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How Behavioral Finance And The Analytics Hierarchy Is Reshaping IR

How Behavioral Finance And The Analytics Hierarchy Is Reshaping IR

In the recent whitepaper from Dan Romito, co-Head of Nasdaq Strategic Capital Intelligence, Dan discusses the evolution of the analytics hierarchy and how evidence-based behavioral targeting can better assist IR professionals in their investor targeting strategies.

Corporates navigating the modern dynamics of today’s capital markets may benefit from implementing the principles of behavioral finance into their daily workflow. Behavioral finance integrates conventional finance with the modern facets of psychology in order to map out the mechanics guiding the decision-making process. Capital markets participants are quickly realizing that the customary quant journey only takes you so far. As a result, best practices are being redefined to also incorporate a greater degree of behavioral analysis.


  • The emergence of behavioral analytics has created a distinct analytics hierarchy consisting of three buckets: descriptive, predictive and prescriptive.
  • Prescriptive analytics may help improve the accuracy of predictions and provide decision options by concentrating on the attributes displayed in real-time by a set of individuals under the institutional umbrella.


For instance, the traditional measure of pitching performance is considered to be the earned run average, or “ERA.” It has proven to be flawed as it does not separate the ability of the pitcher from the abilities of the fielders that he plays with. In other words, ERA is misconstrued as the most telling statistic for teams seeking the best pitcher because it does not take into account the tendencies of that pitcher in certain situations (for example, pressure versus pitching when there is a lead) and it assumes an equal capability of the fielders on each team. This dynamic should strike a chord with the IR community – allowing a false positive to emerge from utilizing broader institutional data rather than intelligence specific to a person. In this example,


  • Descriptive analytics would say a pitcher has an ERA of 2.50, so therefore, he is a good pitcher.
  • Predictive analytics would state that because of the 2.50 ERA, the pitcher is going to win 70% of the time.
  • Prescriptive analytics extends beyond the conventional approach by segmenting the players and evaluating tendencies in a mutually exclusive manner. Therefore, prescriptive statistics would affirm that the 2.50 ERA is more a function of the superior fielding capability rather than the overall skill set of the pitcher.
    • Under the prescriptive model, General Managers would seek a pitcher that better fits the team based on his individual tendencies, rather than be overly influenced by the collective stats of their team.




This is analogous to how the dynamic between the three buckets of analytics operates in the IR world:


  • Descriptive stats would state that a given investor prefers certain fundamentals such as ROE or margin, which is useless after evaluating the contrary – are there really investors on the planet that do not like margin or ROE?
  • Predictive stats would state that because the institution prefers ROE and margin, they are a 60% match with the corporate. Once again, probabilities based on collective institutional perspective without segmenting individual tendencies are inherently flawed.
  • Prescriptive analytics improves the accuracy of predictions and provides better decision options because it concentrates on the attributes displayed in real-time by a set of individuals under the institutional umbrella. Corporates can pinpoint the person, not the institution, who displays the greatest tendency of meeting with other corporates that resemble their specific investment thesis as well as map out the individual’s investment decision-making process.


With evidence-based targeting, corporates can help to eliminate some of the challenges of identifying and prioritizing institutional targets by more finely pinpointing those that offer the greatest opportunity. Moving forward, as IR professionals are increasingly compelled to maintain greater control over their respective narrative, pinpointing the individual investor who represents as close to the best fit possible may be important. Given the assortment of complex macro adjustments, whether it is MiFID II, ESG, the contraction of the sell-side, etc., evidence-based behavioral targeting provides forward-looking analytics that may help to identify where a particular investment thesis lies relative to the established tendencies of any given person.

Download the whitepaper to gain more insight into evidence-based investor targeting.



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