Publications

Business and Sustainability: Great Jobs and Current Trends

Anonymous — Fri, 23 Feb 2024 07:00:00 +0000

Business and Sustainability: Great Jobs and Current Trends Anonymous (not verified) Fri, 02/23/2024 - 00:00

Wondering how to set yourself apart and land a great job in the future? Top finance and consulting employers share how understanding sustainability – including environmental and social risks and opportunities - is key to success in every role, along with developing strong business skills. Join CESR for this panel at the Leeds Professionalism Summit.

Speakers Include:

Ramsay Huntley, Senior Advisor, Sustainable Finance at Wells Fargo

Tarek Haffar, Manger, Deloitte

Meg Parker Young, VP of ESG Strategy at Fannie Mae

Amy Halvorson (BusAdmin '10), Enterprise Sustainability Lead at Starbucks

Event Details:

2/23
Session 2 (10:10 – 11am) and Session 5 (2:10 – 3pm)
Leeds Business School

Registration:

Just for sophmores, sign up via the offical summit registration form.

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Tainted Executives as Outside Directors

Anonymous — Wed, 01 Nov 2023 06:00:00 +0000

Tainted Executives as Outside Directors Anonymous (not verified) Wed, 11/01/2023 - 00:00

Baer, Leah; Ertimur, Yonca; Zhang, Jingjing. 2023. The Accounting Review. 98 (7): 33–59.

We examine outside board appointments of executives allegedly involved in governance failures—“tainted” executives—to shed light on appointing firms’ underlying motivations. Less attractive firms and those with greater advising needs are more likely to appoint tainted executives to their boards than other firms are. Tainted appointees are less likely to be placed on the nominating and governance committees than nontainted appointees. Tainted appointees have similar or better skill sets compared with nontainted appointees. Firms that appoint tainted executives to their boards display an improvement in operating performance in the postappointment period relative to the preappointment period and relative to a matched control sample. We do not find evidence of poor monitoring outcomes for these firms. Overall, our evidence suggests that board needs, not a conspicuous attempt to weaken monitoring, drive the appointment of tainted executives to boards.

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Product-based Approximate Linear Programs for Network Revenue Management

Anonymous — Mon, 22 Aug 2022 17:20:20 +0000

Product-based Approximate Linear Programs for Network Revenue Management Anonymous (not verified) Mon, 08/22/2022 - 11:20 Dan Zhang

Rui Zhang, Saied Samiedaluie, and Dan Zhang. 2022. Product-based Approximate Linear Programs for Network Revenue Management. Operations Research, 70(5):2837-2850.

The approximate linear programming approach has received significant attention in the network revenue management literature. A popular approximation in the existing literature is separable piecewise linear (SPL) approximation, which estimates the value of each unit of each resource over time. SPL approximation can be used to construct resource-based bid-price policies. In this paper, we propose a product-based SPL approximation. The coefficients of the product-based SPL approximation can be interpreted as each product’s revenue contribution to the value of each unit of each resource in a given period. We show that the resulting approximate linear program (ALP) admits compact reformulations, like its resource-based counterpart. Furthermore, the new approximation allows us to derive a set of valid inequalities to (i) speed up the computation and (ii) select optimal solutions to construct more effective policies. We conduct an extensive numerical study to illustrate our results. In a set of 192 problem instances, bid-price policies based on the new approximation generate higher expected revenues than resource-based bid-price policies, with an average revenue lift of 0.72% and a maximum revenue lift of 5.3%. In addition, the new approximation can be solved 1.42 times faster than the resource-based approximation and shows better numerical stability. The valid inequalities derived from the new approximation further improve the computational performance and are critical for achieving additional gains in the expected revenue. The policy performance is competitive compared with the dynamic programming decomposition method, which is the strongest heuristic known in the literature.

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The need to validate exogenous shocks: Shareholder derivative litigation, universal demand laws and firm behavior

Anonymous — Wed, 23 Feb 2022 07:00:00 +0000

The need to validate exogenous shocks: Shareholder derivative litigation, universal demand laws and firm behavior Anonymous (not verified) Wed, 02/23/2022 - 00:00

Dain Donelson, Laura Kettell, John McInnis and Sara Toynbee. Journal of Accounting and Economics, 2022, vol. 73, issue 1

Several recent studies argue that the adoption of universal demand (UD) laws represent an exogenous decline in litigation risk by increasing the procedural hurdles associated with shareholder derivative litigation. This study examines how UD laws affect the incidence of derivative litigation risk and related decisions. We show that the adoption of UD laws had no meaningful impact on derivative litigation from 1996 to 2015. We also find no evidence that UD laws affect aggressive accounting, voluntary disclosure, executive compensation, or corporate governance decisions. Collectively, our findings cast doubt on the validity of using UD laws as an exogenous shock to litigation risk.

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Opaque Selling of Multiple Substitutable Products with Finite Inventories

Anonymous — Sat, 27 Nov 2021 17:22:49 +0000

Opaque Selling of Multiple Substitutable Products with Finite Inventories Anonymous (not verified) Sat, 11/27/2021 - 10:22 Dan Zhang

Qian Liu, Yongbo Xiao, and Dan Zhang. 2022. Opaque Selling of Multiple Substitutable Products with Finite Inventories. Naval Research Logistics, 69(4):529-549.

Opaque selling, in which a seller offers opaque goods (OGs), in addition to physical goods, has been shown to be an effective strategy to segment a market and improve the seller’s profit. This paper studies opaque selling with stochastic demand and fixed initial inventories of multiple products, where the seller dynamically controls the product offers and determines the product assignment to fulfill the demand for OGs over time. The problem is formulated as a stochastic dynamic program. Due to the curse of dimensionality, we study the fluid control problem that gives a time-based fluid policy and a stationary probabilistic fulfillment strategy. We show that the fluid policy is asymptotically optimal when the arrival rates and initial inventory level are scaled up linearly. Furthermore, we propose a decomposition heuristic based on the corresponding fluid solution. The decomposition heuristic is shown to provide a tighter upper bound than the fluid control problem. Numerical study on a set of test instances illustrates the performance and efficacy of opaque selling.

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An Analysis of "Buy X, Get One Free" Reward Programs

Anonymous — Fri, 17 Sep 2021 07:00:00 +0000

An Analysis of "Buy X, Get One Free" Reward Programs Anonymous (not verified) Fri, 09/17/2021 - 01:00 Dan Zhang

Yan Liu, Yacheng Sun, and Dan Zhang. 2021. An Analysis of "Buy X, Get One Free" Reward Programs. Operations Research, 69(6): 1823-1841.

We study the effects of redemption hurdles on reward program members’ decision-making and firm profitability. We focus on the popular “Buy X, Get One Free” (BXGO) programs, which set a redemption threshold (X), and possibly an expiration term for the reward. In our model, forward-looking consumers interact with a monopolistic firm, and strategically make purchase and redemption decisions over an infinite time horizon. Our analysis leads to the following results. First, a consumer’s purchase utility and purchase probability increase as her reward point inventory approaches the redemption threshold or expiration. These patterns are consistent with the “point pressure” phenomenon documented in the empirical literature. Second, a redemption threshold alone cannot improve the firm’s profit, unless it is coupled with a finite expiration term, or a positive transaction utility that consumers may derive from reward redemption. Third, setting the optimal redemption threshold requires the program to strike a balance between the effective price paid by consumers and their purchase probabilities. These results have rich managerial implications for effectively designing reward programs.

A Harvard Business Review article summarizing the findings of this paper and Sun and Zhang (2019) can be found here.

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Closing the Gap: A Learning Algorithm for Lost-Sales Inventory Systems with Lead Times

Anonymous — Tue, 27 Apr 2021 14:37:12 +0000

Closing the Gap: A Learning Algorithm for Lost-Sales Inventory Systems with Lead Times Anonymous (not verified) Tue, 04/27/2021 - 08:37

We consider a periodic-review, single-product inventory system with lost sales and positive lead times under censored demand. In contrast to the classical inventory literature, we assume the firm does not know the demand distribution a priori and makes an adaptive inventory-ordering decision in each period based only on the past sales (censored demand) data. The standard performance measure is regret, which is the cost difference between a learning algorithm and the clairvoyant (full-information) benchmark. When the benchmark is chosen to be the (full-information) optimal base-stock policy, Huh et al. [Huh WT, Janakiraman G, Muckstadt JA, Rusmevichientong P (2009a) An adaptive algorithm for finding the optimal base-stock policy in lost sales inventory systems with censored demand. Math. Oper. Res. 34(2):397–416.] developed a nonparametric learning algorithm with a cubic-root convergence rate on regret. An important open question is whether there exists a nonparametric learning algorithm whose regret rate matches the theoretical lower bound of any learning algorithms. In this work, we provide an affirmative answer to this question. More precisely, we propose a new nonparametric algorithm termed the simulated cycle-update policy and establish a square-root convergence rate on regret, which is proven to be the lower bound of any learning algorithm. Our algorithm uses a random cycle-updating rule based on an auxiliary simulated system running in parallel and also involves two new concepts, namely the withheld on-hand inventory and the double-phase cycle gradient estimation. The techniques developed are effective for learning a stochastic system with complex system dynamics and lasting impact of decisions.

Zhang, H., Chao, X., & Shi, C. (2020). Closing the gap: A learning algorithm for lost-sales inventory systems with lead times. Management Science, 66(5), 1962-1980. https://doi.org/10.1287/mnsc.2019.3288

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How Do Foreclosures Exacerbate Housing Downturns?

Anonymous — Tue, 27 Apr 2021 14:31:33 +0000

How Do Foreclosures Exacerbate Housing Downturns? Anonymous (not verified) Tue, 04/27/2021 - 08:31

This article uses a structural model to show that foreclosures played a crucial role in exacerbating the recent housing bust and to analyse foreclosure mitigation policy. We consider a dynamic search model in which foreclosures freeze the market for non-foreclosures and reduce price and sales volume by eroding lender equity, destroying the credit of potential buyers, and making buyers more selective. These effects cause price-default spirals that amplify an initial shock and help the model fit both national and cross-sectional moments better than a model without foreclosure. When calibrated to the recent bust, the model reveals that the amplification generated by foreclosures is significant: ruined credit and choosey buyers account for 25.4% of the total decline in non-distressed prices and lender losses account for an additional 22.6%. For policy, we find that principal reduction is less cost-effective than lender equity injections or introducing a single seller that holds foreclosures off the market until demand rebounds. We also show that policies that slow down the pace of foreclosures can be counterproductive.

Guren, A. M., & McQuade, T. J. (2020). How do foreclosures exacerbate housing downturns? The Review of Economic Studies, 87(3), 1331-1364. https://doi.org/10.1093/restud/rdaa001

Guren, A. M., & McQuade, T. J. (2020). How do foreclosures exacerbate housing downturns? The Review of Economic Studies, 87(3), 1331-1364. https://doi.org/10.1093/restud/rdaa001

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Implications of Non-GAAP earnings for real activities and accounting choices

Anonymous — Tue, 27 Apr 2021 14:28:29 +0000

Implications of Non-GAAP earnings for real activities and accounting choices Anonymous (not verified) Tue, 04/27/2021 - 08:28

Managers almost always define non-GAAP earnings to exclude the effects of acquisition and restructuring expenses, the amortization of intangibles, and impairments. I find that managers with a history of reporting non-GAAP earnings act as if they place lower weight on these excluded expenses when making real activities and accounting choices. They pursue more and larger acquisitions, have higher total capital investment, are more likely to restructure, and are more likely to recognize discretionary impairments. In a difference-in-differences setting, I find that non-GAAP reporting firms are less likely to alter their restructuring activities following a significant change in accounting rules for restructuring expense recognition. Finally, in supplementary analyses, I find that non-GAAP-reporting firms tend to repeat these real activities and accounting choices year-after-year, resulting in more persistent special-item expenses.

Laurion, H. (2020). Implications of non-GAAP earnings for real activities and accounting choices. Journal of Accounting & Economics, 70(1), 101333. https://doi.org/10.1016/j.jacceco.2020.101333

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Explaining firms' earnings announcement stock returns using FactSet and I/B/E/S data feeds

Anonymous — Tue, 23 Mar 2021 06:00:00 +0000

Explaining firms' earnings announcement stock returns using FactSet and I/B/E/S data feeds Anonymous (not verified) Tue, 03/23/2021 - 00:00

Hand, John R. M.; Laurion, Henry; Lawrence, Alastair; Martin, Nicholas. 2021. Review of Accounting Studies.

Since 2001, the number of financial statement line items forecasted by analysts and managers that I/B/E/S and FactSet capture in their data feeds has soared. Using this new data, we find that 13 item surprises—11 income statement-based and 2 cash flow statement-based analyst and management guidance surprises—reliably explain firms’ signed earnings announcement returns. No balance sheet or expense surprises are significant. The most important surprises are (i) one-quarter-ahead sales guidance surprise, (ii) analyst sales surprise, (iii) annual Street earnings guidance surprise, and (iv) analyst Street earnings surprise. We also find that the adjusted R2s of our multivariate regressions are three times higher than the adjusted R2s of univariate Street earnings surprise regressions, and that the four most important surprises account for approximately half of this increase in explanatory power.

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