Investing Guide at Deep Blue Group Publications LLC Tokyo: Financial Engines Revs Up Retirement Plans
When they were newlyweds 23 years ago Jeff
Maggioncalda’s wife, Anne, challenged him to a Monopoly battle. Determined to
gain the upper hand, Jeff secretly created a program, known as a simulation
engine, to model the results of 1 million Monopoly games and used it to
generate a list of probabilities and payoffs for all the game’s properties. “We
played until she realized I had this little cheat sheet, and then she thought I
was a total jerk,” he laughs. (Actually, Anne, whose Ph.D. dissertation was on
the reproductive strategies of male orangutans, was amused by his creative
adaptation.)
Maggioncalda, 45, has used his knack for
simulations (and for landing on the corporate equivalent of Boardwalk) to win
at a real-life game, too: the financial
advice business. As chief executive of Financial Engines FNGN -0.48% he has
built the nation’s largest registered investment advisor, with (as of Mar. 31)
$92 billion in 401(k) assets under management for nearly 800,000 workers of 553
big employers, including Alcoa AA +0.88%, Dow Corning GLW +1.01%, Ford, IBM IBM
+0.99% and Microsoft MSFT +0.14%.
Savers pay Financial Engines from 0.2% to 0.6%
of their 401(k) assets annually to manage their nest eggs based on modern
portfolio theory, which aims to maximize the return for a given level of risk.
Its computers run thousands of scenarios (known as a Monte Carlo simulation) to
give each worker a picture of how much retirement income he or she is likely to
have, and use an “optimization engine” to determine the best portfolio given
the costs, quality and styles of the mutual funds available in each 401(k), with
a preference for low-cost index funds. Clients can consult with humans manning
call centers, but the advice they’ll get comes from the computer models. (For
two examples of Financial Engines’ portfolio makeovers, see below.)
Now, with its baby boomer clients edging toward
and past 60, Financial Engines is angling to grab a piece of another
potentially big business: managing assets and income payouts for retirees.
“Retirement income … it’s a really hard problem.
You’re looking at a 30-dimension probability distribution,” observes
Nobel-winning economist William F. Sharpe, a cofounder of Financial Engines.
While Maggioncalda’s entrepreneurial energy has built Financial Engines, the
80-year-old Sharpe, who won the Nobel in 1990 for his work on the pricing of
financial assets and the relationship between risk and return, is at its
intellectual core.
Back in 1996 Sharpe was offering asset
allocation software he’d developed on his website for free–to, as he puts it,
“give ordinary people the tools to think probabilistically about their
investments.” But during a long lunch at Stanford University’s student union,
Joseph Grundfest, a Stanford Law professor and former member of the Securities
& Exchange Commission, persuaded him that he’d make a bigger impact with a
for-profit business. “If we’re serious about changing how people behave in the
real world, we’re going to need to start a company,” Grundfest told Sharpe over
a second cup of coffee.
The two academics, along with the late Craig W.
Johnson, an attorney who took equity positions in startups, seeded Financial
Engines, and Grundfest went hunting for someone to run it. “You needed a
candidate who could have an intelligent conversation about modern portfolio
theory with Bill Sharpe. Right away your pool of candidates gets cut down by
99%,” he says. Grundfest settled on Maggioncalda, then a 27-year-old newly
minted Stanford M.B.A. who had written a prescient case study about how the
Internet could disrupt the stock brokerage business for a class taught by Intel
founder Andy Grove. The three older men hired Maggioncalda to write a business
plan and promised to eventually make him CEO–if he could raise the venture
capital to build the business. “At that time the idea of a 27-year-old CEO in
Silicon Valley wasn’t broadly accepted. Today, at 27, you’re washed up,” muses
Grundfest, now 62. Continue
reading…
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