“Simplicity is the ultimate sophistication.” – Leonardo da Vinci

In our opinion, most people do not make the connection between simplicity and sophistication. After all, we’re largely conditioned to think of sophistication in relation to complexity. If you need to provide a lengthy explanation using complicated terms and concepts, then it must sophisticated, right?

Not necessarily. For example, consider the investment process of the Quantitative Strategies Team at CI Segall Bryant & Hamill Asset Management (CI SBH). If ever there was a case for complexity, it would likely be for a quantitative investment team that oversees more than $1 billion in client assets (as of June 30th, 2025) for institutions and high-net-worth individuals through a wide range of niche and broad-market strategies. But for this team, we believe simplicity is a key and noteworthy differentiator.

The CI SBH Quantitative Strategies team is led by Scott Decatur, Director of Quantitative Strategies. Dr. Decatur’s career in the industry spans almost three decades and includes senior roles at well-established asset management companies. He earned a B.S. and M.S. in Computer Science and Electrical Engineering from Massachusetts Institute of Technology, and a Ph.D. in Computer Science from Harvard University with a doctoral thesis focused on machine learning in the presence of noisy data. You might think the investment process that Dr. Decatur and his team have pioneered would be overly complex, yet we believe it’s anything but. However, it doesn’t mean the process lacks sophistication – quite the opposite, in fact. The team believes in the power of simplicity when executing on an effective and transparent investment process designed to deliver consistent excess returns (i.e., outperform a benchmark or other relevant metrics).

The idea is that the simplest hypothesis (or investment model, in this case) that is able to adequately explain the data is most likely to be correct. This simple approach contrasts to quantitative investment processes whose models are relatively more complicated. From our perspective, some forms of excessive complexity may include:

1. Using many unnecessarily complex and convoluted factors
2. Using distinct factors and weights for each different industry or country
3. Employing models that vary over time; trying to time which factors may work under certain circumstances

Many “innovations” in an investment model can cause harm, such as a timing model that gets “whipsawed” in volatile markets, or a newly discovered factor that looks appealing in backtests but not in reality. A complex model that backtests well but fails in real life can be a hallmark of “overfitting” as a result of excessive data mining.

While collecting and analyzing data is a critical part of investing – especially within a quantitative process – the CI SBH Quantitative Strategies Team believes the key is focus, not volume. It’s tempting to call upon artificial intelligence (AI) to summon billions of data points and analyze them instantaneously. But rather than chasing endless streams of information, the team concentrates on the few insights that matter most. By keeping the process simple and purposeful, they avoid the noise and complexity that can cloud investment decisions and instead gain a clear understanding of what truly drives outcomes.

Given his deep background in mathematics, Dr. Decatur believes that if a simpler process is effective today, it’s more likely to succeed in the future versus one that’s highly complicated with many moving parts and factors to account for. Even if some novel factors don’t actively fail, they can still dilute the truly effective parts of a model. Therefore, the team avoids adding extraneous factors and complexity to their models, allowing for the productive parts of their model to “speak” loud and clear.

“Simplicity is a prerequisite for reliability.” – Edsger W. Dijkstra, Dutch computer scientist and Turing Award winner

The ultimate goal of the CI SBH Quantitative Strategies Team is to build and maintain a differentiated investment process that can reliably deliver excess returns. But how might you achieve reliability? The team conducts extensive research in U.S., developed international and emerging markets, as well as across large-cap and small-cap stocks. They see opportunity across global markets, but find smaller or non-U.S. companies especially compelling, as these businesses often attract less analyst coverage and may present undervalued opportunities with strong potential for price appreciation. Then they look to find a single, unified approach that they deem is likely to work well in various settings and can do so consistently over time. In fact, the team rarely needs to adjust their investment model – doing so only when exceptional circumstances arise.

From our perspective, a simple approach that has been effective across settings is more likely to work tomorrow than an approach that endlessly contorts itself to favor the types of stocks that happen to lead their specific market segment during a particular time period. Such contrived models run the risk of overfitting the narrow circumstances on which they were formulated. Instead, the CI SBH Quantitative Strategies Team’s simpler, broad-based investment model looks to identify stock characteristics that transcend the noise and get at the deeper “truth” that could drive stock performance and may generate enhanced returns.

The team derives their quantitative model from widely recognized principles of traditional fundamental analysis. Their stock selection model aims to achieve consistent long-term performance by systematically identifying undervalued stocks that also exhibit positive health and strength characteristics. Each security in a given universe is scored based on conventional valuation metrics, as well as momentum and profitability factors. This multi-factor approach seeks to target well-rounded securities, not merely value companies or high-quality companies, but those that simultaneously embody both traits.

With regard to managing risk, the team’s proprietary risk model utilizes an alternative approach to conventional mean-variance optimization that is designed to reduce the risk of unpredictable swings that may result from targeting a tracking error level. Rather than avoiding risk, the team seeks to redirect it—moving away from unproductive exposures and toward areas where their stock selection model has the greatest impact. Long-term historical analysis helps define guardrails across factors such as sector, geography, size, and style, allowing the team to take active, purposeful risks that can lead to better outcomes.

The team’s commitment to simplicity also helps to deliver transparency and clarity. A simple, intelligible process helps investors comprehend what is being bought, and why. It provides for better understanding when things are going well and, more importantly, in those periods when they’re not. An overly complicated process can obscure how investment decisions are actually made, thus potentially reducing confidence in its ability to perform in novel periods and environments, and preventing understandable explanations if and when it starts to fail.

Everything should be made as simple as possible, but not simpler. – Albert Einstein (paraphrased)

Even though the CI SBH Quantitative Strategies Team takes great care not to overcomplicate their investment process, they’re also equally careful to avoid oversimplifying it merely for the sake of simplicity. Their process has been crafted in an effort to properly balance attractive stock prices with strong and healthy company fundamentals. The team measures data carefully and intelligently in order to make proper “apples-to-apples” comparisons between stocks. They also implement prudent checks and balances in their risk controls and portfolio construction processes. In short, they do not sacrifice care and diligence for simplicity.

“Simplicity is a great virtue … but complexity sells better.” – Edsger W. Dijkstra

While it’s not a trivial task to create a simple but effective process, the benefits can be worth the effort. Yet, despite the potential investment benefits of a simpler process, simplicity can be a liability when communicating to investors the merits of a given process, since complexity does indeed tend to sound “impressive” and sell better. This is especially the case for quantitative strategies, where constantly adding new wrinkles might be seen as both proof of sophistication and required for the “arms race” that some claim to be playing and winning. Many people may be drawn to the latest “shiny new toys” that appear to be on the cusp of innovation (and, therefore, construed as more valuable), and investors are no different in our opinion.

However, we believe the real value lies in resisting the allure of flashy models constantly tweaked by teams of researchers and not falling for promises of ground-breaking factors fueled by AI. While these appealing ideas may sound intriguing, they go against the cardinal rule of simplicity that the CI SBH Quantitative Strategies Team believes is a driving force behind their investment success that has stood the test of time.