Category: Inside Business

The original premise of a “hedged fund,” as a financial journalist originally described the concept in 1949, was simple: A portfolio balanced between long and short positions could profit in nearly any market.

That idea may have taken a while to seep into the mainstream, but as it has over the past decade, the hedge fund industry has exploded, rocketing from $310 billion in assets under management in 2002 to more than $2 trillion today. Institutional investors and high net-worth individuals flocked to these largely unregulated, nonpublic funds in no small part because they offered access to assets and trading strategies that are all but impossible to replicate.

But new research from Nicolas Bollen, the E. Bronson Ingram Professor of Finance, says those hedge funds that are hardest to imitate—something investors look for and for which they often pay a premium—are the ones most prone to failure.

In addition, Bollen finds that these types of funds contain a significant amount of volatility, indicating that they are vulnerable to the type of risks they are supposed to guard against. “This result suggests the presence of an omitted but potentially catastrophic risk factor in funds for which standard regression analysis fails,” Bollen writes in the study, forthcoming in the Journal of Financial and Quantitative Analysis.

Those previously undetected risks raise the annual probability of failure for hard-to-replicate funds from 10 percent to 12 percent. The findings have implications for investors who rely on statistical models to screen funds for heightened risk factors as part of their due diligence process.

Determining Hedge-fund Performance

The difficulty in assessing hedge fund performance lies in the industry’s opacity. Fund managers report returns publicly at their discretion, leaving wide gaps of data about holdings, accuracy, and even whether a fund is still operating. (In October 2011, hedge funds with more than $1.5 billion in assets under management were required to start disclosing fund details to U.S. regulators, but that information will not be made public.)

As hedge funds have grown, academic researchers have developed statistical models designed to correlate hedge fund returns with known investment strategies. Using these models, along with data from a broad cross section of funds from 1994-2008, Bollen found that more than one-third of all funds cannot be correlated to known style factors. The phenomenon becomes even more pronounced in funds with short histories.

Bollen suggests those results indicate that using hedge fund regression models to learn about a fund manager’s trading style and selection of assets may be even weaker than previously thought. Further, he says it may be a Sisyphean task to try to develop a complete set of risk factors, especially those representing catastrophic losses during rare events.

Where does that leave investors? For the time being, relying more heavily on qualitative judgments about things like a fund manager’s background and strategy mix than the quantitative analyses of econometricians.

A version of this article originally appeared in VB Intelligence.

Lowest prices of the year! Markdowns! Exclusive Dealer! Top Quality! We’ve all been exposed to them—the marketing strategies promising bargains or high value. Yet as alluring as those pitches can be, consumers draw very different—and sometimes contradictory— conclusions when it comes to sale prices or value. To make it even more challenging, consumers often fill in gaps in their knowledge by drawing inferences about products.

New research co-authored by Steve Posavac, the E. Bronson Ingram Professor in Marketing, finds that in some consumers’ minds, price denotes quality. Yet for others, low price leads a consumer to believe he or she is getting a good value.

“Consumers rarely have complete information and use various strategies to fill the gaps in their knowledge as they consider and choose products,” the researchers wrote in an article published in the April 2013 Journal of Consumer Research. “One of these strategies involves using naive theories: informal, common sense explanations that consumers use to make sense of their environment. For example, consumers may believe that popular products are high in quality while also believing that scarce products are high in quality.”

Posavac and collaborators Hélène Deval, Susan P. Mantel and Frank R. Kardes found that consumers use a series of theories when considering value and price. How they size up a possible purchase depends on what is on their mind when they’re thinking about a given product— something marketers need to take into account when crafting ads, marketing strategies and promotions.

Price vs. Quality Experiments

The researchers conducted eight experiments that tested marketing techniques that leaned toward price or quality. In one experiment, consumers were shown an ad for a bottle of wine with either a high or low price. When subtly reminded of quality, consumers evaluated the expensive wine more favorably than the cheap wine. However, when subtly reminded of value, they rated the cheap wine more favorably.

“In the case of price, most people simultaneously believe that low prices mean good value and that low prices mean low quality. But these two beliefs are not equally present in consumers’ minds all the time,” the authors wrote. In short, people can hold opposing beliefs about the same product.

When Product Marketing Backfires

Sales promotions succeed when consumers perceive that they are getting a good deal, but they can also backfire if consumers perceive that lower prices indicate poor quality. And if the company makes assumptions that one naive theory guides consumers, they run the risk that the strategy could actually cause a decrease in sales and perceived value. “For example, a marketer who feels that low prices signal value may go all in on a low-price strategy in an attempt to drive sales but may succeed only at reducing brand value and alienating consumers if a substantial percentage of the firm’s customers believe that low prices are commensurate with low quality,” they wrote.

Posavac and his fellow researchers cite retailer J.C. Penney as an example. The company moved to a new strategy of abandoning sales events in favor of everyday low pricing. However, J.C. Penney customers had been so conditioned to the naive theory that sales promotions signified good deals that the absence of such events was taken by many longterm customers to mean that there were no longer opportunities to get good deals—and sales dropped.

“[Companies] design a strategy by assuming that a certain naive theory is going to drive consumer evaluation and choice when, in fact, several naive theories are available to the consumer,” the authors conclude. So what’s the best strategy? The authors suggest that, in practice, marketing communications that set the stage by suggesting a given naive theory—quality, for example—and then make a product appeal in keeping with that theory will have the best results.

New research by Bob Whaley, the Valere Blair Potter Professor of Management, and Jacob Sagi, the Vanderbilt Financial Markets Research Center Associate Professor of Finance, has led to the creation of a recently launched group of NASDAQ indexes. The NASDAQ OMX Alpha Indexes are designed to help investors measure performance between individual stocks and exchange-traded funds. In practice, this means that the returns of popular holdings such as Apple and Citigroup could be isolated from sharp swings in the market. Traditionally it has been difficult—if not impossible for some—to trade directly on the relative performance of one asset compared to another.

In a new research paper describing how relative performance indexes work, Sagi and Whaley use the example of Apple (AAPL) compared to the S&P 500. In September 2008, AAPL’s share price plummeted by 32 percent, more than three times the amount lost in the broader markets. Seeing such an outsized decline in AAPL’s share price compared to the market overall may have signaled a buying opportunity to some investors. But as the financial crisis worsened, AAPL’s share price fell by another 1.4 percent. During the same time, however, the broader market fell by about 16.6 percent.

“AAPL outperformed the market as the investor expected,” Sagi and Whaley write. But anyone who had purchased shares of AAPL, while beating the market, would still have suffered a loss.

If an investment tool based on a relative performance index had been available to capture AAPL’s performance against the market, however, it would have yielded a substantial gain.

To try and replicate that same trade using the tools available at the time, an investor would have had to buy a long position in AAPL, while shorting, or betting against, a product like an S&P 500 index fund. The central risk in that scenario is that an investor would be exposed to an unlimited loss in the short position. By contrast, a relative performance index would place at risk only the original amount of the investment. Further, the money and time spent rebalancing those trades to account for volatility in both the long and short position would be too much for most investors to bear.

While the derivative products on the indexes developed by Sagi and Whaley can be used to invest in the relative performance of any pair of securities or exchange-traded funds, NASDAQ OMX so far has unveiled 23 index options tracking “highly liquid” assets, including:

• AAPL vs. SPY Index (symbol: AVSPY)
• Gold (GLD) vs. SPY Index (symbol: GVSPY)
• Twenty-plus Year Treasury Bonds (TLT) vs. SPY Index (symbol: TVSPY)
• Citigroup (C) vs. Financial Sector (XLF) Index (symbol: CVXLF)
• Emerging Markets (EEM) Index vs. SPY Index (symbol: EVSPY)

For now, Sagi and Whaley see the relative performance indexes as providing an easy and low-cost way to execute what traditionally has been a cumbersome trade. But as these indexes become more widely used, the authors say, they could introduce entirely “new return/risk management strategies to the investment arsenal.”

Read more about Whaley and Sagi.

Watch a CNBC interview with Whaley about the NASDAQ indexes.

With millions of new patients coming into the U.S. health care system over the next decade, the term “operations” is taking on a whole new meaning in America’s hospitals. Starting in the year 2014, as many as 32 million additional people will be covered by health insurance under the federal reform law passed last year. That additional demand comes at a time when medical facilities are struggling to reduce costs, improve safety and provide higher patient satisfaction.

To address these issues in the past, many health care facilities looked to manufacturing and its specialized product lines as a model for how to treat patients most efficiently and effectively. But little research has been done on how well these new “focused delivery” units have worked. To investigate this question, Nancy Lea Hyer, Associate Professor of Operations Management, turned to a dedicated trauma unit at Vanderbilt University Medical Center for answers. The case study was published in the Journal of Operations Management in 2009, and last year won that publication’s Best Paper Award.

“The following two questions were used to guide our research,” writes Hyer, who collaborated on the project with Dr. John A. Morris Jr., Professor of Surgery and Director of the Division of Trauma and Surgical Critical Care at Vanderbilt University Medical Center, and Professor Urban Wemmerlöv from the University of Wisconsin–Madison. “(1) How does the concept of focus, as it is used in connection with manufacturing plants, transfer to the context of a critical care hospital setting?, and (2) How does focus affect operational, clinical, and financial outcomes?”

Since 1987 the Medical Center has served as Middle Tennessee’s only Level I trauma center—meaning it is equipped to handle the most severely injured patients—within a 150-mile radius. By 1993 executives at the Medical Center authorized the creation of a separate $5 million, 31-bed facility called the Vanderbilt Trauma Center. The dedicated facility opened in August 1998.

Administrators staffed the Trauma Center with a designated team of doctors, nurses, social workers, even security and cleaning personnel, and outfitted it with X-ray and lab equipment. Physicians work 12-hour shifts instead of the traditional 24-hour rotation, so they are in-house and immediately available during their time on duty. And the facility itself was designed with an open-bay concept to accommodate the center’s 14 intensive care unit beds. This layout permits cross-trained staff to assist each other as needed and makes it easy to rapidly reconfigure the unit to respond to changing circumstances, such as a sudden influx of critically injured patients.

Prior to the creation of the Trauma Center, patients were transferred from unit to unit throughout the hospital as their recovery progressed. In directing patient care, trauma physicians traveled all over the hospital seeing patients and interacting with a wide array of staff, who cared for both trauma and nontrauma patients. In the new critical care facility, the dedicated staff delivers care in a single location and manages patient care through much, if not all, of the patient’s hospital stay. This has allowed the unit to develop and hone a specialized set of treatment protocols.

The Trauma Center also has its own financial director, who tracks physician and staff performance in much the same way a private company would. And to further differentiate themselves as an independent entity in the hospital, nurses and other staff wear distinctive black uniforms.

Did the measures work? To answer that question, Hyer and her co-authors examined the dedicated center’s length-of-stay, mortality rates and financial metrics. To compare before and after performance, researchers used data from 1996–1998, the two years before the separate Trauma Center opened, and from 2000–2002, which allowed for a period of adjustment.

The length of stay for patients treated in the dedicated trauma center declined by an average of 6.5 percent overall, and by 15 percent for those with more severe injuries. However, researchers detected no change in the mortality rate.

On the financial front, trauma care operations showed losses of $2.2 million and $1.75 million in 1996 and 1997, respectively. In 1998 the Trauma Center saw a small surplus of $50,000. By 2000 and 2002, however, the independent trauma facility showed surpluses ranging from $5.5 million to $7.89 million. Calculated on a per-patient basis, the unit turned an average loss of $578 per patient into a surplus of $2,493 per patient. Researchers also found similarly positive results when comparing the unit’s performance to benchmark peers.

“The discovery by the trauma unit’s managers that [Vanderbilt] discharged patients quicker, while charging comparatively less for its services, was both an affirmation that the focused hospital unit worked as intended and a sign that they needed to increase the charge levels and have [the Medical Center] translate these into better contracts with the payers,” the authors write.

Looking into some of the reasons for the improved performance on length-of-stay and financial measurements, researchers found ways the staff became more efficient treating patients. For example, before the creation of a separate trauma center, tracheotomies (a common procedure for patients who may be on a ventilator for an extended time) had typically been performed in an operating room, where it took an average of 80 minutes’ worth of physician time. In the new Trauma Center, doctors developed a bedside tracheotomy procedure, which takes only 15 minutes of a doctor’s time and is just as safe as one performed in a fully equipped operating room. Whether done in an operating room or at the bedside, however, the high reimbursement rates are the same, allowing the Trauma Center to capture the financial gains.

The finding prompted the unit to redouble its efforts to charge procedures more accurately for reimbursement, and most important, not to overlook them as had sometimes been done when patients were shuttled between departments.

But the authors acknowledge that one case study is not enough to suggest a need for broader changes within the health care system. Further, they write that nothing in their research suggests that the creation of a dedicated unit in itself is “a sufficient condition for success.” Rather, such initiatives should be aligned with other changes to factors such as hospital infrastructure, management and culture.

Nevertheless the case does indicate a need to further explore reasons why a dedicated unit can help bolster hospital performance. “Hopefully,” the authors write, “future studies will determine with greater precision what factors need to coexist with [focused hospital units] to create better performing health care delivery organizations.”

Watch Hyer’s video about process improvement.

The 2010 U.S. Congressional elections saw an unprecedented boom in campaign spending—$4 billion in all, with about $1.12 billion coming in the form of individual contributions to candidates, according to the Center for Responsive Politics. While political pundits continue to debate what impact this money has on election outcomes, new research from the Owen School points to some clear winners: the individuals who donate and the corporations they support.

Using innovative techniques to match geographic areas that are most affected by government policy with “economically relevant” politicians, the husband-and-wife team of Alexei Ovtchinnikov, Assistant Professor of Finance, and Eva Pantaleoni, Researcher at the Vanderbilt Kennedy Center for Research on Human Development, analyzed nearly 5 million campaign donations between 1991 and 2008.

What they describe in a new research paper is strong evidence that individuals who make political donations—whether at the behest of firms or not—directly benefit companies in their communities.

“The reason we look at individual contributions is because it accounts for about two-thirds of all the money given directly to politicians,” Ovtchinnikov says, noting that only about 10 percent of firms are actively involved in campaign finance. “Individuals are the big players in this game.”

But it’s companies that are reaping the most recognizable benefits. Ovtchinnikov says firms located in areas that most intensely target “economically relevant” politicians see positive changes in return on asset (ROA) and market-to-book ratios. The bottom-line boost that comes from campaign donations is similar to investing in a new research-and-development or capital-expenditure project.

Further, the economic benefit to firms strengthens when donations come from areas that have high unemployment rates—even if the politicians on the receiving end do not live in that district.

The new study also finds that political contributions flow disproportionately from companies’ home districts to key members of Congressional committees with jurisdiction over their industry. “What you’re seeing is an ability for people to reach politicians with dollars when they can’t reach them with votes,” Ovtchinnikov says.

The net result is that a significant amount of political donations come from narrow geographic clusters. Between 1991 and 2008, for example, three small areas around New York, Chicago and Washington, D.C., accounted for 11.7 percent of all campaign contributions—$425.9 million—even though they represented less than 2 percent of the population.

While the most recent study examined individual donations, a previous study by Ovtchinnikov and others published last year in The Journal of Finance shows a correlation between corporate political donations and higher stock returns.

“Our results … suggest an extremely high rate of return for firms participating in the political contribution process,” Ovtchinnikov and his co-authors write. “Alternatively, it is possible that politicians find it most beneficial to grant favors to large firms because those are the firms that generate the largest amount of tax revenues and jobs.”

In a similar study, David Parsley, the E. Bronson Ingram Professor in Economics and Finance, has found that corporate lobbying is “positively related” to a firm’s financial performance. In that study—where the top five firms in the U.S. accounted for 42 percent, or $160 million, of the total amount spent on lobbying in 2005—Parsley says that portfolios of companies engaged in the most intense lobbying efforts significantly outperformed their benchmark peers.

“Firms in this category earned an excess return of 5.5 percent over the three years following portfolio formation, while the rest of the firms earned essentially a zero excess return,” Parsley and his co-authors write. They do note, however, that the study’s results indicate that the gains were achieved through defensive lobbying, suggesting that simply spending the most on lobbying does not necessarily lead to better financial performance.

For his part, Ovtchinnikov says these studies, by demonstrating that campaign and lobbying expenditures have positive effects on corporate performance, open up intriguing new lines of inquiry for researchers.

“We have shown that firms are benefiting,” he says. “Now we need to begin asking why they benefit.”

Read more about Ovtchinnikov and Parsley.

Try asking any Monday morning quarterback about blown fourth-down play calls in the NFL and you are guaranteed passionate opinions. In most fourth-down plays, an NFL team will punt or try for a field goal. But occasionally teams decide to do something that is viewed as risky—attempt a fourth-down conversion, or “go for it.”

Associate Professor of Management Ranga Ramanujam, David Lehman from the National University of Singapore and other researchers studied 22,603 fourth-down decisions over five NFL seasons to understand when teams were more likely to attempt a seemingly risky fourth-down conversion. The goal is to apply this research to organizations. These findings are reported in a new study in Organization Science. The paper is titled “The Dynamics of the Performance-Risk Relationship within a Performance Period: The Moderating Role of Deadline Proximity.”

Ramanujam and his co-authors report that teams that were behind generally were more likely to go for it on fourth down. Also the more points behind they were, the more likely it was they would go for it. This effect became stronger as the game progressed. In other words, a trailing team was more likely to go for it later in the game rather than earlier in the game. Interestingly this was only the case for teams trailing by a margin of about three touchdowns or less. Teams trailing by wider margins actually became less and less likely to go for it as the game progressed.

“The idea here is that as the deadline approaches, the time available begins to factor into the decision to try something different in response to underperformance,” Ramanujam says.

The results suggest that as the game clock runs down, teams within striking distance of their opponent grow more and more eager to try risky plays that might help them win the game. However, teams outside that striking distance grow increasingly concerned with “saving face” or avoiding a risky move that might backfire and make them look stupid.

“We argue that this same tension between chasing organizational goals and avoiding reputational threats can help us understand risk-taking behaviors in other types of organizations,” Lehman says.

The goal of the study was to understand how risky organizational decisions might be shaped by performance feedback (i.e., the extent to which current performance is above or below the aspired level of performance) and deadline proximity (i.e., time remaining before an important deadline such as an earnings report date).

“We know that deadlines play an important role in organizational life. Part of what we’re trying to understand is how deadlines affect the well-known relationship between underperformance and risk taking,” Ramanujam says. “In other words, when are organizations more likely to deviate from routines? This is an important question for understanding a variety of important organizational outcomes such as innovation, change and fraud.”

Ramanujam, though, is quick to acknowledge that “although many managers have a natural talent for finding a football analogy for every business situation, football games are very different from the operations of business organizations. However, they are sufficiently similar in some key respects to make these findings potentially relevant to business organizations.”

For instance, fourth-down decisions typically are organizational decisions in that they are based on the inputs of various people on and off the field and are based on performance relative to a target and in reference to a deadline.

Ramanujam also notes that unlike prior studies that analyzed whether fourth-down conversions are as risky as they are made out to be, this study was about understanding when teams were more likely to go for it.

“What is especially relevant to our study is that teams treat this as a nonroutine choice,” Ramanujam says. “In more than 80 percent of fourth-down plays, the teams punted the ball.

Watch Ramanujam’s video about fourth-down decisions.

Generations of MBA graduates have mastered pricing models designed to evaluate companies based on capital assets like equipment, land and raw materials. But as the world economy shifts to one that increasingly places a premium on brainpower instead of horsepower, there are few, if any, reliable methods for analyzing the financial value of human capital.

To help bridge that gap, Vanderbilt’s Financial Markets Research Center brought together researchers last October to look at the value and risk of human capital and how it affects a firm’s business and financial strategy. For the event’s organizer, Miguel Palacios, Assistant Professor of Finance, the pursuit of this new model is more than just an interesting theoretical exercise—it’s personal.

The Colombia native is co-founder of Lumni Inc., a Miami-based company that has developed investment products to help send promising students to school who otherwise would be shut out of higher education by soaring costs. Operating in four countries, including the U.S., Lumni was cited by Businessweek in 2009 as one of America’s most promising social ventures. More recently The Economist highlighted the company in a piece about innovative new microfinance ventures designed to help students pay for higher education.

With co-founder and fellow Colombian Felipe Vergara, a former McKinsey consultant, Lumni has financed education opportunities for 1,800 students using more than $10 million. The company raises money from investors such as the Inter-American Development Bank, foundations, universities and wealthy donors. Students commit to paying a fixed percentage of their income—never more than 15 percent—into a Lumni-created fund typically for five years after graduation. In turn, that fund pays out proceeds to investors.

As The Boston Globe has described it, “These contracts, proponents say, would allow more kids to finish college. They would free graduates from crushing debt. And they could liberate youngsters to pursue socially valuable but low-paying work such as teaching.”

But it is not just save-the-world types who would benefit. Palacios and others point out that this model would also help fund the next generation of doctors and lawyers as a way to spread investor risk through a broad pool of students.

“The best analogy is insurance,” Palacios says. “Not everybody crashes. If you pool everybody together, you are in a much better position.”

The analogy may end there, however, for unlike insurance there is no set of accepted standards for quantitatively measuring risk or reward when it comes to education. How, for example, does the value of human capital change when a person adds a medical degree versus a bachelor’s in philosophy?

“This is the largest asset that most people have,” says Palacios, who estimates that human capital accounts for about 90 percent of aggregate wealth.

Palacios says human capital poses a particular challenge for researchers because, while it represents the single largest asset class in the world’s economy, one cannot directly observe its value or dynamics. “We merely observe wages, human capital’s dividends,” Palacios wrote in a 2009 paper. “Thus, we need a framework to determine human capital’s value.”

Researchers continue to look for a breakthrough model in the field, and so far Palacios’ work indicates that human capital is less prone to economic shocks than equities, bringing a new level of empirical rigor to academic colleagues and potential investors alike.

Yet there are other, more practical concerns for the student-investment concept beyond technical valuation, namely how to guard against a student putting off a career because there is no pressure to repay loans. To address this, Lumni draws on psychologists to help screen its applicant pools and designs the student contracts in such a way as to deter abuse of the system.

There is also a risk that potentially high earners—medical school students, for example—would avoid signing up because they could end up paying out more to Lumni’s investors than they would to a private student loan company. In such a case, the company offers better terms such as a lower percentage of income to be repaid.

Lumni is not the first company to attempt this invest-in-students model; its conceptual roots stretch back to the 1940s and 1950s when economist Milton Friedman first proposed the idea. More recently the companies MyRichUncle, a U.S. student financing company that is now out of business, and German-based CareerConcept, launched versions of the idea. MyRichUncle began experimenting with these types of student contracts in 2001. CareerConcept, however, has seen success since it began offering student investment funds in 2002. It has sent thousands of students to school across more than 20 countries, mostly in Europe, through eight funds totaling 40 million euros.

For now, Lumni is trying to establish itself in the Americas, with a goal of financing 1 million students over the next 12 years. Vergara told Businessweek, “My vision is to create a revolution in investing in human capital to show it’s possible to receive an education despite low income.”

But companies like Lumni can only execute on that vision if they have the analytical tools being developed by Palacios and others to correctly value the contracts they sign.

Read more about Palacios.