Myth-Busting Quantitative Investing

Earlier this year, we launched the U.S. Systematic Equity Fund – I Series, utilizing a quantitative investment approach which is a strategy that combines data, technology and investment expertise within a disciplined, rules-based investment process.

If you are evaluating a quantitative strategy for your portfolio, you are likely considering two related questions: how investment models are used and what role people play in the decision-making process. Both are important considerations and central to understanding how quantitative strategies are developed and managed.

How quantitative investing is utilized in Wespath’s portfolios

Our team has incorporated quantitative managers across several equity strategies.¹ As mentioned, earlier this year, we launched the U.S. Systematic Equity Fund – I Series, which employs a quantitative, or “systematic,” investment process. In this context, we define systematic investing as a research-, data- and technology-driven approach that uses rules-based portfolio construction to manage risk tightly versus a benchmark while seeking modest outperformance. The focus on a repeatable, rule-based, technology-driven framework here makes the fund a clear example of a quantitative option.

Wespath’s U.S. Equity Fund – I Series (USEF-I) has exposure to this systematic strategy through its own investment in the U.S. Systematic Equity Fund – I Series, and we added the same subadvisor, BlackRock, to the International Equity Fund – I Series in August 2026 to manage developed market non-U.S. equities using a similar approach.

We also added exposure to a strategy with quantitative characteristics in the fourth quarter of 2025, when we invested in a strategy managed by Arrowstreet within USEF-I. Arrowstreet manages an all-cap strategy that leverages proprietary models, disciplined risk controls and a focus on stock-specific opportunities across the U.S. equity landscape.

Our investment team has high conviction in the repeatability and discipline of these managers’ investment processes. Importantly, the managers employ differentiated forecasts and sources of information that we believe complement the passively managed strategies and the other active managers within our portfolios.

Separating Perception from Practice

Despite its adoption across the investment industry, quantitative investing has several common misconceptions. Much of this stems from the attention given to sophisticated technology, large datasets and analytical models, which can overshadow the underlying investment principles that drive these strategies. Some investors view quantitative investing as a purely automated process. Others assume it is synonymous with high frequency trading, or that it’s entirely disconnected from fundamental analysis. These misconceptions can create the impression that success in quantitative investing is driven by complexity alone.

In reality, more complexity does not necessarily lead to better investment outcomes. The most effective quantitative strategies are typically grounded in clear investment rationale, disciplined processes and robust risk management. Understanding that distinction is key to separating perception from reality and better evaluating the roles these strategies may play within a diversified portfolio.

With that in mind, let’s take a closer look at three of the most common myths about quantitative investing and examine the realities behind them.

Myth #1: Quantitative investing is just a black box

Quantitative investing is often viewed as a process in which models make investment decisions with limited human involvement. That perception can arise from the emphasis placed on data, technology and analytical tools used within the investment process. However, these tools are typically only one component of a broader framework built upon research and fundamental insights.

Many quantitative signals are actually rooted in familiar investment concepts such as valuation, profitability, quality, momentum or other fundamental characteristics. The primary difference between a quant strategy and more traditional approaches is not the investment rationale itself, but the ability to evaluate and apply these insights systemically across a broad universe of securities.

Human judgment remains central to the process. Investment teams develop and test new ideas, determine which signals are economically meaningful, evaluate the quality of data, monitor portfolio risks, and refine their models as markets evolve. Technology enables these insights to be implemented consistently and at scale, but it does not replace the expertise and oversight required to manage a portfolio effectively.

Viewed through this lens, quantitative investing is not a replacement for traditional investment principles. Rather, it is a disciplined framework for how those principles can be applied in a structured, repeatable and risk-aware manner.

Myth #2: Quantitative investing is high–frequency trading

Quantitative investing can be misinterpreted as high-frequency trading, a strategy which seeks to capture small pricing inefficiencies through a large volume of transactions executed over extremely short time horizons. While both may utilize technology and data-driven processes, they pursue investment objectives over very different time horizons and employ different portfolio management approaches.

In contrast to high-frequency trading, many quantitative equity managers focus on identifying securities with attractive return potential over months or years while maintaining a disciplined approach to portfolio construction and risk management.

For these quantitative managers, technology is used to evaluate investment opportunities across a broad universe of securities, develop return forecasts, and construct portfolios that align with specific risk and return objectives. Portfolio holdings may change as new information becomes available, but investment decisions are driven by evolving forecasts and considerations rather than rapid-fire trading activity.

Myth #3: Quantitative models are overly reliant on historical data

The use of large datasets and analytical models can create the perception that quantitative investing is primarily driven by historical data. This can be concerning to investors worried that quantitative managers might rely too heavily on the same historical factors and signals, resulting in greater overlap across portfolios and creating crowded positioning.

It is true that historical information often plays a key role in quantitative investing. For instance, many quant managers evaluate how specific signals have behaved across different market environments and assess whether those relationships remain supported by rationale and investment theory.

But this type of historical data is typically only one component of the research process. In addition to traditional market and fundamental data, many managers incorporate alternative and unstructured datasets, including company filings, earnings call transcripts, news, web activity and other sources that may provide insight to current business conditions and market dynamics. Quantitative managers have consistently increased their utilization of more current data as new sources of information become available, and techniques continue to evolve. These strategies have also developed more differentiated signals in response to past periods of overcrowding, alleviating concerns about dependence on the same historical data.

As a result, quantitative models are informed by a broader and more dynamic set of inputs than many investors may assume. The objective now is not to replicate historical outcomes, but to use available data and research to inform forward-looking investment decisions within a defined framework.

Quant and beyond

As our investment team continues to evaluate and evolve opportunities to enhance portfolio construction, we believe it is important for investors to understand quantitative investing for what it is, rather than what it is often assumed to be. Our experience implementing these strategies has reinforced our belief that this approach can serve as a thoughtful and risk-aware component of a diversified investment portfolio.

¹ All investments carry some degree of risk that will affect the value of the fund’s holdings, its investment performance and the price of its units. As a result, loss of money is a risk of investing in the funds. Past performance is no guarantee of future performance. This is not an offer to purchase securities or an investment recommendation. Please see the Investment Funds Description – I Series for information about the Wespath funds.