The decomposition of dynamic risks a company faces into components associated with various sources of risk, such as financial risks, aggregate economic risks, or industry-specific risk drivers, is of significant relevance in view of risk management and product design, particularly in (life) insurance. Nevertheless, although several decomposition approaches have been proposed, no systematic analysis is available. This paper closes this gap in literature by introducing properties for meaningful risk decompositions and demonstrating that proposed approaches violate at least one of these properties. As an alternative, we propose a novel martingale representation theorem (MRT) decomposition that relies on martingale representation and show that it satisfies all of the properties. We discuss its calculation and present detailed examples illustrating its applicability.

Despite a 20-year trend toward integrated marketing communications, advertisers seldom coordinate television and search advertising campaigns. We find that television advertising for financial services brands increases both the number of related Google searches and searchers' tendency to use branded keywords in place of generic keywords. The elasticity of a brand's total searches with respect to its TV advertising is 0.17, an effect that peaks in the morning. These results suggest that practitioners should account for cross-media effects when planning, executing, and evaluating both television and search advertising campaigns.

Management science and decision science have grown exponentially since midcentury. Two closely-related fields central to this growth are multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT). This paper comments on the history of MCDM and MAUT and discusses topics we believe are important in their continued development and usefulness to management science over the next decade. Our aim is to identify exciting directions and promising areas for future research.

Despite the public’s familiarity with search engines, little existing research empirically investigates the impact of such a search-cost-reduction tool on online market structure. Knowledge scarcity of this question can mainly be attributed to the challenge of accessing detailed data from a cross-website search engine. Using data from the online illegal transaction platform, the Darknet markets, we manage to empirically evaluate the influence of a cross-website search engine (i.e., GRAMS) on the market structure at the vendor and product category levels. The results show that, although the search engine’s entry enhances the overall market performance, the benefit is more significant among leading vendors and popular products, contributing to a more concentrated market. Additional analyses provide empirical evidence that the trustworthiness and the scale-up ability of leading vendors can be the underlying mechanisms for the increased market concentration after the introduction of search engines into Darknet markets. Our study not only contributes to the literature on the dynamics of sales distribution in a multiple-vendor e-commerce market but also provides insights into understanding the operating dynamics of the Darknet markets, which can be helpful for law enforcement policymaking.

The Tax Cuts and Jobs Act of 2017 established a new program called Opportunity Zones (OZs) that reduces or eliminates capital gains taxes on investment in a limited number of low-income Census tracts. We provide a model illustrating how a change in capital taxation affects employment in existing and new establishments. We then use establishment-level data to show that, in its first two years, the OZ designation increased employment growth relative to comparable tracts by between 3.0 and 4.5 percentage points in metropolitan areas. The job growth occurred in multiple industries and persisted into 2021 rather than quickly disappearing. However, most of the jobs created by the program were likely taken by residents who live outside of the designated tracts, consistent with only 5% of U.S. residents working in the same Census tract as the one in which they live.

The topic of learning to solve optimization problems has received interest from both the operations research and machine learning communities. In this paper, we combine ideas from both fields to address the problem of learning to generate decisions to instances of optimization problems with potentially nonlinear or nonconvex constraints where the feasible set varies with contextual features. We propose a novel framework for training a generative model to produce provably optimal decisions by combining interior point methods and adversarial learning, which we further embed within an iterative data generation algorithm. To this end, we first train a classifier to learn feasibility and then train the generative model to produce optimal decisions to an optimization problem using the classifier as a regularizer. We prove that decisions generated by our model satisfy in-sample and out-of-sample optimality guarantees. Furthermore, the learning models are embedded in an active learning loop in which synthetic instances are iteratively added to the training data; this allows us to progressively generate provably tighter optimal decisions. We investigate case studies in portfolio optimization and personalized treatment design, demonstrating that our approach yields advantages over predict-then-optimize and supervised deep learning techniques, respectively. In particular, our framework is more robust to parameter estimation error compared with the predict-then-optimize paradigm and can better adapt to domain shift as compared with supervised learning models.

Airfares evolve dynamically, giving rise to a so-called price path. This price path is controlled via two levers: (i) a fare ladder, which defines a set of airfares before the selling season, and (ii) revenue management algorithms, which control how fares evolve along the ladder during the season. We hypothesize that the current policies to control both levers—which do not account for quality differences between competing airlines—give rise to an inefficient price path and, accordingly, a loss of potential revenue. We substantiate this hypothesis via a field experiment. By partnering with an airline, we introduced quality considerations in the design of fare ladders, across 5,000 itineraries, to show that current ladder-design policies indeed lead to a suboptimal price path. We also show that this inefficiency can be mitigated by incorporating quality differences between competing airlines. This creates a smoother (and more profitable) price path.

We investigate the effect of auditing on promoting exports for private firms in emerging markets. Using a sample of private firms from 125 countries between 2006 and 2015, we show that firms that have their financial statements audited have more exports than firms that do not have their financial statements audited. To infer causality, we employ a regression discontinuity design (RDD). Using the discontinuity around the mandatory financial audit threshold, we find that firms slightly above the threshold have more exports than do firms that are slightly below the threshold. We also exploit the countries with exogenous regulation shocks to the mandatory audits. Using the difference-in-differences (DiD) design, we find that firms that are exempted from mandatory audits have less exports subsequent to the regulation change. Further analyses reveal that the effect of auditing is more pronounced in countries with higher audit quality and for firms with limited alternative information. Our findings suggest that the auditing function promotes exports—an important economic consequence for the global economic development.

We offer a new anchoring explanation for the ex-day abnormal returns of stock distributions, including stock dividend distributions, splits, and reverse splits. We propose that investors tend to anchor on cum-day prices in valuating ex-distribution stocks, resulting in a positive association between ex-day returns and adjustment factors. We find that this positive return-factor relation exists for all three types of stock distributions and in both the pre- and post-decimalization periods. Furthermore, we find that this positive return-factor relation is more pronounced among events that are more subject to investors’ anchoring propensity, featured by less investor attention, greater arbitrage difficulty, greater valuation uncertainty, less investor sophistication, and higher market sentiment. Last, using brokerage account data, we show that stocks that are traded by investors with more investment experience demonstrate a weaker return-factor relation. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2843 .

Resource pooling strategies have been widely used in industry to match supply with demand. However, effective implementation of these strategies can be challenging. Firms need to integrate the heterogeneous service level requirements of different customers into the pooling model and allocate the resources (inventory or capacity) appropriately in the most effective manner. The traditional analysis of inventory pooling, for instance, considers the performance metric in a centralized system and does not address the associated issue of inventory allocation. Using Blackwell’s Approachability Theorem, we derive a set of necessary and sufficient conditions to relate the fill rate requirement of each customer to the resources needed in the system. This provides a new approach to studying the value of resource pooling in a system with differentiated service requirements. Furthermore, we show that with “allocation flexibility,” the amount of safety stock needed in a system with independent and identically distributed demands does not grow with the number of customers but instead diminishes to zero and eventually becomes negative as the number of customers grows sufficiently large. This surprising result holds for all demand distributions with bounded first and second moments.