What can Michael Lewis’s “Moneyball” theory teach the retail wealth management industry?

The theory – coined in Lewis’s book, Moneyball: The Art of Winning an Unfair Game, and later adapted into a movie starring Brad Pitt – centers on a small baseball team, Oakland A, competing with its much larger rivals. While it could never match the depth of its rivals’ wallets, the team found a way of identifying underpriced players through statistical analysis. This identified metrics that were good predictors of performance and yet weren’t valued in the selection procedure.

This scenario does, PriceMetrix says in a new report, resemble the problem facing firms hiring in the wealth industry: top talent is very mobile and thus very expensive, and yet the payoff to the hiring firm remains uncertain.

A successful hire depends on the absence of compliance issues combined with a healthy margin, the report says, and so it focuses on production and the growth in production over time as a measure of success. It uses a model to predict future revenue as a function of advisor and book metrics from a benchmark time period. To do this, it used data from end-2006 to predict the twelvemonth total revenue for 2011 – data which the firm already had.

While current production is highly predictive of future production, as would be expected, fee revenue is more powerful as a predictor of future revenue than trailer or transactional revenue – so the greater the share of fee revenue in an advisor’s books, the higher the chance of revenue outperformance in the future.

Another measure that is linked to future success is the quality of assets on an advisor’s books. Controlling for other factors, every “high asset household” (defined as $250,000+ investable) on an advisor’s books increases future annual revenue by $1,650. Conversely, the number of small asset households (below $250,000) had a negative effect, other things being equal, with each additional small household expecting to decrease future annual revenue by $270. For the average advisor with 173 small accounts on his or her books that could equate to an annual “penalty” of as much as $46,000, the report says.

The depth of the client relationship and the share of wallet is another important indicator of future revenue. The report captures the depth of an advisor-client relationship through a number of metrics, such as the number of retirement accounts on an advisor’s books and average accounts per household. Other things equal, PriceMetrix’s analysis finds that each retirement account increases expected annual future production by $510, while more accounts per household also boosts an advisor’s predicted performance.

Another relationship that emerged from the data was that the effect of median household size depends on whether an advisor has deep or shallow relations (as measured by accounts per household). While conventional thinking suggests that it’s good for an advisor to have a high median household size, the data challenges this assumption when viewed on its own. Instead, it offers an alternate idea: that a high median household balance is positive only when it is combined with deep relations, but actually has a negative impact on future revenue when combined with shallow client relations.

“Households with significant assets are attractive, but the medium to long-term benefit (in terms of production) is only realized if one can capture a high proportion of that household’s share of investable assets,” PriceMetrix said. The report says the risk of “customer churn” in an advisor’s book is often overlooked. This could work two ways – overvaluing an advisor with large but shallow relationships, or undervaluing an advisor who scores poorly on traditional metrics but has durable client relationships.

The age issue

Experience is commonly thought to be linked to production, making long-time advisors attractive for hiring firms. However, the analysis in the report didn’t bear this out, finding that – without controlling for other factors (and thus without controlling for other correlations within the data, such as that between experience and assets) – experience had a negligible effect on future production. What’s more, controlling for other factors, experience is actually found to have a negative effect on future production, with every year of experience decreasing it by $12,700. Simply put, when comparing a younger advisor with otherwise similar metrics to an older advisor, the younger advisor would likely be a better bargain for a hiring firm, providing more future growth.