Areté Asset Management is an investment firm based in Baltimore, working hard on financial analysis for the last 10 years. Calcbench talked with founder Dave Robertson about his stock valuation philosophy and how he uses Calcbench data (yes, he’s a customer) to deliver value investing.

Q: Tell us a bit about Areté and your approach to investment generally, before we geek out on the data stuff.

We believe a big part of the effort to invest better simply involves piecing together all the good ideas so many other successful investors have already shared. In this respect, much of it is more straightforward execution than reinventing the wheel: keep turnover low, keep fees down, put skin in the game, and maintain higher active share to produce outsized results. In short, it is these things that are needed to prioritize long-term investing over firm growth.

We also distinguish ourselves with a strategic view of technology — which isn’t something widespread in the investment world, in our opinion. For example, we consider how connectivity, search, and organization tools can be used to improve knowledge management and how technology can facilitate client communication and education.

Q. You work with quantitative and macro research — so broadly speaking, what kind of financial data are you looking for and studying?

Our company tends to have long holding periods for stocks. We look for what Jack Treynor [an economist and investment theorist who died in 2016] called “slow-traveling ideas” — that is, ideas that aren’t easily discounted by the market. Areté uses company-specific financial data from Calcbench to model cash flows and returns on capital based on those ideas.

We also like to get context for that financial data from proxy statements. For example, the proxy often gives us insights into corporate governance, whether the board is a bunch of cronies for the CEO, and whether the leadership has compensation tied to long-term success.

So there’s quantitative and qualitative research we collect. Then Areté works iteratively with those data sets and perspectives to hone in on a core understanding of a given company.

Q. What are you trying to achieve with financial data? Proving or disproving hypothesis? Framing new hypotheses that hadn’t been considered? Something else?

We use data — from Calcbench, of course! — to understand a company’s worth and what the market is discounting for revenues and profits. We also create What-If scenarios to pressure-test a company’s sensitivities. For example, if profitability increases by 200 basis points, what does that do to its valuation?

We also use the data to understand what current prices imply for the cost of capital. If you value a large universe of companies based on current expectations, and use current stock prices, you can back out the cost of capital. That lets us take micro-level data and derive macro-level insights.

Q. Word on the street is that you love XBRL. Calcbench does too, since it lets us immediately pull data from the SEC’s corporate financial repository. Tell us more about your interest in XBRL.

Our technology roadmap drew us to XBRL. Areté is a small firm, so we were looking to avoid large, expensive financial databases and their equally hefty licensing fees. I watched XBRL develop for a while, and waited for a solution like Calcbench to come along and harness the power XBRL offers. The technology itself is well-organized, gives you a convenient way to download the data into spreadsheets, and lets you trace items back to their source documents.

My view is that XBRL has “democratized” financial data. It’s cheap, fast, accurate, and widely accessible. With a good vendor — like Calcbench — a consumer of financial data feels like a kid in a candy store, with access to all sorts of high-quality, affordable data.

Q. You’ve been in this line of work for years. How has the task of financial modeling evolved over time? What are you doing today that’s different from when you started?

The big changes are the massive increase in computational power, software capabilities, and connectivity. When I started in this line of work, I ran valuation models once a month on a mainframe computer, in an office building. Now I can do the same thing on a desktop or laptop computer, from wherever I am, and any time I want.

The challenge is that anyone else can do that modeling and analysis too — so scale is no longer the competitive advantage it used to be. Any smart analyst with a computer and Excel can do really cool analysis. So this work is no longer the exclusive purview of large companies with big budgets.

On a practical, daily level, I do get to do a lot more of the fun stuff: developing hypotheses, analyzing models, thinking about results, and that sort of thing. A generation ago you’d spend lots more time just gathering, entering, and organizing data.

Q. The analyst community is building more complex models, testing more complicated theories, investing more money, working more quickly. All of that suggests that data reliability and trustworthiness must be paramount, no?

It’s paramount. I can’t help but think of the book The Origin of Wealth, by Eric Beinhocker. He argues that the origin of wealth is knowledge, where that knowledge is then applied to some useful purpose. There’s a lot of truth in that, and you can’t “know” something unless it’s trustworthy.

In the investment world specifically, I’d say reliable and trustworthy data serves two useful purposes. First, it helps analysts and investors create a base of knowledge to better assess what a stock is worth and what its risks are. Second, it gives investors stronger confidence and conviction that the analysis is valid, and therefore you can act upon it.

All that said, let me also add — you need context to go with data. You need context to calibrate what the data represents and where the data came from.

Q. What do you expect to happen next? What will financial analysis and financial modeling look like in, say, 2030? How is Areté Asset Management prepared for the future of financial analysis?

What financial analysis will look like in the future will depend a lot on what gets rewarded. In a market dominated by ultra-loose monetary policy, like what we’ve had for several years now, the analyses that get rewarded are things like trend-following and data mining. I’m not sure those efforts can produce sustainable benefits. I believe there will be a power shift to people who have the patience to invest for the longer term.

If that comes to pass, then we’ll see a new golden age of valuation-based investing, because the marginal buyer of stocks will become concerned about the value of the stock he or she is buying. I’m amazed at the amount of money in index funds and share repurchase programs; when the change comes, it will really refocus efforts toward discounted cash-flow and other measures of intrinsic value.

I also believe that risk management will take a larger role in financial analysis. There will be more holistic efforts to incorporate history, cycles, valuations, and probabilities.

Q: What about Areté Asset Management specifically? How will you prepare for the future?

We’re going to remain focused on long-term, valuation-based investing — because that’s how durable benefits are created. We’re going to keep focusing on what tools can be developed and deployed to improve decision-making and on automating tasks so as to improve workflow. And we’re going to keep working to synthesize the ever-increasing amounts of information into insights and narrative threads that cut through all the noise out there.

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