CFOs who are looking to expand their roster of financial analysts, and not finding as many high-quality ones as they want, perhaps should assign some of the blame to themselves.
Rather than simply seek top talent, companies should also impose “rules of engagement” on those they hire that are likely to result in superior-quality analysis, according to the Corporate Executive Board (CEB), a membership-based research organization.
Many finance teams struggle to define the skills and behaviors that will deliver effective analytic support, CEB says. They often use generic descriptions of desired analyst skills, such as “thinks critically,” “influences business partners,” or “fosters innovation.” As a result, they may inadequately develop their financial-planning-and-analysis (FP&A) staffs.
The best companies, CEB says, describe more specific behaviors and techniques they want their analysts to demonstrate, such as “challenges conventional ideas in both group and one-on-one settings,” “proposes clear action steps,” and “focuses on cause-and-effect relationships between observable factors.”
“Financial analysts may come out of MBA or undergrad programs with good, finance-oriented analytical toolboxes, but they don’t necessarily have a good sense for how to produce analysis that’s easily consumable by senior executives and business partners,” says Tim Raiswell, a senior research director for the CEB Finance Leadership Council. “One thing that differentiates great organizations is having rules of engagement for what good analysis and a good analytic process look like.”
For example, CFOs could have a rule that they won’t even look at an analyst’s report unless it begins with the one thing they need to take away from the analysis or actions the company or business lines should take as a result of the analyst’s findings.
CEB came to its insights through qualitative research that consisted largely of extensive interviews with 70 corporate FP&A heads, as well as academics and consultants. Based on that research, it recommends a methodology for analysts to follow that it calls “insight as a process,” which consists of three key principles.
The first principle is that all analysis should include both inductive and deductive elements. Inductive analysis, also called pattern analysis, involves seeing patterns in data and inferring cause-and-effect relationships between different data points. Deductive analysis starts with an expectation based on previous experience, like “three months after housing sales start to increase, we see sales of our products increase,” and assesses whether that relationship might be changing and should be retested.
“If, say, you favor inductive analysis, so that you base every analysis on looking for new trends, you may miss something important by not using deductive tools that look at historical trends and rules that have helped in the past for the same type of analysis,” says Raiswell.
The second principle is that all analysis should start with a hypothesis. Without one, the analysis will lack focus, and it will take longer to arrive at a useful conclusion. A hypothesis would be, “The three-month sales lag relative to housing starts no longer applies because consumers have less access to credit now.” Whether the hypothesis is proven or not, something will be learned.
A good hypothesis, CEB says, has three characteristics. First, it is testable. If there is not strong-enough data to perform a test, the hypothesis is pointless. Second, it is fragile. If the hypothesis is a rock-solid theory of what you believe to be true already, you will never see beyond conventional wisdom, which is the point of most financial analysis. Third, a hypothesis should be clear. If it requires a PhD in finance to understand it, start over again, the council advises. “The clearer the hypothesis is, the clearer the final work will be,” says Raiswell.
Similarly, the third principle of the “insight as a process” scheme is to apply Occam’s razor, a philosophical tenet that holds that when there are competing theories, it is best to first examine the simplest one — the one that makes the fewest assumptions.
“If your hypothesis is testable, fragile, and clear, you’ll find out pretty quickly if the simplest theory is not the correct one,” Raiswell says. “Financial analysts tend to be enthralled by complex concepts. It’s easy to get caught up in an idea like ‘the reason customers aren’t buying isn’t because they can’t get credit but because of this crazy new reason I think I’ve uncovered.’ Analysts should be reminded to at least kick the tires on the simple explanation before going to higher levels of complexity. They should remember why they’re doing what they’re doing: someone will consume their analysis and potentially act on it.”