Upside Potential Research Articles

An Empirical Assessment of Firm Responses to the COVID-19 Crisis

The COVID-19 pandemic presents an unprecedented crisis environment. To mitigate the negative impact of the COVID-19 crisis or to ensure survival, firms across all industries attempt to adequately respond. Yet, it remains largely unclear how firms respond to the COVID-19 crisis, and how effective these responses are. In this empirical study, we address these issues. In a first step, exploring how firms respond to the COVID-19 crisis, we identify five distinct tactical response types, namely operational, digitalization, financial, supportive, and organizational responses. We also find that responses vary in their scope, as some firms take action on their own, while others collaborate on an inter-organizational level. Eventually, we observe different response angles, manifesting in firms that leverage upside potential during the pandemic and firms that focus on the mitigation of downside risk. In a second step, we measure the effectiveness of the different responses, conducting an event study to estimate the financial value that investors attribute to the announced COVID-19 responses. Our results indicate that firm responses to the COVID-19 crisis are generally associated with an increase in firm value, and that financial and digitalization responses are valued particularly positive. In contrast to solo efforts, collaborative responses are also associated with a higher increase in firm value. Collectively, our empirical findings extend the emerging, yet mainly conceptual, literature on the COVID-19 crisis, as well as the more established general literature on crisis response management.

Preference for experiences: Regulatory focus and the trade-offs between experiential and material purchases.

Consumers often make trade-offs between experiential and material purchases, a choice which has important implications for consumers as well as marketers. The current research explores the effect of regulatory focus on such trade-offs. We find that promotion-driven individuals have a higher preference for experiential purchases than people with a prevention focus. This effect is demonstrated with a set of nine studies that utilize different operationalizations of regulatory focus and purchase type, including seven studies in the main text that use experimental data and two studies in the online Supplemental Materials that use real-world data (Instagram and Google Trends). Due to the variable nature of an experiential purchase, promotion-focused individuals are drawn to its numerous upside potentials, whereas prevention-focused people focus on its many downside potentials. Using both the mediation and moderation approaches, we show that the focus of attention on experiential purchases drives the observed effect. Finally, we demonstrate the evidential value of our findings with a p-curve analysis. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Machine Learning Approach to Benchmarking Operators Performance: A New Perspective Utilizing Factor Contribution Analysis

Summary This study explains how production performance of the multifractured horizontal wells can be divided into two key contributing components: geographical location and completion strategy. Furthermore, we show how to quantify the contribution of these two independent components to production and to understand the variations in key performance drivers across the evaluated field. Being able to differentiate these contributions allows us to compare well performance in a consistent manner and identify potential upside opportunities, such as refracturing candidates, infill well development, and operator benchmarking. Further analysis uses multiple benchmarks to evaluate operator performance and assess how underperforming operators can optimize their completion strategies. We use a novel machine learning approach—a combination of XGBoost and Factor Contribution Analysis (FCA)—that not only allows for fieldwide well evaluations, but also provides a quantifiable contribution of each feature to production. Our approach generates a production prediction model and accounts for the completion parameters and geological information for each well. The final model can be used to either predict future performance of a field/well or to understand reservoir and completion characteristics. This study focuses on the latter and provides an approach to understand the main influencing factors behind well performance as a result of location and completion strategies. Our study is conducted on three major unconventional plays (Haynesville, Eagle Ford, and Bakken), where we demonstrate how different completion features (e.g. lateral length, proppant volume, and fluid volume) affect production data and what we could expect in terms of production should the well have been completed differently. We show how to combine the effect of individual controlling factors (e.g. location, depth, lateral length, proppant volume, fluid volume, and well spacing) to appropriately characterize the performance of each well in terms of two key components: location and completion. This enables us to quantify what portion of the production is a result of rock quality and how much is due to its completion strategy. This technique also allows us to quantify and relate each of these features, and highlight areas with desirable geological features, as well as good candidates for refracturing jobs. Moreover, we benchmark different operators’ performance as it relates to changing rock quality and completion strategies.

On the Effectiveness of Stock Index Futures for Tail Risk Protection

This paper examines the effectiveness of using stock index futures contracts as substitutes for fixed-income securities in implementing expected shortfall targeting strategy. We find that the futures-based implementation outperforms its index-and-bill counterpart both in terms of downside protection and risk-adjusted performance at daily rebalancing frequency. This outperformance is driven not only by the transaction cost advantage, but also by the replication imperfections due to futures mispricing providing over the long term a better participation in upward market movements. When less frequent rebalancing intervals are used, the futures-based implementation becomes less effective at protecting the downside risk but still capture better the upside potential of the index.

Petrophysical Analysis and Acoustic Impedance Inversion to Determine the Storage Target of Carbon Dioxide in Gundih Block, Central Java

A pilot project of Carbon Capture and Storage in Indonesia is being prepared in the Gundih field, Central Java. This project acquired Kujung formation as their target area. However, an alternative to that formation is being investigated with Kujung formation's data as a benchmark to determine the total capacity of storage in Gundih Block. To determine the other carbon dioxide storage target, several tests are needed, one of which is petrophysical analysis. The results of this analysis are porosity, permeability, and thickness of a reservoir. After determining the porosity cut off from the Well data, we use the acoustic impedance inversion approach to assess the spread of the reservoir laterally. In this paper, we estimated that the porosity ranges from 7-12% with an average permeability of 10 mD. After examining the lateral distribution of the reservoir through the inversion, we can roughly estimate the distribution of good porosity reservoir. With the same method and logs derived from Kujung formation, we estimate that the upside potential from Ngrayong formation has an average porosity of 30% and average permeability of 16 mD. which are good enough to become an area of interest. With these results, we conclude that determining the area of interest from other target formation through petrophysical analysis and acoustic impedance inversion is sufficient to determine reservoir areas in a formation, though further injection feasibility studies are needed to determine the feasibility of injection of CO2 in Ngrayong Formation.

How the COVID-19 Pandemic Affects Risk Awareness in Dentists: A Scoping Review.

Dentists are on the frontline of infection, especially when it comes to respiratory viruses like the new coronavirus. The purpose of this paper is to conduct a scoping review to better understand dentists’ risk awareness, awareness of COVID-19 symptoms, preventive measures, and effective methods of COVID-19 infection prevention and management. This paper systematically assesses the published literature on dentistry and COVID-19. Various electronic databases including Ovid MEDLINE, Scopus, Embase, and MEDLINE via PubMed were searched up to 9 September 2021. Overall, 39 papers were included. Almost the entirety of dentists (94.5%) reported awareness of the three most common COVID-19 symptoms, and a risk awareness score of about 90% was shown, while 88.2% of dentists reported adopting preventive measures. More than 50% did not want to treat infected people. While 70.3% of dentists recommended usage of N95 masks, the rate of dentists using them was below 40%. Sufficient awareness of risks during the pandemic was found in dentists. Although they were using preventive measures, there remains upside potential for adopting all recommended measures. Further, the usage of N95 masks is improvable, even though the benefit of wearing them could not be confirmed.

Cloud Migration Strategies: How to Plan and Execute a Successful Move

One of the most critical cloud migrations ever was Netflix moving to AWS. This initiative, launched and completed in the run-up to 2021, delivered a 50% streaming cost reduction while materially improving the offerings to customers worldwide, proving the potential upside to a practical cloud approach

Economic Dispatch with High Penetration of Wind Power Using Extreme Learning Machine Assisted Group Search Optimizer with Multiple Producers Considering Upside Potential and Downside Risk

The power system with high penetration of wind power is gradually formed, and it would be difficult to determine the optimal economic dispatch (ED) solution in such an environment with significant uncertainties. This paper proposes a multi-objective ED (MuOED) model, in which the expected generation cost (EGC), upside potential (USP), and downside risk (DSR) are simultaneously considered. The heterogeneous indices of upside potential and downside risk mean the potential economic gains and losses brought by high penetration of wind power, respectively. Then, the MuOED model is formulated as a tri-objective optimization problem, which is related to uncertain multi-criteria decision-making against uncertainties. After-wards, the tri-objective optimization problem is solved by an extreme learning machine (ELM) assisted group search optimizer with multiple producers (GSOMP). Pareto solutions are obtained to reflect the trade-off among the expected generation cost, the upside potential, and the downside risk. And a fuzzy decision-making method is used to choose the final ED solution. Case studies based on the Midwestern US power system verify the effectiveness of the proposed MuOED model and the developed optimization algorithm.

POSC291 Acceleration of Digital Health Technologies in Value-Based Payment Systems

To achieve value-based healthcare (VBHC), outcomes that are meaningful to patients should be measured. In the field of dialysis some countries have implemented value-based payment systems to ensure that these outcomes are met by measuring Key Performance Indicators (KPIs). Digital health technologies (DHT) have the potential to support in maximizing these outcomes resulting in a financial upside potential. This study aims to explore which countries have value-based payment systems in dialysis and where investing in DHTs can be of the highest value for providers.

Early field planning using optimisation and considering uncertainties

During early phases of oil field development, field planners must decide upon the optimal number of wells and optimal field plateau rate, usually by performing sensitivity studies. These design choices are then “frozen” in subsequent development stages. However, they often end up being suboptimal when the field is built and produced and the uncertainty is reduced. In this work, we employ non-linear numerical optimisation, latin hypercube sampling and the Schwartz & Smith oil price model to compute probability distributions of the optimal number of wells, plateau rate and project value. We also employ an analytical model to compute production profiles and project value and consider uncertainties in in-place oil volume, well productivity and oil price. Then, we study how do these distributions change from early field planning until when the field is abandoned, when uncertainties are reduced to a minimum. The variation in time of the in-place oil volume uncertainty is modelled with a random walk. The well productivity is a step function altered randomly after production startup. The actual oil price trajectory is picked randomly from possible trajectories computed with the Schwartz and Smith model. The results show that the distributions of the optimal number of wells, plateau rate and project value depend greatly on the uncertainties in the input data. Field designs based on the average of the distributions during the early phase are profitable, but suboptimal. A potential upside of such designs is that they entail less capital investment and therefore less financial risk when compared against the optimal field design. • Robust approach to consider uncertainties when computing optimal field design. • The optimal design strongly depends on the uncertainties in input parameters. • Uncertainties in input parameters are considered to vary during the field’s life. • The value of perfect information is modest for the cases studied in this paper.

The Survival of Mediocre Superstars in the Labor Market

Abstract We argue that liquidity constrained firms face strong incentives to hire experienced, but low ability workers instead of novice workers with higher upside potential. Using four decades of high-frequency information on worker performance in a “superstar” labor market allows us to estimate the revealed ability of experienced workers at the time they are hired by a new firm. More than one-fifth of these hires are “substandard” in that the revealed ability of the hired experienced worker lies below the mean ability of recent novices. Even more hires (around 40%) are “mediocre,” as their ability falls short of the hiring threshold that maximizes the long-run average ability of the active workforce. Replacing mediocre hires by novice workers would increase the average ability of the workforce by 0.1 standard deviations. (JEL J31, J44, L83, M51).

New Venture Creation: A Drift-Variance Diffusion Control Model

A Model for New Venture Creation New ventures go through multiple stages: In the early stage, there is a business concept and preliminary evidence supporting the concept. In later stages, there are revenues and sales. In each stage, there are usually milestones for the venture to meet in order for investors to provide additional funding. Otherwise, the venture is abandoned. The entrepreneur can engage in a set of costly activities that aim to create value and reach the appropriate milestones. In “New Venture Creation: A Drift-Variance Diffusion Control Model,” Wang and Zenios develop a framework for new venture creation. The authors provide theoretical guidance on the optimal policy, which is relatively simple to describe. Their analysis reveals a trade-off between how costly an activity is and how much upside potential the activity generates, and their result shows how a new venture creator can manage that trade-off.

E&P Notes (November 2021)

E&P Notes (November 2021)

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.

Measuring and Managing the Opportunity Cost of Downside Risk Protection

Portfolio insurance targets the elimination of losses in excess of a predefined threshold while allowing for access to the upside potential of an underlying risky asset. This article studies the implications of the choice of the underlying asset on the frequency and magnitude of floor breaches and on the loss of performance associated with the protection against downside risk; it considers in particular the use of a diversified portfolio of stocks as opposed to a single stock. The authors find that gap risk is more prominent when insurance is applied to a stock than to a portfolio, but it can be reduced by implementing time-invariant portfolio protection or by letting the multiplier decrease when volatility rises. However, option-based insurance with dynamic option replication has a large probability of breaching, no matter the underlying asset. Among diversified portfolios, minimum variance portfolios minimize the opportunity cost of insurance, but the ranking of diversified portfolios in terms of long-term returns does not appear to be disturbed by insurance. <b>Key Findings</b> ▪ Floor breaches in a portfolio insurance strategy occur more frequently and larger if the risky asset of the strategy is a single stock than if it is a diversified portfolio of stocks. ▪ Option-based portfolio insurance with dynamic replication of the option involves large gap risk, whether the risky asset is a single stock or a diversified portfolio. ▪ The opportunity cost of insurance against downside risk, measured as the long-term return spread between the insured portfolio and its underlying risky asset, is minimized by taking the risky asset to be the minimum variance portfolio.

Involuntary job loss: welfare effects, earnings impacts and policy options

Workers who experience involuntary job loss suffer from deep and persistent negative consequences. In this paper, we first summarise the evidence on the effects of involuntary job loss on displaced workers’ wellbeing. We conclude that displacement harms workers’ mental health and economic security in the short term and negatively affects their earnings and mortality risk in the long term. We then extrapolate the estimates of Hyslop and Townsend (2017) to estimate the economy-wide net-present value of wages lost as a result of displacement by the workers displaced in New Zealand in a representative year. Our estimates suggest that this value is likely between $3.3 billion (in a year of economic upswing) and $15.4 billion (in a year of very severe economic downswing). Finally, we survey the policy options available for dealing with involuntary displacement. We conclude that unemployment insurance or unemployment benefits can effectively mitigate the immediate negative effects of displacement and have only small downsides. By contrast, training and job placement programs are typically ineffective, but in some circumstances might have high potential upside.

Assessing value of a digital company: Uber’s IPO 2019

Theoretical basis Via this case, students are introduced to several alternative methods of valuation, including the valuation based on the “real options” theory. The novelty of the case is the link between valuation and the type of innovation that the company represents. The suggested valuation frameworks, which include both quantitative and qualitative assessments, are applicable not only in the context of an IPO valuation but also in the context of any kind of M&amp;A activity. Research methodology This case was prepared mostly via secondary research. All the information about Uber and the industry was collected via publicly available sources. No internal documents of the company were used in the preparation of this case. The primary research consisted of an interview with the protagonist Catherine (whose name is disguised). Other disguised elements in the case include the name of the Value Investor conference organizer (Spyros Spyrou, not his real name), the country of the Value Investor conference (omitted) and the conference venue (Princess hotel, not any actual venue). Case overview/synopsis In 2019, Uber, the famous ride-sharing company, made waves in financial markets as the most controversial IPO valuation. With a wide range of proposed values, Uber puzzled investors, once again living up to its fame of a rebel and a disruptor. When Uber finally went public in May 2019, its IPO valuation stood at $82.4bn. The heated discussion in the media continued even after the IPO: “Is Uber worth this amount? Is there an upside potential for the investors who bought shares at the IPO price? What if this is a hype and markets are simply embracing higher valuations?” Complexity academic level This case can be used at the undergraduate, graduate (MBA) or executive level in finance-related courses such as Company Valuation or Valuing Innovation, which cover the topic of valuation and specifically the topic of valuing innovative companies.

Evaluating the Performance and Ability Explain of Market Index Returns by Selected Stock Portfolios Based on Throughput Accounting Criteria in Comparison with the New Network Matrix Model

One of the strategies used in active portfolio management is the matrix model which can be used to form different portfolios with different characteristics of stocks or companies. In this study, with the data of 156 companies listed on the Tehran Stock Exchange during the period 2011 to 2018 using the network matrix model and based on throughput accounting criteria, portfolio formation and their performance with the portfolios of the new network matrix model (Defensive, neutral and aggressive stocks) and market portfolio were compared. The results show that the proposed network matrix model portfolios based on throughput accounting criteria have higher performance than the new network matrix model in terms of Sharpe, Sortino, upside potential, and omega criteria. Also, portfolios consisting of stocks of companies with high system performance, in addition to the above criteria, have higher performance in terms of Jensen's Alpha criteria than the new network matrix model, and in terms of upside potential and omega criteria, have higher performance than the market portfolio. The performance of portfolios consisting of stocks of companies with low system performance has a stronger correlation with the market portfolio compared to the new network matrix model.

The effect of material and experiential consumption on goal pursuit

AbstractPrevious research predominately focuses on the negative aspects of material consumption. Thus, the primary purpose of this current research is to investigate the potential upside of material consumption on consumers' goal pursuit. Specifically, four studies show that whether individuals have recently engaged in experiential or material consumption determines how feedback about their performance in a later goal‐directed activity influences their goal‐relevant behavior. Experiential consumption triggers individuals' intrinsic motivation and focuses their attention on their enjoyment of engaging in an activity rather than the outcomes that result from doing so. Consequently, the feedback they receive about their performance in one task has little effect on their behavior in conceptually related situations. By contrast, the extrinsic motivation activated by material consumption leads individuals to use the feedback as an indication of the quality of their performance, motivating them to work harder on a later goal‐directed activity if the feedback they received was negative. Field studies confirm the implications of these findings in actual consumption situations.

Project Valuation and Risk Assessment in Food Product Development: Evidence from Fuzzy Real Option Valuation

Abstract To survive in today's competitive environment, companies must continuously develop and offer customers new products. To increase the probability of a successful business case of investing in the development of a new product, careful attention must be paid to risk analysis in terms of the present value of future potential income. The article considers an example of the research work of the Latvia University of Life Sciences and Technologies, in the framework of which a technical and technological project was developed to produce a new product, like Mediterranean anchovy, from cheaper Baltic sprats. The main goal of this work is to explore the application multi-factor sensitivity and fuzzy real option analysis to the valuation of new product development project. The multivariate analysis of the sensitivity of the financial model of the greenfield production project described in this article revealed the main risk groups, as well as their degree of influence on the assessment of the Net Present Value of the project by a potential investor. The use of Fuzzy Real Option Valuation made it possible to evaluate the project with uncertain parameters, as well as to calculate the potential upside from preliminary refinement of parameters to eliminate negative scenarios. The described approach is applicable to risk assessment of new food product development and allows investors to make a more informed decision about participation in such projects.