Fundamental Tradeoff Research Articles

Aging-related cerebral microvascular changes visualized using ultrasound localization microscopy in the living mouse

Aging-related cognitive decline is an emerging health crisis; however, no established unifying mechanism has been identified for the cognitive impairments seen in an aging population. A vascular hypothesis of cognitive decline has been proposed but is difficult to test given the requirement of high-fidelity microvascular imaging resolution with a broad and deep brain imaging field of view, which is restricted by the fundamental trade-off of imaging penetration depth and resolution. Super-resolution ultrasound localization microscopy (ULM) offers a potential solution by exploiting circulating microbubbles to achieve a vascular resolution approaching the capillary scale without sacrificing imaging depth. In this report, we apply ULM imaging to a mouse model of aging and quantify differences in cerebral vascularity, blood velocity, and vessel tortuosity across several brain regions. We found significant decreases in blood velocity, and significant increases in vascular tortuosity, across all brain regions in the aged cohort, and significant decreases in blood volume in the cerebral cortex. These data provide the first-ever ULM measurements of subcortical microvascular dynamics in vivo within the context of the aging brain and reveal that aging has a major impact on these measurements.

Deep imaging flow cytometry.

Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications by virtue of its ability to image single cells in a high-throughput manner. However, there remains a challenge posed by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present deep-learning-enhanced imaging flow cytometry (dIFC) that circumvents this trade-off by implementing an image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) flow cytometry platform, enabling higher throughput without sacrificing sensitivity and spatial resolution. A key component of dIFC is a high-resolution (HR) image generator that synthesizes "virtual" HR images from the corresponding low-resolution (LR) images acquired with a low-magnification lens (10×/0.4-NA). For IFC, a low-magnification lens is favorable because of reduced image blur of cells flowing at a higher speed, which allows higher throughput. We trained and developed the HR image generator with an architecture containing two generative adversarial networks (GANs). Furthermore, we developed dIFC as a method by combining the trained generator and IFC. We characterized dIFC using Chlamydomonas reinhardtii cell images, fluorescence in situ hybridization (FISH) images of Jurkat cells, and Saccharomyces cerevisiae (budding yeast) cell images, showing high similarities of dIFC images to images obtained with a high-magnification lens (40×/0.95-NA), at a high flow speed of 2 m s-1. We lastly employed dIFC to show enhancements in the accuracy of FISH-spot counting and neck-width measurement of budding yeast cells. These results pave the way for statistical analysis of cells with high-dimensional spatial information.

Sparse Control Synthesis for Uncertain Responsive Loads With Stochastic Stability Guarantees

Recent studies have demonstrated the potential of flexible loads in providing frequency response services. However, uncertainty and variability in various weather-related and end-use behavioral factors often affect the demand-side control performance. This work addresses this problem with the design of a demand-side control to achieve frequency response under load uncertainties. Our approach involves modeling the load uncertainties via stochastic processes that appear as both multiplicative and additive to the system states in closed-loop power system dynamics. Extending the recently developed mean square exponential stability (MSES) results for stochastic systems, we formulate multi-objective linear matrix inequality (LMI)-based optimal control synthesis problems to not only guarantee stochastic stability, but also promote sparsity, enhance closed-loop transient performance, and maximize allowable uncertainties. The fundamental trade-off between the maximum allowable ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">critical</i> ) uncertainty levels and the optimal stochastic stabilizing control efforts is established. Moreover, the sparse control synthesis problem is generalized to the realistic power systems scenario in which only partial-state measurements are available. Detailed numerical studies are carried out on IEEE 39-bus system to demonstrate the closed-loop stochastic stabilizing performance of the sparse controllers in enhancing frequency response under load uncertainties; as well as illustrate the fundamental trade-off between the allowable uncertainties and optimal control efforts.

AoD-Adaptive Channel Feedback for FDD Massive MIMO Systems With Multiple-Antenna Users

In this paper, we propose an efficient feedback scheme for an angle of departure (AoD) based channel estimation in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems with multiple antennas at the users. The channel feedback scheme is based on zero-forcing block diagonalization (BD) and it is proposed for two distinct design cases; in case I, the number of streams intended for a user equals the number of antennas at that user; in case II, the number of streams is less than the number of receive antennas. Case I is applicable in scenarios where high data rate requirements are needed as it transmits data symbols over all of the available degrees of freedom of the system. Diversely, case II is applicable when reliability is a priority in the system as it uses the additional <i>receive</i> antennas at the user to achieve spatial diversity to enhance the link performance. The proposed scheme is analyzed for the two cases by quantifying the downlink rate gap from the case of perfect channel state information (CSI). Moreover, we design structured feedback codebooks based on optimal subspace packing in the Grassmannian manifold and show that these codes achieve close performance to the perfect CSI case. Additionally, a vector quantization scheme is proposed to quantize the user&#x2019;s channel matrix when optimal power allocation across multiple streams is adopted in the low signal-to-noise ratio (SNR) region. The feedback codebooks are based on optimal line packing in the Grassmannian manifold, where every vector of the user&#x2019;s channel matrix is quantized and sent to the BaseStation. The results demonstrate a fundamental trade-off between vector quantization, with power optimization across the data streams, and subspace quantization. Specifically, vector quantization codebooks outperform subspace-based codebooks in the low SNR region, while the situation is reversed in the high SNR region.

Information-Theoretic Secret Sharing From Correlated Gaussian Random Variables and Public Communication

In this paper, we study an information-theoretic secret sharing problem, where a dealer distributes shares of a secret among a set of participants under the following constraints: (i) authorized sets of users can recover the secret by pooling their shares, and (ii) non-authorized sets of colluding users cannot learn any information about the secret. We assume that the dealer and participants observe the realizations of correlated Gaussian random variables and that the dealer can communicate with participants through a one-way, authenticated, rate-limited, and public channel. Unlike traditional secret sharing protocols, in our setting, no perfectly secure channel is needed between the dealer and the participants. Our main result is a closed-form characterization of the fundamental trade-off between secret rate and public communication rate.

Proactive Eavesdropping With Jamming Power Allocation in Training-Based Suspicious Communications

This letter studies proactive eavesdropping with one legitimate monitor (E) in the classical single-hop suspicious communication. Specifically, unlike all previous works that ignored the suspicious channel training phase and just considered the jamming power optimization of E in the suspicious data transmission phase to facilitate eavesdropping, this letter advances the research by comprehensively investigating the jamming power allocation of E in both phases, with the purpose of maximizing its eavesdropping success probability. Under this setup, we first derive an exact expression of the objective and reveal the existence of a fundamental trade-off in deciding the jamming power allocation, for which a simple one-dimensional search is employed to find the optimal solution. To simplify the analysis, we further derive a tight approximation of the objective and then develop a very fast alternating optimization algorithm to find the sub-optimal jamming power allocation. Moreover, we extend our analysis to the case where the channel state information is available at the suspicious transmitter via channel feedback. Simulation results demonstrate the effectiveness of our proposed scheme compared to competitive benchmarks.

LIDAR-Assisted Channel Modelling for LiFi in Realistic Indoor Scenarios

We present a new, accurate, low complexity channel modelling methodology for LiFi in realistic indoor scenarios. A LIDAR scanner is used to capture the 3D environment in which the LiFi system is to be deployed. Next, the generated 3D point cloud data is pre-processed to determine the reflectance parameters of the walls and objects in the room. This is easier and more realistic than the manual definition of the environment, which is the current state of the art. As an additional innovation, the complexity of the channel modeling is reduced by i) modeling the line-of-sight and initial reflections precisely in the frequency-domain and ii) using a well-established analytical model based on the integrating sphere for all higher-order diffuse reflections. All steps together yield a substantially simplified channel modelling approach and model the links between multiple optical frontends and multiple mobile devices realistically. As a validation of our new approach, we compare measurements and simulations in two indoor scenarios: an empty room and a conference room with furniture. Simulations and measurements show excellent agreement with a mean square error below 3 percent. Moreover, we evaluate the performance of a distributed multiuser multiple-input multiple-output (MIMO) link and found excellent agreement between the model and measurements. Finally, we discuss the fundamental trade-off between complexity and model error, which depends on the scenario.

Ultra-Reliable and Low-Latency Wireless Communications in the High SNR Regime: A Cross-Layer Tradeoff

Ultra-Reliable and Low-Latency Communications (URLLC) has attracted considerable attention because of its potential applications in factory automation, automated driving, and telesurgery anticipated for the era of the sixth-Generation (6G) networks. In URLLC with random channel gains and a hard delay constraint, the scheduling of backlogged queues and finite blocklength coding in the physical layer will make it very challenging to specify its performance limits. In this paper, we focus our attention on the asymptotic cross-layer analysis of URLLC when the Signal-to-Noise Ratio (SNR) is sufficiently high. More specifically, we find that a fundamental tradeoff exists among the service capability, latency, and error probability in the high SNR regime, which is characterized by a gain conservation equation. The main result of this work reveals that the sum of our defined service rate gain, real-time gain, and reliability gain is equal to one under the optimal policy. Numerical simulations are also exploited to validate that the derived gain conservation equation holds even with bounded random arrival.

Quantifying the trade-off between stiffness and permeability in hydrogels.

Hydrogels have a distinct combination of mechanical and water-transport behaviors. As hydrogels stiffen when they de-swell, they become less permeable. Here, we combine de Gennes' semi-dilute polymer theory with the Kozeny-Carman equation to develop a simple, succinct scaling law describing the relationship between mechanical stiffness and hydraulic permeability where permeability scales with stiffness to the -8/9 power. We find a remarkably close agreement between the scaling law and experimental results across four different polymer families with varied crosslinkings. This inverse relationship establishes a fundamental trade-off between permeability and stiffness.

Enterprise Risk Management (ERM) Practices to Achieve Long Term and Sustainable Organization’s Goals: Case of Institut Teknologi Bandung (ITB)

Every choice made in the pursuit of objectives has its risks. From day-to-day operational decisions to the fundamental trade-offs in the boardroom, dealing with uncertainty in these choices is a part of the organizational lives. A strategy is nothing more than a commitment to a set of coherent, mutually reinforcing policies or behaviours aimed at achieving a specific competitive goal. In order to ensure the implementation of efforts and the allocation of resources to achieve strategic goals, top management should conduct integrated risk management practices to all activities/initiatives of the organization’s management, both individually and collectively. Risk management is an intrinsic part of business planning and decision making. No direction is taken without looking at the potential risks and comparing them against the organization’s risk appetite. This paper aims to research in general the practice of enterprise risk management within Institut Teknologi Bandung (ITB) as a well-known and public-state-owned university in Indonesia. This research concludes that the enterprise risk management implementation is not fully implemented yet within ITB as an enterprise. Almost all respondents agree that the implementation of enterprise risk management has a positive and significant influence on the organization’s objectives achievement. Improving university performance overall will require an effective enterprise risk management practice. Author highly recommends ITB to adopt risk management practice based on ISO-31000 standard, and it can be combined with other risk management standards available nowadays if necessary. ITB needs to start the implementation at the soonest as possible, in order to maintain its strategic position as a top university in Indonesia, increase its competitive advantages to compete in the global scale, and at the same time achieving its vision and mission in a long-term and sustainable manner.

Trade-offs and design principles in the spatial organization of catalytic particles

Catalytic particles are spatially organized in a number of biological systems across different length scales, from enzyme complexes to metabolically coupled cells. Despite operating on different scales, these systems all feature localized reactions involving partially hindered diffusive transport, which is determined by the collective arrangement of the catalysts. Yet it remains largely unexplored how different arrangements affect the interplay between the reaction and transport dynamics, which ultimately determines the flux through the reaction pathway. Here we show that two fundamental trade-offs arise, the first between efficient inter-catalyst transport and the depletion of substrate, and the second between steric confinement of intermediate products and the accessibility of catalysts to substrate. We use a model reaction pathway to characterize the general design principles for the arrangement of catalysts that emerge from the interplay of these trade-offs. We find that the question of optimal catalyst arrangements generalizes the well-known Thomson problem of electrostatics. Comparing ways of arranging catalysts in living systems reveals that the reaction- and diffusion-limited regimes require different strategies. The formalism generalizes the Thomson problem of optimizing the configuration of electrons on a sphere.

Effective Online Order Acceptance Policies for Omnichannel Fulfillment

Problem definition: Omnichannel retailing has led to the use of traditional stores as fulfillment centers for online orders. Omnichannel fulfillment problems have two components: (1) accepting a certain number of online orders prior to seeing store demands and (2) satisfying (or filling) some of these accepted online demands as efficiently as possible with any leftover inventory after store demands have been met. Hence, there is a fundamental trade-off between store cancellations of accepted online orders and potentially increased profits because of more acceptances of online orders. We study this joint problem of online order acceptance and fulfillment (including cancellations) to minimize total costs, including shipping charges and cancellation penalties in single-period and limited multiperiod settings. Academic/practical relevance: Despite the growing importance of omnichannel fulfillment via online orders, our work provides the first study incorporating cancellation penalties along with fulfillment costs. Methodology: We build a two-stage stochastic model. In the first stage, the retailer sets a policy specifying which online orders it will accept. The second stage represents the process of fulfilling online orders after the uncertain quantities of in-store purchases are revealed. We analyze threshold policies that accept online orders as long as the inventories are above a global threshold, a local threshold per region, or a hybrid. Results: For a single period, total costs are unimodal as a function of the global threshold and unimodal as a function of a single local threshold holding all other local thresholds at constant values, motivating a gradient search algorithm. Reformulating as an appropriate linear program with network flow structure, we estimate the derivative (using infinitesimal perturbation analysis) of the total cost as a function of the thresholds. We validate the performance of the threshold policies empirically using data from a high-end North American retailer. Our two-location experiments demonstrate that local thresholds perform better than global thresholds in a wide variety of settings. Conversely, in a narrow region with negatively correlated online demand between locations and very low shipping costs, global threshold outperforms local thresholds. A hybrid policy only marginally improves on the better of the two. In multiple periods, we study one- and two-location models and provide insights into effective solution methods for the general case. Managerial implications: Our methods provide effective algorithms to manage fulfillment costs for online orders, demonstrating a significant reduction over policies that treat each location separately and reflecting the significant advantage of incorporating shipping in computing thresholds. Numerical studies provide insights as to why local thresholds perform well in a wide variety of situations.

A multiparametric volumetric quantitative ultrasound imaging technique for soft tissue characterization.

Quantitative ultrasound (QUS) offers a non-invasive and objective way to quantify tissue health. We recently presented a spatially adaptive regularization method for reconstruction of a single QUS parameter, limited to a two dimensional region. That proof-of-concept study showed that regularization using homogeneity prior improves the fundamental precision-resolution trade-off in QUS estimation. Based on the weighted regularization scheme, we now present a multiparametric 3D weighted QUS (3D QUS) method, involving the reconstruction of three QUS parameters: attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC) and effective scatterer diameter (ESD). With the phantom studies, we demonstrate that our proposed method accurately reconstructs QUS parameters, resulting in high reconstruction contrast and therefore improved diagnostic utility. Additionally, the proposed method offers the ability to analyze the spatial distribution of QUS parameters in 3D, which allows for superior tissue characterization. We apply a three-dimensional total variation regularization method for the volumetric QUS reconstruction. The 3D regularization involving N planes results in a high QUS estimation precision, with an improvement of standard deviation over the theoretical 1/N rate achievable by compounding N independent realizations. In the in vivo liver study, we demonstrate the advantage of adopting a multiparametric approach over the single parametric counterpart, where a simple quadratic discriminant classifier using feature combination of three QUS parameters was able to attain a perfect classification performance to distinguish between normal and fatty liver cases.

Optimal Resource Allocation and Task Segmentation in IoT Enabled Mobile Edge Cloud

Recent development toward innovative applications and technologies like self-driving, augmented reality, smart cities, and various other applications leads to excessive growth in the number of devices. These devices have finite computation resources and cannot handle the applications that require extensive computation with minimal delay. To overcome this, the mobile edge cloud (MEC) emerges as a practical solution that allows devices to offload their extensive computation to MEC located in their vicinity; this will lead to succeeding the arduous delay of the millisecond scale: requirement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$5^{th}$</tex-math></inline-formula> generation communication system. This work examines the convex optimization problem. The objective is to minimize the task duration by optimal allocation of the resources like local and edge computational capabilities, transmission power, and optimal task segmentation. For optimal allocation of resources, an algorithm name <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> stimation of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> ptimal <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> esource <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> llocator <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(EORA)</b> is designed to optimize the function by keeping track of statistics of each candidate of the population. Using EORA, a comparative analysis of the hybrid approach (partial offloading) and edge computation only is performed. Results reveal the fundamental trade-off between both of these models. Simultaneously, the impact of devices’ computational capability, data volume, and computational cycles requirement on task segmentation is analyzed. Simulation results demonstrate that the hybrid approach: partial offloading scheme reduces the task's computation time and outperforms edge computing only.

Visualising the nutritional performance landscapes for the black soldier fly,Hermetia illucens (Diptera: Stratiomyidae)

The black soldier fly (BSF),Hermetia illucens (Diptera: Stratiomyidae), has become one of the most economically important insects that can be a sustainable replacement for traditional feed for swine, poultry, and aquaculture. Despite its emerging importance, studies examining the nutritional requirement of BSF have been scarce. Here we used nutritional geometry approach to determine the separate and combined effects of dietary protein and carbohydrate content on multiple life-history traits and body composition linked to fitness in BSF. BSF larvae were reared on one of 32 chemically defined diets that varied in protein-to-carbohydrate ratio (P:C=1:16, 1:8, 1:4, 1:2, 1:1, 2:1, 4:1, or 8:1) and in protein plus carbohydrate concentration (P+C=60, 120, 180, or 240 g/l). Females developed more slowly into larger adults than males, but two sexes responded to dietary variation in protein and carbohydrate in a similar manner. The topography of the nutritional performance landscapes mapped for all measured traits differed significantly from one another, with the optimal P:C ratio being 1:1.71 for preadult survivorship, 1:1.56 – 1:1.11 for development time, 1:3.36 – 1:3.16 for body weight at adult emergence, and 1:1.83 – 1:1.40 for growth rate. Significant divergence in nutritional optima among these components of fitness indicates that the occurrence of fundamental life-history trade-off can be mediated by macronutrient intake. The index representing the preadult performance of BSF had a nonlinear relationship with dietary protein, with the peak centred at the intermediate P:C ratios of 1:1.43 – 1:1.37. The optimal P:C ratio was 1:2.12 – 1:1.70 for lean body weight and 1:14.14 – 1:10.82 for lipid content. Our results highlight the importance of the balanced composition of dietary protein and carbohydrate for optimising BSF performance and have implications for enhancing the mass production of this beneficial species.

Engineering Social Learning: Information Design of Time-Locked Sales Campaigns for Online Platforms

Many online platforms offer time-locked sales campaigns, whereby products are sold at fixed prices for prespecified lengths of time. Platforms often display some information about previous customers’ purchase decisions during campaigns. Using a dynamic Bayesian persuasion framework, we study how a revenue-maximizing platform should optimize its information policy for such a setting. We reformulate the platform’s problem equivalently by reducing the dimensionality of its message space and proprietary history. Specifically, three messages suffice: a neutral recommendation that induces a customer to make her purchase decision according to her private signal about the product and a positive (respectively (resp.), negative) recommendation that induces her to purchase (resp., not purchase) by ignoring her signal. The platform’s proprietary history can be represented by the net purchase position, a single-dimensional summary statistic that computes the cumulative difference between purchases and nonpurchases made by customers having received the neutral recommendation. Subsequently, we establish structural properties of the optimal policy and uncover the platform’s fundamental trade-off: long-term information (and revenue) generation versus short-term revenue extraction. Further, we propose and optimize over a class of heuristic policies. The optimal heuristic policy provides only neutral recommendations up to a cutoff customer and provides only positive or negative recommendations afterward, with the recommendation being positive if and only if the net purchase position after the cutoff customer exceeds a threshold. This policy is easy to implement and numerically shown to perform well. Finally, we demonstrate the generality of our methodology and the robustness of our findings by relaxing some informational assumptions. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

Spatial regulation of cell motility and its fitness effect in a surface-attached bacterial community.

On a surface, microorganisms grow into a multi-cellular community. When a community becomes densely populated, cells migrate away to expand the community's territory. How microorganisms regulate surface motility to optimize expansion remains poorly understood. Here, we characterized surface motility of Proteus mirabilis. P. mirabilis is well known for its ability to expand its colony rapidly on a surface. Cursory visual inspection of an expanding colony suggests partial migration, i.e., one fraction of a population migrates while the other is sessile. Quantitative microscopic imaging shows that this migration pattern is determined by spatially inhomogeneous regulation of cell motility. Further analyses reveal that this spatial regulation is mediated by the Rcs system, which represses the expression of the motility regulator (FlhDC) in a nutrient-dependent manner. Alleviating this repression increases the colony expansion speed but results in a rapid drop in the number of viable cells, lowering population fitness. These findings collectively demonstrate how Rcs regulates cell motility dynamically to increase the fitness of an expanding bacterial population, illustrating a fundamental trade-off underlying bacterial colonization of a surface.

Anonymous Stochastic Routing

We propose and analyze a recipient-anonymous stochastic routing model to study a fundamental trade-off between anonymity and routing delay. An agent wants to quickly reach a goal vertex in a network through a sequence of routing actions, whereas an overseeing adversary observes the agent’s entire trajectory and tries to identify the agent’s goal among those vertices traversed. We are interested in understanding the probability that the adversary can correctly identify the agent’s goal (anonymity) as a function of the time it takes the agent to reach it (delay). A key feature of our model is the presence of intrinsic uncertainty in the environment, so that each of the agent’s intended steps is subject to random perturbation and thus may not materialize as planned. Using large-network asymptotics, our main results provide near-optimal characterization of the anonymity–delay trade-off under a number of network topologies. Our main technical contributions are centered on a new class of “noise-harnessing” routing strategies that adaptively combine intrinsic uncertainty from the environment with additional artificial randomization to achieve provably efficient obfuscation.

EDoF-ToF: extended depth of field time-of-flight imaging.

Conventional continuous-wave amplitude-modulated time-of-flight (CWAM ToF) cameras suffer from a fundamental trade-off between light throughput and depth of field (DoF): a larger lens aperture allows more light collection but suffers from significantly lower DoF. However, both high light throughput, which increases signal-to-noise ratio, and a wide DoF, which enlarges the system's applicable depth range, are valuable for CWAM ToF applications. In this work, we propose EDoF-ToF, an algorithmic method to extend the DoF of large-aperture CWAM ToF cameras by using a neural network to deblur objects outside of the lens's narrow focal region and thus produce an all-in-focus measurement. A key component of our work is the proposed large-aperture ToF training data simulator, which models the depth-dependent blurs and partial occlusions caused by such apertures. Contrary to conventional image deblurring where the blur model is typically linear, ToF depth maps are nonlinear functions of scene intensities, resulting in a nonlinear blur model that we also derive for our simulator. Unlike extended DoF for conventional photography where depth information needs to be encoded (or made depth-invariant) using additional hardware (phase masks, focal sweeping, etc.), ToF sensor measurements naturally encode depth information, allowing a completely software solution to extended DoF. We experimentally demonstrate EDoF-ToF increasing the DoF of a conventional ToF system by 3.6 ×, effectively achieving the DoF of a smaller lens aperture that allows 22.1 × less light. Ultimately, EDoF-ToF enables CWAM ToF cameras to enjoy the benefits of both high light throughput and a wide DoF.

Is Corporate Social Responsibility investing a free lunch? The relationship between ESG, tail risk, and upside potential of stocks before and during the COVID-19 crisis

Did Corporate Social Responsibility investing benefit shareholders during the COVID-19 pandemic crisis? Distinguishing between downside tail risk and upside reward potential of stock returns, we provide evidence from 5,073 stocks listed on stock markets in ten countries. The findings suggest that better ESG ratings are associated with lower downside risk, but also with lower upside return potential. Thus, ESG ratings helped investors to reduce their risk exposure to the market turmoil caused by the pandemic, while maintaining the fundamental trade-off between risk and reward.