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by Tuck Communications   Apr 26, 2013

Notes on Research at Tuck

Playing with Bias

Say you’re an up-and-coming professional golfer and you want to climb the ranks of the sport as quickly as possible. What’s your strategy? Sure, lots of practice will help. And perhaps hiring a coach is a good idea, too. But when it comes to deciding where to play, Tuck professor Richard Rendleman, Jr. makes the following observation:  you are more likely to improve your Official World Golf Ranking (OWGR) by loading up on foreign tournaments.

In a working paper Rendleman co-authored with Columbia business professor Mark Broadie, the men report that the OWGR—a system for ranking male professional golfers who play worldwide, which determines eligibility for major tournaments and the World Golf Championships events—is significantly biased against players who compete on the PGA Tour. How biased? They found that “PGA Tour golfers are penalized by an average of 26 to 37 OWGR ranking positions compared to non-PGA Tour golfers.”

Rendleman and Broadie arrived at this conclusion after comparing the ranks from the OWGR to two unbiased methods for estimating golfer skill and performance: the score-based skill estimate, and the Sagarin method. Importantly, neither of those methods uses tour information to determine rank; they are simply based on statistical models comparing players within and across tournaments.

Rumors of OWGR bias have abounded in golfing circles for years, leading some rankings-savvy players to stick to foreign circuits as a path to the big tournaments. Unless something changes in the ranking system, Rendleman and Broadie’s research provides a good reason for all those trips to Europe and Asia.

M. Broadie, R. Rendleman, “Are the Official World Golf Rankings Biased?” Forthcoming in Journal of Quantitative Analysis in Sports.

Guilt By Association

Many of the services we have come to rely on, from air travel to utilities, depend on technical systems that we take for granted until they don’t work. Unplanned outages in power plants aren’t merely an inconvenience; according to GE Energy they are extremely costly for their owners and operators because of lost revenues, penalties and fines, and operations and maintenance costs. The same is true for the systems that underpin the industries of aerospace and defense, oil and gas, and infrastructure.

These mission-critical systems are made up of sophisticated subsystems—think of generators and turbines in a power plant—that require specialized engineers and technicians to restore them in the event of a failure.  Technical complexity has long been a dominant challenge in minimizing unplanned downtime. But as business models emerge in which companies collaborate in the operations and maintenance of the overall system, a new challenge arises: governance complexity. Increasingly, system uptime now depends on the joint actions of multiple parties.

In new research published this spring, Tuck associate professor Brian Tomlin and his co-researcher Sang Kim of the Yale School of Management examine how governance complexity influences system uptime. Combining the economic perspective of game theory with the engineering perspective of reliability theory, they explore the consequence of a business model in which different parties are responsible for different subsystems.

Does this collaborative model lead to better system uptimes than a traditional single-operator model? “It depends on what levers can be pulled,” says Tomlin. “We show that, all else being equal, the collaborative model tends to shift reliability investments away from failure prevention and towards recovery capacity. A key question with the collaborative model is how system outage consequences are allocated in the event that both subsystems fail. Our research suggests that careful attention should be given to this allocation question at the outset of these collaborations.”

S-H Kim and B Tomlin, “Guilt by Association: Strategic Failure Prevention and Recovery Capacity Investments

Accounting for Uncertainty

The national security expert Gregory Traverton has made a now famous distinction between a puzzle and a mystery. Puzzles are tractable problems solved with good information. Mysteries are surrounded by unreliable or overwhelming amounts of information and rarely lead to settled conclusions. A new working paper by Tuck professor Robert J. Resutek shows that equity investing—beset as it is with uncertainty—is more of a mystery than a puzzle, and that different forms of uncertainty affect future stock returns in different ways.

In “Sources of Investor Uncertainty and Expected Stock Returns,” Resutek and co-author Chad Larson of the Olin School of Business hone in on two types of investor uncertainty: that which is related to future fundamental performance (i.e., future cash flow), and the uncertainty attributable to information quality (poor accounting). Prior studies have singled out these categories as important to firm value, but until this paper no one has directly tested their power to predict stock returns.

Resutek and Larson found the two types of investor uncertainty to have strong but countervailing effects on stock returns. Fundamental uncertainty, they write, “leads to overly optimistic future earnings expectations, leading to future period return reversals.” In short, it makes the stock price go down. This result confirms prior studies.

More surprising is their second conclusion: that information uncertainty causes firms with poor accounting quality to realize returns that average 25 basis points higher than their peers with good accounting quality. Over the span of a year, that translates into a three percent difference in the return that stockholders require for making the investment (cost of equity). “To the best of our knowledge,” Larson and Resutek write, “we provide the first empirical evidence that variation in a conventional, accounting-based measure of earnings quality positively predicts future realized returns.” Why? That’s a mystery for another day.

C. Larson and R. Resutek, “Sources of Investor Uncertainty and Expected Stock Returns”