Why Quant for the FutureWhyQuantfortheFuture

Since the first quantitative equity funds launched more than 30 years ago, the demise of systematic investing has been predicted many times over. Most recently quants faced the “Quant Winter” that started in 2018 and lasted nearly three years. Many thought that this extended period of underperformance by systematic strategies was the nail in the coffin for quant investing. But quantitative equity strategies are not only alive and well, they are also in a remarkable position to address today’s evolving investor concerns.

While the investment opportunities that arise from quants’ ability to efficiently process data are important, the precision with which quants apply that data to construct portfolios and manage risk is what sets them apart. Expertise in portfolio construction allows quants to build diversified portfolios that aim to deliver on alpha promises while avoiding uncompensated risk. This precision may serve clients in several capacities, from adding consistent alpha in the most inefficient illiquid markets, to serving as a predictable core position that provides solid beta exposure with the ability to add value in most market environments. While upside may be more modest than that of concentrated active managers, the consistent application of the investment strategy enables quants to avoid style drift, and their flexible approach to portfolio construction makes it easier to customize for modern institutional investor needs, including being adapted for tax efficiency. 

Discipline, Data, Diversification 

As the concept of investment bias began to take hold in the 1970s and 1980s, many investors started to explore how to prevent biases such as overconfidence, loss aversion, and confirmation bias from disrupting sensible investment decisions. In response, quantitative investors built simple models that took advantage of these biases and applied their models dispassionately through bubbles and dips. As the first quant equity funds launched, simple measures of valuation, quality, momentum, and earnings trends delivered consistent alpha. The concept of “risk-adjusted returns” wasn’t yet appreciated, and investors were drawn to concentrated, high-conviction portfolios where portfolio managers could tell compelling stories about each stock held in their portfolio. In contrast, listening to a quant drone on about capturing market inefficiencies was enough to put investors to sleep.  

Today, those simple models have evolved to incorporate vast amounts of newly available data, paired with new analytical techniques such as natural language processing (NLP) and machine learning. In this more data-intensive, technology-driven world, quants can more effectively deliver on their traditional strategies while also expanding into more custom solutions that cater to nuanced client preferences. Outlined below are three ways in which quants can deliver outcomes for investors. 

Cracking the Code of Inefficient Markets

The most inefficient markets in the world have great alpha potential and often face greater investment challenges. For example, illiquidity is both a risk and a cost investors face when investing in areas like emerging markets, small or micro caps. When dealing with liquidity challenges, a quant’s ability to measure alpha potential and model trading costs to produce “net alpha” expectations is crucial. A portfolio with many small positions is both easier and cheaper to trade than a more concentrated strategy with larger blocks of buys and sells in the market. Thus, a systematic small cap emerging markets strategy typically has a higher capacity limit than its more concentrated counterparts. 

But inefficient markets also include a vast amount of data: tens of thousands of companies, multiplied by their underlying data points – often messy, inconsistent, or missing. This presents the challenge of both verifying the reliability and relevance of the data and determining whether the company or opportunity shows investment potential. Importantly, neither excessive nor dirty data poses a challenge for quants because they have a long history of leveraging tools to process data in these opaque markets. As a result, quants can bring valuable insights to these less efficient markets, allowing them to build diversified portfolios with more focused risk/return tradeoffs. As shown in the chart below, quant has delivered more consistent alpha than fundamental in the 9/2008-9/2023 time period.