciated by the market. So, the presence (or lack) of a wide economic moat tends not to significantly impact expected returns for those companies.
Holding competitors at bay
However, even successful companies can fall out of favour for various reasons, some within their control but others beyond their control. An established moat can help out-of-favour companies keep competitors at bay while re-establishing operating results. Thus, we have begun to use this factor to help identify those companies that may be more likely to overcome negative sentiment/momentum to return to in-favour status, seeking to avoid companies whose stock prices continue to tailspin. We believe it helps us identify, to paraphrase Buffett, wonderful companies at wonderful prices.
Avenues to risk management
Although factors such as Industry Moat have an important role to play in discovering alpha, it’s important to stress that they are not the be-all and end-all of our ‘new quant’ approach to investing. We do not have to have a view on which sectors or factors will outperform at any given time; thus, we set sector limits and use risk models to avoid unintended risk exposures, focusing instead on stock selection to generate excess return potential.
Leveraging our computer capabilities, we update our stock forecasts — predicted alpha — each day for the entire universe of stocks. However, before we place trades, our team will review these to ensure we incorporate the most recent information. In addition, we review portfolio positioning daily, enabling us to adapt our strategies to take advantage of timely market opportunities. This active approach is designed to help ensure our clients’ portfolios always reflect our best, most current ideas.
Federated Hermes MDT US Equity
Federated Hermes MDT Advisers apply an active, systematic investment approach which seeks to generate excess returns by leveraging sophisticated modelling to identify forecasting techniques tailored to different company types. The recently launched MDT US Equity Fund in UCITS format builds on MDT’s 30-year track record of successfully managing the same quantitative US equity strategy through various market cycles. Targeting long-term capital appreciation, the fund invests across all market caps and styles within the Russell 3000, using MDT’s proprietary model to enable disciplined stock selection, enhance returns, and manage risk.
Visit www.federatedhermes.com/mdt
Disclaimer:
For professional investors only. Capital at risk.
The value of investments and income from them may go down as well as up, and you may not get back the original amount invested. The views and opinions contained herein are those of the author and may not necessarily represent views expressed or reflected in other communications. This does not constitute a solicitation or offer to any person to buy or sell any related securities or financial instruments.
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