There are 3 primary elements of my investment process that set it apart from other-small cap growth managers:

• Letting tech-powered evidence, data collection, and analysis drive the investment process
• Recognizing and integrating behavioral biases
• Avoiding pitfalls and landmines

Tech-Powered Evidence, Data Collection, and Analysis Drive the Investment Process

We track company-specific datapoints from a variety of sources that can help us to evaluate a company’s fundamentals. These sources include earnings transcripts; financial statement analysis; corporate press releases; segment reporting; competitor, supplier, and/or customer news; and industry news. Any of these datapoints can potentially play a role in my decision to build, reduce, or maintain a position, as well as whether or not to buy or avoid a stock. We also regularly review industry rankings and market shifts to identify possible inflection points for both existing and prospective investments. Additionally, we push prospective investments through a “go/no go” list of financial and other data metrics.

All of this information helps me get a sense of where a company sits in its company life cycle, and whether it may be moving from one category to another, e.g., emerging into a more established growth phase or moving into a more mature stage of its life cycle.

Recognizing and Integrating Behavioral Biases

I’ve become highly interested in this area of investing, which is part of the broader area of Behavioral Finance. Only in recent years have I begun to fully appreciate how many mental mistakes anyone can make and how many different biases exist. By diligently working to recognize and avoid some of the more common behavioral finance mistakes (as well as possibly taking advantage of other investors’ errors), we should be positioned to make better investment decisions.

Some of the more common behavioral biases include:

Cognitive Bias, also known as the Dunning-Kruger effect, occurs when individuals with low knowledge or competence in a given area tend to overestimate their knowledge and abilities. If we overestimate our skills and/or knowledge, we may make poor investment decisions. After all, we don’t know what we don’t know.

Herd Effect Bias is the tendency to follow the actions of a group, often while ignoring primary research, company fundamentals, or valuation.

Risk Aversion Bias is the tendency of investors to favor perceived low risk investments over higher risk ones.

Confirmation Bias is the habit of looking for data or any other information that supports one’s current view while ignoring anything that may contradict that belief or indicate a change in expectations.

Avoiding Pitfalls and Landmines

We’re always on the lookout for red flags. If I find one that looks like it could take a year or more for a company to correct or recover from, I typically move to reduce or exit the position. Some examples include accounting irregularities or SEC filing delays, poor shareholder governance practices, poor capital allocation (such as ill-advised acquisitions), a sudden and/or confounding management departure, or a discontinuance of operating metric disclosures. In addition, if an investment thesis is difficult to understand or seems needlessly complex, we’ve learned that we’re better off staying away. We also try to manage risk when a holding’s valuation becomes extreme.

In my experience, really successful long-term growth companies are hard to find. In most cases they need both good management and a good business model. It’s our goal to try and ensure the Fund’s holdings have both. One example of a company with both traits is Coveo Solutions, which is a software business headquartered in Quebec, Canada that specializes in enterprise search engine solutions. Behind the scenes the last few years, management has been investing heavily in AI and developed algorithms that substantially improve search engine results, improving a client’s customer experience and lowering labor costs. Solutions include website key-word search (fewer ‘nothing found’ results), employee workplace search, customer service and support (improving call center productivity), and e-commerce product search (more personalized experiences). Seven of the top 10 technology companies are Coveo customers. In fact, many of its 700 customers are larger enterprises. While topline growth has been tepid due to customers beta-testing their new AI solutions (as well as a small product line being phased out), bookings have reaccelerated, and it should be just a matter of time before revenues begin to show the new product success. I see Coveo as a ‘software way’ to play AI adoption, and the stock has been trading at a very attractive valuation of about 2.5x next year’s revenues.

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