Costello Research Market Efficiency

Are there upsides to “overboarding”?

Nilesh Patel — Mon, 14 Jul 2025 17:13:26 +0000

Are there upsides to “overboarding”? Nilesh Patel Mon, 07/14/2025 - 13:13

In This Story

How many board seats is too many for one director? That’s the question on many investors’ minds, as they confront the possibility of “overboarding”—directors being spread too thin to do their work effectively. BlackRock, for example, voted to oust one of Twitter’s board members in 2022 because he sat on the boards of six other companies.

However, new research from Sebahattin Demirkan, associate professor of accounting in the Costello College of Business at �鶹��Ƶ, suggests there may be upsides to board directors holding multiple seats at the same time. His paper in Asian Review of Accounting finds that deeply networked boards are more flexible and creative when it comes to a key competitive area: open-ended strategic alliances.

The paper’s co-authors are Robert Felix of The Catholic University of America and Nan Zhou of University of Cincinnati.

The researchers used a metric called “board centrality” to quantify the total social capital held by a company’s board of directors.

“Centralized directors are leaders who have been in the industry a long time,” says Demirkan. “They sit on multiple boards. And they are deeply connected within an industry, such as if Nvidia and Intel have a relationship and that person is working as a board member for both.”

The researchers used the SDC strategic alliances database to capture new alliances formed during the period 1998-2011. Their data-set comprised 18,412 firm-year observations.

First, they divided the alliances into joint ventures and incomplete contracts. Joint ventures, which involve setting up an entirely separate business entity, entail much less complexity and uncertainty than do contractual alliances, which by definition cannot account for all the surprising challenges that may lie ahead. The paper finds that centralized boards were more likely to embark upon both types of strategic alliances, but the effect was strongest for contractual alliances.

Demirkan stresses that the two types aren’t mutually exclusive; a contractual alliance can be a precursor to a successful joint venture. “I always use marriage as a comparison…Some cultures do arranged marriage because families know each other very well, because they were neighbours and so on and so forth. It’s trial and error but you don’t want to make too many errors because these arrangements may hurt your parent company business. That’s where board capital comes in. Centralized board members know which strategic alliances will work for this particular company.”

Further, centralized boards took on more strategic alliances when their CEOs were relatively new, underscoring boards’ customary advisory role. Similarly, diversified firms, which normally require more guidance from the board, saw a stronger relationship between board capital and propensity for strategic alliances. The same was true of firms with more intangible assets—in other words, innovative companies engaging in heavy R&D.

The researchers also looked into whether the enhanced alliance activity paid off for these firms. Their analysis confirmed that the special ability of centralized boards to form successful strategic alliances was associated with higher market performance, steadier returns and lower audit fees.

This strikes an interesting contrast with some of Demirkan’s earlier publications on similar topics. For example, a 2014 paper in Journal of Business Research found that firms with contractual alliances suffered from lower earnings quality, making them a riskier bet for investors. A 2016 paper in Contemporary Accounting Research linked contractual alliances to higher audit fees.

“The market doesn’t like companies with contractual alliances,” Demirkan says. “It discounts their stock prices, because the information environment is not good; it’s quite complex and the boundaries are not well defined.”

However, the new paper indicates that centralized boards act as a reassuring signal to capital markets that offsets some of the inherent risk of contractual alliances. “Boards with high social capital may manage this better, because they are much more knowledgeable,” Demirkan says. “They know which contractual alliances are good, and the market also trusts them to pick these alliances wisely.”

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GenAI brings us closer to automating investment expertise

Jennifer Anzaldi — Thu, 22 Aug 2024 18:39:34 +0000

GenAI brings us closer to automating investment expertise Jennifer Anzaldi Thu, 08/22/2024 - 14:39

In This Story

Yi Cao

Long Chen

Large language models (LLMs) such as ChatGPT and Google Gemini excel at being trained on large data-sets to generate informative responses to prompts. Yi Cao, an assistant professor of accounting at the Donald G. Costello College of Business at �鶹��Ƶ, and Long Chen, associate professor and area chair of accounting at Costello, are actively exploring how individual investors can use LLMs to glean market insights from the dizzying array of available data about companies.

Long Chen and Yi Cao

Their new working paper, co-authored with Jennifer Wu Tucker of the University of Florida and Chi Wan of University of Massachusetts Boston, examines AI’s ability to identify “peer firms,” or product market competitors in an industry.

Cao explains the significance of selecting peers by relating this process to the real-estate market. “The capital market is similar to the real-estate market in that a firm’s value is partially determined by the value of its peers. In the real-estate market, we price a home based on the value of comparable properties in the neighborhood, or the so-called 'comps.' In our paper, we aim to leverage the power of LLMs to identify comps for evaluating firm value.”

This task is at least as difficult as it is essential. It takes much time, skill and effort to gather, aggregate and manage data to select peers. However, the researchers reasoned that LLMs could do a lot of the heavy lifting of data aggregation and analysis for the individual investors, and produce a list of peers comparable in validity to that identified by human experts.

“The advantage is in the capability to utilize all the information potentially out there so that it is at least performing as well as other traditional methods that can help us investors and researchers,” says Cao.

For the study, Chen and Cao employed Bard from Google, now known as “Gemini,” as their LLM of choice because “Bard has a greater ability to utilize its pre-training data, which is arguably larger than ChatGPT’s and with more parameters,” says Cao.

After defining “product market competition” and forming a prompt for Bard, the researchers instructed Bard to limit its knowledge pool to a specific year within the period 1981-2023, in order to avoid “look-ahead bias,” i.e., future information scrambling the results.

“We need to understand that LLMs are actually a very powerful, new tool, unmatched in their efficiency, ability to process vast amounts of information at a low cost, and accessibility to the general public.”

They limited focal firms to large, publicly listed companies as there is less data out there for smaller or private firms. In all, the data-set comprised more than 300,000 focal firm-years.

On average, the LLM could generate about seven peer firms for a focal firm, a number that is similar to the SEC recommendations on how firms should disclose their segments.

The researchers then compared the LLM’s performance to the lists generated by three human experts for a set of 40 leading computer software companies. The average overlap was a little over 40 percent, greater than expected.

They also compared the AI-identified peer lists to two alternative systems for identifying peers: the federal government’s Standard Industrial Classification (SIC) codes and Text-based Network Industry Classification (TNIC), which compares firms based on linguistic similarities in their 10-K filings. The LLM’s output overlapped significantly with TNIC’s. Plus, the peers identified by the LLM were generally a better fit than those from SIC and TNIC, as their monthly stock returns hewed closer to the focal firm.

But TNIC outperformed the LLM in identifying peers for mid-sized firms within the sample, indicating that it is not a clear-cut case of universal LLM superiority.

“It’s especially beneficial for individual investors—as all the cost concerns that we’re talking about are especially relevant for them,” Chen adds.

Regarding the future of LLM, Chen states, “There are always costs and benefits associated with using generative AI. It is uncertain whether current systems will soon be obsolete.” When asked about the SEC adopting an AI tool for investors, Chen emphasizes that users need to understand the pros and cons of using AI to make their informed judgments “because AI cannot be held responsible for the information it provides or for how it is utilized.”

Chen concludes, “We need to embrace this new technology, but we must recognize that it is not yet in a perfect state. Competition to improve the technology is fierce. Our findings might just represent the lower bound of the effectiveness of the technology.”

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The distress anomaly: Where finance theory breaks down

Jennifer Anzaldi — Thu, 22 Sep 2022 13:54:03 +0000

The distress anomaly: Where finance theory breaks down Jennifer Anzaldi Thu, 09/22/2022 - 09:54

In This Story

Alexander Philipov

Exceptions may prove the rule, but they must first be explained. That is why finance researchers are drawn to the distress anomaly-- a well-documented phenomenon that challenges the risk-return paradigm in equity markets. Generally, higher-risk investments are expected to yield higher returns than safer, more stable securities. In recent years, however, studies have shown that high-credit-risk securities for companies in distress – i.e. when their already-low credit rating is being downgraded -- realize abnormally low returns compared to non-distressed securities of the same or lower risk.

Academics have proposed a range of rationales for this puzzle. Alexander Philipov, finance area chair and associate professor at �鶹��Ƶ, says they mainly fall into two categories. One is risk-based, stating that the pattern could be explained by time-varying risk (e.g. having low risk in bad times), or by hidden value transfers during bankruptcy, when equity holders could extract value from other stakeholders. The other rationales look at possible investor biases, such as investors having lottery type preferences and chasing after sky-high returns in an unlikely recovery from distress. Other psychological biases, such as tendencies to hold on to losing stocks in the hope they would turn around may also be at play. These biases would be exhibited by retail investors while institutional investors are not likely susceptible to them. As yet, there is no consensus among academics on which rationales, if any, get closest to the truth of the distress anomaly.

Philipov’s recent paper in Review of Finance[1] is the first to show that the distress anomaly extends to corporate bonds. In addition, the anomaly is associated with potentially severe implications for the real economy. The researchers documented this by comparing the stock and bond portfolios of distressed firms with those of other portfolios.

Corporate bonds help evaluate existing rationales because their payoff structure, liquidity, and investor base are different from those of stocks. The study shows that existing rationales for the distress anomaly are inconsistent with the return patterns of corporate bonds. Specifically, rationales involving value transfers from bondholders to stockholders are inconsistent with the data showing that both bonds and stocks of distressed firms are similarly overpriced. Furthermore, the anomaly is more pronounced in market downturns, as is the distressed stocks and bonds’ risk—evidence against the time-varying risk story.

The study also finds the distress anomaly implies real distortions in the real economy because corporate decisions are undertaken based on incorrect asset prices. These suboptimal decisions may include excess investments, and over issuance of debt and equity. “We provide suggestive evidence that real distortions are not only economically significant but also severely understated if measured based on equity mispricing alone (as in previous studies). Adding bonds brings new light to the magnitude of these distortions,” says Philipov.

Based on the new bond evidence, the most coherent rationale for the distress anomaly appears to be underreaction to financial distress, even by the most sophisticated investors. The researchers conclude that the distress anomaly is an unresolved puzzle, deeper than previously thought.

Philipov suggests that future research on the distress risk anomaly may focus on alternative ways of measuring risk or on new advances in behavioral finance, such as cumulative prospect theory which focuses on how investors under- or overestimate probabilities. Clues may also lie in the investment strategies of smart market actors. “If there are many investors with biases, especially biases that you could possibly predict, you should be able to find market players which are exploiting these biases,” Philipov says. Lack of evidence of such profit-taking activity may imply that trading frictions may be too high to take advantage of pricing misalignments. Yet another promising research strategy could be to look beyond the data, into what really goes on behind the scenes at distressed companies. “Maybe what those public investors are losing, there are some players that are gaining from it,” Philipov says. “But we’re not able to observe that.”

He further suggests that interdisciplinary work may be able to tackle questions such as, “Do distressed companies advertise more or less? Do they allocate less for research? What do their operations look like? Are they trying to apply new management techniques or are they quickly losing talent?”

Academics are drawn to find explanations to market phenomena like the distress anomaly where theory breaks down. “We should be trying to explain what’s going on, because that’s where you get new insights: studying corners where things aren’t working as general theory dictates,” Philipov says.

[1] Co-authored by Doron Avramov of ISC Herzliya, Tarun Chrodia of Emory University and Gergana Jostova of George Washington University.

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