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This paper explores the double-edged sword effect of investor attention on market liquidity. Based on the analysis on 597 cryptocurrencies from 2014 to 2020, our findings show that static investor attention improves cryptocurrency market liquidity over the next three months by attracting more investors into the market and stimulating buy and sell transactions. By contrast, abnormal attention persistently and negatively affects the liquidity and leads to excessive net buying pressure in the market and a crowded buyers' market, resulting in a sharp deterioration of liquidity. Moreover, these effects intensify during low global economic policy uncertainty periods and for cryptocurrencies with small market capitalization and low idiosyncratic volatility. Overall, our results have important implications for investors, portfolio managers, and policymakers. [ABSTRACT FROM AUTHOR]

The goal of this paper is to examine the effect of high liquidity creation on systemic risk. We use a hand-collected dataset on 94 banks from 16 Western European countries over the 2004–2020 period, including the crisis (2008–2009) period and sound periods (2004–2007 and 2010–2020). We assess banks' systemic risk using two different proxies: banks' systemic risk exposure, measured by the marginal expected shortfall (MES), and banks' systemic risk contribution, measured by the delta conditional value at risk (ΔCoVaR). Based on panel regressions, our results mainly show that, during calm periods, high liquidity creation is associated with high systemic risk exposure. Moreover, we show that the effect of liquidity creation on banks' systemic risk exposure is stronger during turmoil periods. Interestingly, our results show that banks' liquidity creation increases the systemic contribution only during the financial crisis of 2008–2009. Our findings contribute to the literature and the regulatory debate by suggesting that regulators should pay more attention to high liquidity-creating banks as they may cause aggregate financial fragility. [ABSTRACT FROM AUTHOR]

In this paper, in order to serve credit risk management, we introduce a pricing model for a vulnerable Bull Spread options in a Mixed Modified Fractional Hull-White-Vasicek stochastic volatility and stochastic interest rate model. We use Milstein scheme to find the sample paths of asset price and its volatility, and the sample paths of interest rates of asset price movement. We use the double Mellin transform to obtain an analytical vulnerable bull spread call option formula and an analytical vulnerable bull spread put option formula under fractional stochastic volatility and fractional stochastic interest rates. [ABSTRACT FROM AUTHOR]

The objective of this paper is to emphasize the differences between a call and a warrant as well as the different valuation methods of warrants which have been introduced in the financial literature. For the sake of simplicity and applicability, we only consider a debt-free equity-financed firm. More recently a formal distinction between structural and reduced form pricing models has been introduced. This distinction is important whether one wishes to price a new warrant issue or outstanding warrants. If we are interested in pricing a new issue of warrants, e.g. in the context of a management incentive package, one has to rely on a structural model. However most of practitioners use the simple Black-Scholes formula. In this context, we analyze the accuracy of the approximation of the "true" price of a warrant by the Black-Scholes formula. We show that in the current low interest rate environment, the quality of the approximation deteriorates and the sensitivity of this approximation to the volatility estimate increases. [ABSTRACT FROM AUTHOR]

This paper is the first attempt to provide a comprehensive account of pricing and informational efficiency of the US and EU natural gas markets. We rely on Maximum Overlap Discrete Wavelet decomposition (MODWT) of daily data of US Henry Hub, British NBP and Dutch TTF natural gas physical and futures returns at different maturities between 2013 and 2019. Multiscale linear and nonlinear Granger causality and random walk testing are investigated. We find that futures prices and spot prices of Henry Hub, NBP, and TTF are cointegrated. Moreover, multiscale causality testing shows that Henry Hub markets exhibit strong bidirectional causality between spot and futures markets. EU markets are globally efficient in terms of pricing despite some inconsistencies on the causality direction across time scales and maturities of the futures contracts. Finally, for the three selected futures markets, informational efficiency is reached only in the long-run. The results make it possible to establish an evaluation of the hubs in terms of their capacity to provide reliable reference prices for the quantities of gas under contract through a discussion on the crucial role of liquidity and storage capacity. [ABSTRACT FROM AUTHOR]

This paper evaluates the stock performance of Islamic banks relative to their conventional counterparts during the initial phase of the COVID-19 crisis (from December 31, 2019, to March 31, 2020). Using 426 banks from 48 countries, we find that stock returns of Islamic banks were about 10–13% higher than those of conventional banks after controlling for a host of the bank- and country-level variables. This study explains the Islamic banks' superior crisis stock performance by exploring the potential role of pre-crisis bank efficiency. In a univariate analysis, we document higher non-parametric Data Envelopment Analysis (DEA) efficiency levels for Islamic banks than conventional banks in the year preceding the COVID-19 crisis. Our multivariate regressions show that the risk-adjusted DEA efficiency scores can explain crisis stock returns for Islamic banks but not conventional banks. The evidence is robust to alternative measures of stock returns, efficiency models, and other empirical strategies. Finally, we present insight on the importance of key bank characteristics in determining the stock returns of conventional banks during the crisis period. [ABSTRACT FROM AUTHOR]

This paper addresses the interpretability problem of non-parametric option pricing models by using the explainable artificial intelligence (XAI) approach. We study call options written on the S&P 500 stock market index across three market regimes: pre-COVID-19, COVID-19 market crash, and post-COVID-19 recovery. Our comparative option pricing exercise demonstrates the superiority of the random forest and extreme gradient boosting models for each market regime. We also show that the model's pricing accuracy has worsened from the pre-COVID-19 to the recovery period. Moneyness was the most important price determinants across the market regimes, while the implied volatility and time-to-maturity inputs contributed intermittently to a lesser extent. During the COVID-19 crash, open interest gained more economic importance due to the increased behavioral tendencies of traders consistent with market distress. [ABSTRACT FROM AUTHOR]

Islamic banking is among rapidly-growing components in the world's financial system. Within its institutions, continuous criteria of efficiency facilitate the evaluation of the impact of the reforms and policies on the banks' performance. In this paper, we employ the Multivariate Adaptive Regression Splines (MARS) method for estimating the efficiency of Islamic banks in developed and developing countries. MARS is a well-known efficient method for the flexible modelling of high-dimensional data. Unlike previous work, using a nonparametric technique of such a robustness instead of parametric approaches contributes to the improvement of the various estimates, which provides investors with accurate and timely information they can immediately react upon for a better decision-making in turbulent times. On the one hand, the results of the experiments show that, in the emerging region, there is evidence of a strong linkage between Islamic banking efficiency and gross domestic product. On the other hand, in the developed region, the efficiency is rather based upon Sharia Supervisory Board and board committees. These outcomes confirm previous works showing that governance-related variables have a significant positive effect on Islamic banking efficiency. Furthermore, the overall MARS-based predictions reveal that Islamic banks operating in developed countries are relatively more efficient than their counterparts in emerging countries. [ABSTRACT FROM AUTHOR]

This paper considers the use of loan loss reserves (LLRs) in the banking production process and treats it as one variable with a dual role. We establish a three-stage network Data Envelopment Analysis model to address this issue. Using a sample of 43 Chinese commercial banks over the period 2011–2019, the results show that the banks with the ratio between LLRs and total loans less than 1% have higher level of efficiency compared to the ones holding the ratio greater than 1%. The results show that when excluding LLRs in the production process, the efficiency scores are significantly inflated. We find that small and medium sized banks are more efficient than their big counterparts, however, the results show that big banks hold more than enough amounts of LLRs than the one required by the regulatory authority. When LLRs are excluded from the production process, it shows that big banks perform better than small and medium sized banks. Our findings show that less liquid banks perform better than the ones with higher levels of liquidity no matter in which way LLRs are treated. Finally, we find that lower capitalized banks, compared to the ones with high levels of capitalization, are less efficient. however, it shows that higher capitalized banks consistently keep more than 1% LLRs out of total loans. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has given rise to a spike in financial market volatility. In this paper, we attempt to assess the effects of financial & news-driven uncertainty shocks in growing Asian economies, using country-specific bond volatility shocks as a measure of local interest rate uncertainty. Also, we contrast the effects of local uncertainty with global stock market uncertainty. Using bond market data from nine Asian markets, we uncover a transmission mechanism of uncertainty shocks via the bond market. The mechanism works as a crowding-out effect due to government-led excessive market borrowing with supply-side consequences for the private sector, as opposed to economic policy or global stock market uncertainty which works more like a demand shock as in the literature. We conclude that countries with growing fiscal deficits that entail a larger government bond market or higher current account deficits, tend to experience an increase in the cost of borrowing due to this bond market volatility or interest rate uncertainty shocks. [ABSTRACT FROM AUTHOR]

Financial institutions, investors, mining companies and related firms need an effective accurate forecasting model to examine gold price fluctuations in order to make correct decisions. This paper proposes an innovative approach to accurately forecast gold price movements and to interpret predictions. First, it compares six machine learning models. These models include two very recent methods: the eXtreme Gradient Boosting (XGBoost) and CatBoost. The empirical findings indicate the superiority of XGBoost over other advanced machine learning models. Second, it proposes Shapley additive explanations (SHAP) in order to help policy makers to interpret the predictions of complex machine learning models and to examine the importance of various features that affect gold prices. Our results illustrate that the utilization of XGBoost along with SHAP approach could provide a significant boost in increasing the gold price forecasting performance. [ABSTRACT FROM AUTHOR]

Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems. [ABSTRACT FROM AUTHOR]

This paper deals with the issue of modelling the volatility of futures prices in agricultural markets. We develop a multi-factor model in which the stochastic volatility dynamics incorporate a seasonal component. In addition, we employ a maturity-dependent damping term to account for the Samuelson effect. We give the conditions under which the volatility dynamics are well defined and obtain the joint characteristic function of a pair of futures prices. We then derive the state-space representation of our model in order to use the Kalman filter algorithm for estimation and prediction. The empirical analysis is carried out using daily futures data from 2007 to 2019 for corn, cotton, soybeans, sugar and wheat. In-sample, the seasonal models clearly outperform the nested non-seasonal models in all five markets. Out-of-sample, we predict volatility peaks with high accuracy for four of these five commodities. [ABSTRACT FROM AUTHOR]

The data revolution transforms operations, innovation, and society through artificial intelligence and advanced analytics. Data-driven innovations (DDI) have the most potential to tackle global challenges, including poverty, healthcare, climate actions, disaster management, gender inequality, peace and justice and others. This paper identifies the sources of DDI capabilities to address various global challenges. The findings show three major foundations of DDI capabilities: market orientation, infrastructure orientation, and talent orientation. Theoretically, these findings highlight the role of dynamic DDI capabilities to sense, seize and transform global challenges. Practically, we present guidelines for developing DDI in an agile and efficient manner that is fair and inclusive. [ABSTRACT FROM AUTHOR]

Green approaches remain little disseminated in the healthcare sector despite growing interest in recent years from practitioners and researchers. Big Data Analytics Capability (BDAC) can play a critical role in the integration of environmental concerns into operations and supply chain management (OSCM) and further strengthen the environmental performance of healthcare facilities. According to the literature, the integration of the environment into operations process remains insufficient to achieve high levels of performance and requires efforts in green process innovation. However, this relationship between BDAC and green process innovation remains poorly justified empirically. To address this theoretical gap, we investigated the relationship between BDAC, environmental process integration, green process innovation in OSCM and environmental performance. The main contribution of this study is the valuable knowledge on how BDAC influences environmental process integration and green process innovation to enhance environmental performance. Moreover, the study highlights the mediating role of green process innovation on environmental performance, a finding that has not been mentioned in the extant literature. The paper provides valuable insight for managers and stakeholders that can assist them in supporting the application of BDAC in healthcare OSCM to create sustainable value. [ABSTRACT FROM AUTHOR]

This research aims to understand the link between perceived innovativeness and customer satisfaction in the fine-dining catering segment. By employing a mixed-method approach, this paper proposes a multidimensional framework for measuring the perceived innovativeness of restaurants throughout the entire customer journey. Customer satisfaction was measured by considering online customer-generated data from TripAdvisor. The study not only finds a strong correlation between perceived innovativeness and customer satisfaction but also presents how fine-dining restaurants can employ user-generated data to co-innovate entire customer journeys and restaurant experiences. The results highlight menu-, service-, and customer experience-related innovativeness as the three most important criteria for fine-dining restaurant customers. Additionally, the results of the qualitative study indicate that in the context of fine-dining catering, the quality of the dishes, the service, and the customers' experience with the service staff and chefs are the main elements of satisfaction that restaurants should consider in promoting innovation. [ABSTRACT FROM AUTHOR]

Every contact centre engages in some form of Call Quality Monitoring in order to improve agent performance and customer satisfaction. Call centres have traditionally used a manual process to sort, select, and analyse a representative sample of interactions for evaluation purposes. Unfortunately, such a process is marked by subjectivity, which in turn results in a distorted picture of agent performance. To address the challenge of identifying and removing subjectivity, empirical research is required. In this paper, we introduce an evidence-based, machine learning-driven framework for the automatic detection of subjective calls. We analyse a corpus of seven hours of recorded calls from a real-estate call centre using Deep Neural Network (DNN) for a multi-classification problem. The study establishes the first baseline for subjectivity detection, with an accuracy of 75%, which is comparable to relevant speech studies in emotional recognition and performance classification. We conclude, among other things, that in order to achieve the best performance evaluation, subjective calls should be removed from the evaluation process or subjective scores deducted from the overall results. [ABSTRACT FROM AUTHOR]

In recent years, the business ecosystem has focused on understanding new ways of automating, collecting, and analyzing data in order to improve products and business models. These actions allow operations management to improve prediction, value creation, optimization, and automatization. In this study, we develop a novel methodology based on data-mining techniques and apply it to identify insights regarding the characteristics of new business models in operations management. The data analyzed in the present study are user-generated content from Twitter. The results are validated using the methods based on Computer-Aided Text Analysis. Specifically, a sentimental analysis with TextBlob on which experiments are performed using vector classifier, multinomial naïve Bayes, logistic regression, and random forest classifier is used. Then, a Latent Dirichlet Allocation is applied to separate the sample into topics based on sentiments to calculate keyness and p-value. Finally, these results are analyzed with a textual analysis developed in Python. Based on the results, we identify 8 topics, of which 5 are positive (Automation, Data, Forecasting, Mobile accessibility and Employee experiences), 1 topic is negative (Intelligence Security), and 2 topics are neutral (Operational CRM, Digital teams). The paper concludes with a discussion of the main characteristics of the business models in the OM sector that use DDI. In addition, we formulate 26 research questions to be explored in future studies. [ABSTRACT FROM AUTHOR]

Data-driven innovation enables firms to design products that are more responsive to market needs, which greatly reduces the risk of innovation. Customer data in the same supply chain has certain commonality, but data separation makes it difficult to maximize data value. The selection of an appropriate mode for cooperation innovation should be based on the particular big data analytics capability of the firms. This paper focuses on the influence of big data analytics capability on the choice of cooperation mode, and the influence of their matching relationship on cooperation performance. Specifically, using game-theoretic models, we discuss two cooperation modes, data analytics is implemented individually (i.e., loose cooperation) by either firm, or jointly (tight cooperation) by both firms, and further discuss the addition of coordination contracts under the loose mode. Several important conclusions are obtained. Firstly, both firms' big data capability have positive effects on the selection of tight cooperation mode. Secondly, with the improvement of big data capability, the firms' innovative performance gaps between loose and tight mode will increase significantly. Finally, when the capability meet certain condition, the cost subsidy contract can alleviate the gap between the two cooperative models. [ABSTRACT FROM AUTHOR]

In recent times, the literature has seen considerable growth in research at the intersection of digital innovation, data analytics, and supply chain resilience. While the number of studies on the topic has been burgeoning, due to the absence of a comprehensive literature review, it remains unclear what aspects of the subject have already been investigated and what are the avenues for impactful future research. Integrating bibliometric analysis with a systematic review approach, this paper offers the review of 262 articles at the nexus of innovative technologies, data analytics, and supply chain resiliency. The analysis uncovers the critical research clusters, the evolution of research over time, knowledge trajectories and methodological development in the area. Our thorough analysis enriches contemporary knowledge on the subject by consolidating the dispersed literature on the significance of innovative technologies, data analytics and supply chain resilience thereby recognizing major research clusters or domains and fruitful paths for future research. The review also helps improve practitioners' awareness of the recent research on the topic by recapping key findings of a large amount of literature in one place. [ABSTRACT FROM AUTHOR]

Many organizations are increasingly investing in building dynamic capabilities to gain competitive advantage. New products play an important role in gaining competitive advantage and can significantly boost organizational performance. Although new product development (NPD) is widely recognized as a potentially vital source of competitive advantage, organizations face challenges in terms of developing the right antecedents or capabilities to influence NPD performance. Our research suggests that organizations should invest in building alliance management capability (AMC), big data analytics capability (BDAC) and information visibility (IV) to achieve their desired NPD success. Informed by the dynamic capabilities view of the firm (DCV) we have stated seven research hypotheses. We further tested our hypotheses using 219 usable respondents gathered using a pre-tested instrument. The hypotheses were tested using variance based structural equation modelling (PLS-SEM). The results of our study paint an interesting picture. Our study makes some significant contribution to the DCV and offers some useful directions to practitioners engaged in NPD in the big data analytics era. We demonstrate that AMC and BDAC are lower-order dynamic capabilities and that AMC has a positive and significant influence on BDAC. In turn, AMC and BDAC influence NPD under the moderating influence of IV. Ours is one of the first studies to empirically establish an association among three distinct dynamic capabilities which are often considered in isolation: AMC, BDAC and NPD. Our findings support emergent views on dynamic capabilities and their classification into various orders. Lastly, we provide empirical evidence that information visibility acts as a contingent variable to both AMC and BDAC effects on NPD. We end our paper by outlining some limitations of our study and by offering useful future research directions. [ABSTRACT FROM AUTHOR]

This paper considers several two-machine open shop problems with two agents. Each agent has an independent set of nonpreemptive jobs, and the objective is to find either a schedule minimizing a linear combination of the makespans of both agents, a schedule minimizing the makespan of one agent with the makespan of the other agent not exceeding a threshold, or all Pareto-optimal schedules with respect to the makespans of both agents. We present a number of results for the problems above, including a polynomial algorithm and a pseudo-polynomial algorithm for special cases, non-approximability, two approximation algorithms, and a fully polynomial-time approximation scheme. Finally, we run numerical experiments to show the effectiveness of the pseudo-polynomial algorithm and the approximation algorithms. [ABSTRACT FROM AUTHOR]

The paper analyzes the performance of tandem network of polling queue with setups. For a system with two-products and two-stations, we propose a new approach based on a partially-collapsible state-space characterization to reduce state-space complexity. In this approach, the size of the state-space is varied depending on the information needed to determine buffer levels and waiting times. We evaluate system performance under different system setting and comment on the numerical accuracy of the approach as well as provide managerial insights. Numerical results show that approach yields reliable estimates of the performance measures. We also show how product and station asymmetry significantly affect the systems performance. [ABSTRACT FROM AUTHOR]

Optimal sampling period selection for high-frequency data is at the core of financial instruments based on algorithmic trading. The unique features of such data, absent in data measured at lower frequencies, raise significant challenges to their statistical analysis and econometric modelling, especially in the case of heavy-tailed data exhibiting outliers and rare events much more frequently. To address this problem, this paper proposes a new methodology for optimal sampling period selection, which better adapts to heavy-tailed statistics of high-frequency financial data. In particular, the novel concept of the degree of impulsiveness (DoI) is introduced first based on alpha-stable distributions, as an alternative source of information for characterising a broad range of impulsive behaviours. Then, a DoI-based generalised volatility signature plot is defined, which is further employed for determining the optimal sampling period. The performance of our method is evaluated in the case of risk quantification for high-frequency indexes, demonstrating a significantly improved accuracy when compared against the well-established volatility-based approach. [ABSTRACT FROM AUTHOR]

Since the 1970s, Brazil has carried out the most successful world program of commercial biomass for use and production of energy by stimulating its sugarcane industry and promoting the large-scale production of ethanol nationwide in response to the first oil shock. Today, the technologies behind ethanol production are well established. Brazil is the world's largest sugarcane supplier, producing its ethanol at a competitive price. If other sugarcane producing countries decided to join Brazil's move toward the production of this biofuel, what impacts might there be for each country's economy and employment? This is what we investigate in this paper. Prime candidates for ethanol production include Australia, China, Colombia, India, Indonesia, Mexico, Pakistan, the Philippines, Thailand, and the United States of America. We evaluated the potential socioeconomic impacts of developing this promising industry by using an input–output approach. More specifically, we adapted the Brazilian method of producing ethanol to these countries' distinct economies. We augmented the input–output table of each country, inserting a new ethanol industry based on the Brazilian ethanol production model. We also augmented their new ethanol industry's sales following a hypothetical hydrous and anhydrous ethanol consumption scenario. Thereafter, we reconcile the national accounts, concluding our analysis by quantifying and comparing the different net effects of this new industry for each of the assessed countries for the year of 2009. We demonstrate which industries would be expected to be positively or negatively impacted by this substitution in each country; and find that not all of the countries we assessed would experience positive socioeconomic results from emulating Brazil's production of ethanol. [ABSTRACT FROM AUTHOR]

The uncertain flow is a common factor that leads to risks in hub-and-spoke systems. Confronted with risks, it cannot be ignored that the decision maker has different risk-bearing capacities, which influence decision-making. This paper presents an α -risk appetite to characterize the risk-bearing capacity and employs the concept of belief degree to construct it. A multi-commodity capacitated p-hub median model is built with time windows, uncertain flows and the α -risk appetite objective function. In this model, the uncertain flows are depicted as uncertain variables, which possess an empirical uncertainty distribution even with little data. Uncertainty theory has the advantage in converting the proposed uncertain model into a deterministic equivalent form. While the deterministic model is still a mixed 0–1 integer programming problem and has challenges in theory and practice for addressing large-scale problems. Based on the difficulty, we design a hybrid genetic algorithm, where a new principle of cis-position assignment is proposed and inserted into this algorithm to accelerate the solving process. By comparing the proposed model with a deterministic benchmark model, the computational results demonstrate that taking the α -risk appetite and uncertainty into account could avoid the losses of neglecting risks. The observations of cost and hub location reflect the model's sensitivity about risk appetite levels. Finally, the effectiveness of the proposed algorithm is verified by two data sets, the Civil Aeronautics Board data and the Australian Post data. [ABSTRACT FROM AUTHOR]

In this paper, a supply chain including a farmer, a dairy manufacturer, and consumers is considered in which a particular product is sold through two channels, i.e., formal and informal. In the former, the dairy manufacturer buys the dairy product at a wholesale price from the farmer and sells it to consumers, whereas in the latter, the farmer sells the product directly to consumers. The farmer may add a sort of chemical additive to the product in order to increase its demand in either channel. This may be discovered only if a product safety accident happens. Furthermore, the government, as a regulator, plays an integral role in preventing the production and consumption of contaminated food. We aim to investigate the behavior of players in this supply chain and decide about safety issues and the wholesale price of the product using the game theory approach. To this end, four scenarios, including centralized structure, decentralized structure, cost-sharing contract, and safety guarantee contract, are examined. The experimental results reveal that by enacting appropriate laws, the government can prevent the supply chain players from utilizing unauthorized chemical additives. Furthermore, it is shown that the safety guarantee contract can improve the safety level of the product, which is always at a higher level in the formal channel than in the informal one. It was also revealed that fining the farmer has a greater impact on product safety than fining the manufacturer. Moreover, the farmer has less tendency to use chemical additives when the costs of the product and chemical additives are high or additives pose a high risk to consumers whereas higher product prices tempt the farmer to utilize more additives. [ABSTRACT FROM AUTHOR]

This paper examines spillover from financial exchange-traded funds (ETF) futures to energy ETF futures using adjusted daily data extending from April 2, 2009, to November 23, 2020. We also explore the portfolio hedging-based conditional variance and co-variance derived from dynamic conditional correlation. The proxies for the financial ETF futures are financial select sector SPDR fund (XLF) and generic 1st S&P 500 index futures (SP1) while generic 1st crude oil WTI futures (CL1), generic 1st natural gas futures (NG1), and energy select SPDR fund (XLE) are proxies of energy ETF futures. The results obtained from Granger causality indicate that there is unidirectional causality from RXLF to RSP1 while bidirectional causality between RXLF and RCL1 at a 5% significance level. Further, dynamic conditional correlation indicates the spillover effect from RXLF to RCL1, RXLF to RXLE, RSP1 to RCL1, and RSP1 to RXLE both in the short-run and long run. The spillover from RXLF to RNG1 is witnessed only in the short run while the spillover from RSP1 to RNG1 is present in long run. The present study corroborates with the studies of Chang et al. (Int J Finan Stud 6(2): 1–24, 2018) and Lau et al. (Int Rev Finan Anal 52: 316-332, 2017). We notice that the average optimal hedge ratio of the RXLF/RNG1 pair is the most expensive while the cheapest hedging strategy is of RSP1/RCL1 pair. [ABSTRACT FROM AUTHOR]

This paper presents a vendor-managed inventory model for a three-layer supply chain comprised of suppliers, manufacturers, and retailers. This model considers an imperfect production system with a deterministic percentage of defective and deteriorated items, exponential demand for perishable products, and operational costs. The VMI model maximizes the benefits, assuming a Stackelberg approach to find the optimal values of replenishment lot sizes, production rates, selling prices, and product cycle length. A solution algorithm is developed to find the optimal solutions. A case study of the Dairy Industry for highly perishable products is provided to illustrate the applications, evaluate the performance, and obtain managerial insights. The results show that the percentage of defective and deteriorated items and remanufacturing rates are crucial in replenishment, production, and selling policies. The VMI agreement includes remanufacturing defective products to obtain new dairy derivates. The deteriorated products can be sold as near-expired products or inputs for compost, fertilizers, etc. Finally, some conclusions and future research lines are provided. [ABSTRACT FROM AUTHOR]

Businesses nowadays often feel impelled to rush and implement Big Data and Artificial Intelligence initiatives in their organizations without clarity on their business problems, nor the appropriate methodologies for extracting actionable insights from the data. In contrast, this paper presents a process that starts with an articulated business problem instead of a "data rush". The presented process, dubbed the Business-Driven Data-Supported (BDDS) process, is rigorously anchored in concepts from Theory of Constraints and Information Theory. BDDS guides businesses in solving their problems by stating observed performance gaps, uncovering their underlying root cause, formulating the "right question", utilizing only the "right data", and choosing the "right methodology" to extract the "right information" from the data, leading to the "right actionable insights." Also provided is a prescriptive framework, dubbed the Data-to-Information-Extraction-Methodology (DIEM), for selecting the "right methodology", based on the understanding level of relevant system dependencies and the availability of relevant data. The BDDS process is illustrated by an example from the healthcare industry, and the efficacy and applicability of the DIEM framework are supported by eleven case studies from a broad range of industries. [ABSTRACT FROM AUTHOR]

This paper presents a real-world liquid fuel distribution problem involving a heterogeneous fleet of multi-compartment vehicles servicing a set of orders of different fuels. Two new features are introduced that affect significantly the performance of the fuel distribution process in terms of safety and efficiency: (i) loading trucks so that the payload remains balanced throughout each phase of a delivery route, and (ii) sequencing the requests for loading trucks at the depot loading facilities. A Mixed Integer Programming formulation is presented and an Adaptive Large Neighbourhood Search algorithm with various novel features is developed and benchmarked. A new loading model is formed and solved for allocating the ordered items (fuel) to vehicle compartments, as part of constructing/repairing delivery routes. The computational performance of the proposed solution approach has been tested on a series of benchmark problems. Moreover, a series of experiments were performed in order to assess the effect of the balanced loading constraints on the traveled distance. The results indicate that the effect of this type of constraint on the total traveled distance is kept at a reasonable level, reaching a maximum increase of 4.37%. The computational tools presented in this work may accommodate the dispatchers' work in producing efficient and safe delivery routes while managing efficiently any potential bottleneck in the truck loading facilities. [ABSTRACT FROM AUTHOR]

This paper introduces the d-distanceb-matching problem, in which we are given a bipartite graph G = (S , T ; E) with S = { s 1 , ⋯ , s n } , a weight function on the edges, an integer d ∈ Z + and a degree bound function b : S ∪ T → Z + . The goal is to find a maximum-weight subset M ⊆ E of the edges satisfying the following two conditions: (1) the degree of each node v ∈ S ∪ T is at most b(v) in M, (2) if s i t , s j t ∈ M , then | i - j | ≥ d . In the cyclic version of the problem, the nodes in S are considered to be in cyclic order. We get back the (cyclic)d-distance matching problem when b (s) = 1 for s ∈ S and b (t) = ∞ for t ∈ T . We prove that the d-distance matching problem is APX-hard, even in the unweighted case. We show that 2 - 1 d is a tight upper bound on the integrality gap of the natural integer programming model for the cyclic d-distance b-matching problem provided that (2 d - 1) divides the size of S. For the non-cyclic case, the integrality gap is shown to be at most (2 - 2 d) . The proofs give approximation algorithms with guarantees matching these bounds, and also improve the best known algorithms for the (cyclic) d-distance matching problem. In a related problem, our goal is to find a permutation of S maximizing the weight of the optimal d-distance b-matching. This problem can be solved in polynomial time for the (cyclic) d-distance matching problem — even though the (cyclic) d-distance matching problem itself is NP-hard and also hard to approximate arbitrarily. For (cyclic) d-distance b-matchings, however, we prove that finding the best permutation is NP-hard, even if b ≡ 2 or d = 2 , and we give e-approximation algorithms. [ABSTRACT FROM AUTHOR]

In this paper we study the performance of a two-stage approach to scheduling under uncertainty making use of sequences of groups of permutable operations. Given a sample set of uncertainty realization scenarios, the goal is to compute a sequence of groups of permutable operations representing a partial scheduling decision in the first-stage, that yields the best possible score in the second-stage, when, for a specific scenario, a full operation sequence is obtained via a second-stage decision policy. This approach is described for a single-machine problem and the jobshop problem with stochastic and robust optimization, as well as several commonly studied objectives. We propose new constraint programming models as well as a genetic algorithm meta-heuristic to compute such two-stage solutions. We also investigate a warm-start scheme to work around the difficult search space of sequences of permutable operations. Experiments are carried out to characterize when this two-stage approach yields better results. We also compare the introduced methods with existing ones. Theoretical extensions of the known methods are also described and evaluated. [ABSTRACT FROM AUTHOR]

This study contributes to the unconsolidated cryptocurrency literature, with a systematic literature review focused on cryptocurrency market microstructure. We searched Web of Science database and focused only on journals listed on 2021 ABS list. Our final sample comprises 138 research papers. We employed a quantitative and an integrative analysis, and revealed complex network associations, and a detailed research trending analysis. Our study provides a robust and systematic contribution to cryptocurrency literature by making use of a powerful and accurate methodology—the bibliographic coupling, also by only considering ABS academic journals, using a wider keyword scope, and not enforcing any restrictions regarding areas of knowledge, thus enhancing the contribution of extant literature by allowing the insights of more high-quality peripheral studies on the subject. The conclusions of this study are of extreme importance for researchers, investors, regulators, and the academic community in general. Our study provides high structured networking and clear information for research outlets and literature strands, for future studies on cryptocurrency investment, it also presents valuable insights to better understand the cryptocurrency market microstructure and deliver helpful information for regulators to effectively regulate cryptocurrencies. [ABSTRACT FROM AUTHOR]

Transshipment yards are not usually considered in optimization approaches proposed for solving the log transportation problem. However, they may provide substantive advantages with respect to the usual logistics practice of direct delivering from supply forests to mills. Therefore, this paper evaluates, at the operational level, different supply chain topologies with the purpose of assessing potential costs savings associated to the use of intermediate storage facilities. The researched problem is defined by stated sets of log-suppliers, storage and transshipment yards, and facilities demanding several types of logs over a known planning horizon. The optimal transportation and inventory plan must be found. An optimization algorithm based on Column Generation + GRASP pricing metaheuristic was developed to compute minimal operation costs associated to different supply-chain topologies over a bank of instances generated from realistic information of the forestry activity in the Argentinean province of Misiones. [ABSTRACT FROM AUTHOR]

The conjecture of Beineke and Harary states that for any two vertices which can be separated by k vertices and l edges for l ≥ 1 but neither by k vertices and l - 1 edges nor k - 1 vertices and l edges there are k + l edge-disjoint paths connecting these two vertices of which k + 1 are internally disjoint.In this paper we prove this conjecture for l = 2 and every k ∈ N .We utilize this result to prove that the conjecture holds for all graphs of treewidth at most 3 and all k and l. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the cohesive players and a related axiom called equal surplus of cohesive players. On this basis, we study the subclass of efficient, symmetric, and linear (ESL) values satisfying equal surplus of cohesive players. We first give an analytical formula and also propose two characterizations for this subclass of ESL values. With these characterizations, we discuss the relationships between this subclass and other classical ESL values, in particular the Shapley value. We then characterize each value in the subclass of ESL values satisfying equal surplus of cohesive players by introducing the β -null player surplus property and the β -reward cohesive player property. From this, we obtain new parallel characterizations of the Shapley value and the equal surplus division value. Moreover, we show that equal surplus of cohesive players can replace symmetry in many well-known characterizations of values. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of long-term investment by exploring optimal strategies for allocating wealth among a finite number of assets over multiple periods. Based on the classical Markowitz mean-variance philosophy, we develop a new portfolio optimization framework which can produce sparse portfolios. The sparsity of the portfolio at each and across periods is characterized by the possibly nonconvex penalties. For the constructed nonconvex and nonsmooth constrained model, we propose a generalized alternating direction method of multipliers and its global convergence to a stationary point can be guaranteed theoretically. Moreover, some numerical experiments are conducted on several datasets generated from practical applications to illustrate the effectiveness and advantage of the proposed model and solving method. [ABSTRACT FROM AUTHOR]

This paper is devoted to the theory of perishable inventory systems. In such systems items arrive and stay 'on the shelf' until they are either taken by a demand or become outdated. Our aim is to survey, extend and enrich the probabilistic analysis of a large class of such systems. A unifying principle is to consider the so-called virtual outdating process V , where V(t) is the time that would pass from t until the next outdating if no new demands arrived after t. The steady-state density of V is determined for a wide range of perishable inventory systems. Key performance measures like the rate of outdatings, the rate of unsatisfied demands and the distribution of the number of items on the shelf are subsequently expressed in that density. Some of the main ingredients of our analysis are level crossing theory and the observation that the V process can be interpreted as the workload process of a specific single server queueing system. [ABSTRACT FROM AUTHOR]

As the only largest source of revenue and cost in a hospital, the operation room (OR) scheduling problem is a hot research topic. Nonetheless, an integrated model is the missing key to managing and improving the efficiency of ORs. This paper presents a fully integrated model regarding three concepts: meditating elective patients and emergency patients together, considering ORs and downstream units, and proposing hierarchical weekly, daily, and rescheduling models. Due to the inherent randomness in emergency patient arrival, a random forest machine learning model and geographical information systems are used to obtain the emergency patient surgery duration and arrival time, respectively. According to the machine learning model in weekly and daily scheduling, initially, fixed capacity is reserved for emergency patients. When an emergency patient arrives, the surgery starts if a reserved OR is available. Otherwise, the first available OR will be dedicated to the patient due to an emergency patient's higher priority than an elective patient. In this case, it is needed to reschedule the OT schedule for the remaining patient. Moreover, the three-phase model guarantees that an emergency patient assigns to an OR within a specific time limit. To solve the models, genetic algorithm and particle swarm optimization are developed and compared. In addition, a real-world case study is undertaken at a hospital. The results of comparing the proposed approach to the hospital's current scheduling show that the three-phase model had a considerable positive effect on the ORs schedule. [ABSTRACT FROM AUTHOR]

The multi-objective integer programming problem often occurs in multi-criteria decision-making situations, where the decision variables are integers. In the present paper, we have discussed an algorithm for finding all efficient solutions of a multi-objective integer quadratic programming problem. The proposed algorithm is based on the aspect that efficient solutions of a multi-objective integer quadratic programming problem can be obtained by enumerating ranked solutions of an integer quadratic programming problem. For determining ranked solutions of an integer quadratic programming problem, we have constructed a related integer linear programming problem and from ranked solutions of this integer linear programming problem, ranked solutions of the original integer quadratic programming problem are generated. Theoretically, we have shown that the developed method generates the set of all efficient solutions in a finite number of steps, and numerically we have elaborated the working of our algorithm and compared our results with existing algorithms. Further, we have analyzed that the developed method is efficient for solving a multi-objective integer quadratic programming problem with a large number of constraints, variables and objectives. [ABSTRACT FROM AUTHOR]

In this paper, we study large losses arising from defaults of a credit portfolio. We assume that the portfolio dependence structure is modelled by the Archimedean copula family as opposed to the widely used Gaussian copula. The resulting model is new, and it has the capability of capturing extremal dependence among obligors. We first derive sharp asymptotics for the tail probability of portfolio losses and the expected shortfall. Then we demonstrate how to utilize these asymptotic results to produce two variance reduction algorithms that significantly enhance the classical Monte Carlo methods. Moreover, we show that the estimator based on the proposed two-step importance sampling method is logarithmically efficient while the estimator based on the conditional Monte Carlo method has bounded relative error as the number of obligors tends to infinity. Extensive simulation studies are conducted to highlight the efficiency of our proposed algorithms for estimating portfolio credit risk. In particular, the variance reduction achieved by the proposed conditional Monte Carlo method, relative to the crude Monte Carlo method, is in the order of millions. [ABSTRACT FROM AUTHOR]

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we address the controversies of epidemic control planning by developing a novel Simulation-Deep Reinforcement Learning (SiRL) model. COVID-19 reminded constituents over the world that government decision-making could change their lives. During the COVID-19 pandemic, governments were concerned with reducing fatalities as the virus spread but at the same time also maintaining a flowing economy. In this paper, we address epidemic decision-making regarding the interventions necessary given of the epidemic based on the purpose of the decision-maker. Further, we intend to compare different vaccination strategies, such as age-based and random vaccination, to shine a light on who should get priority in the vaccination process. To address these issues, we propose a simulation-deep reinforcement learning (DRL) framework. This framework is composed of an agent-based simulation model and a governor DRL agent that can enforce interventions in the agent-based simulation environment. Computational results show that our DRL agent can learn effective strategies and suggest optimal actions given a specific epidemic situation based on a multi-objective reward structure. We compare our DRL agent's decisions to government interventions at different periods of time during the COVID-19 pandemic. Our results suggest that more could have been done to control the epidemic. In addition, if a random vaccination strategy that allows super-spreaders to get vaccinated early were used, infections would have been reduced by 32% at the expense of 4% more deaths. We also show that a behavioral change of fully quarantining 10% of the risky individuals and using a random vaccination strategy leads to a reduction of the death toll by 14% and 27% compared to the age-based vaccination strategy that was implemented and the New Jersey reported data, respectively. We have also demonstrated the flexibility of our approach to be applied to other locations by validating and applying our model to the COVID-19 case in the state of Kansas. [ABSTRACT FROM AUTHOR]

In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm's parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence. [ABSTRACT FROM AUTHOR]

The integration between blockchain and artificial intelligence (AI) has gained a lot of attention in recent years, especially since such integration can improve security, efficiency, and productivity of applications in business environments characterised by volatility, uncertainty, complexity, and ambiguity. In particular, supply chain is one of the areas that have been shown to benefit tremendously from blockchain and AI, by enhancing information and process resilience, enabling faster and more cost-efficient delivery of products, and augmenting products' traceability, among others. This paper performs a state-of-the-art review of blockchain and AI in the field of supply chains. More specifically, we sought to answer the following three principal questions: Q1—What are the current studies on the integration of blockchain and AI in supply chain?, Q2—What are the current blockchain and AI use cases in supply chain?, and Q3—What are the potential research directions for future studies involving the integration of blockchain and AI? The analysis performed in this paper has identified relevant research studies that have contributed both conceptually and empirically to the expansion and accumulation of intellectual wealth in the supply chain discipline through the integration of blockchain and AI. [ABSTRACT FROM AUTHOR]

Industrial relocation (IR) is a business strategy consisting of moving operations locations. The purpose of this paper is to present how to assess, with multi-attribute decision-making (MADM), alternatives for IR. With MADM, IR strategies can be assessed not only based on a single attribute, as costs, or profits. This paper presents the application of MADM in a real case of IR. Four leading methods of MADM were applied: analytic hierarchy process (AHP), multi-attribute utility theory (MAUT), multi-attribute value theory (MAVT), and technique of order preference by similarity to ideal solution (TOPSIS). Results of AHP, MAUT, MAVT, and TOPSIS were quite similar, indicating the decision for the company not to relocate. A joint comparison of results with compatibility indices and correlation coefficients is the major novelty presented by this paper to the field of Operations Research, known as MADM. [ABSTRACT FROM AUTHOR]

In this paper we propose three models for locating multiple facilities anywhere in the plane. The facilities serve demand points and require raw materials from a list of available sources. Problem characteristic originally proposed in 1909 by Weber for manufacturing systems. Weber argued that optimal locations involve minimizing total transport cost which was comprised of the costs of transporting the raw materials and the delivery cost of the final product when plant production and location costs were invariant across the plane. Both the parameters of raw material sources and demand points affect the best locations for the facilities. In this paper, a special algorithm is designed to heuristically solve these three models. The algorithm exploits the special structure of the models. Problems with up to 2000 demand points and 20 facilities were tested. The results are compared with applying available non-linear solvers in a multi-start approach. The special algorithm performed better in most instances especially for a large number of facilities and a large number of demand points. [ABSTRACT FROM AUTHOR]

The paper studies product bundling in a duopoly supply chain network under the influence of different power-balance structures, bundling decisions and advertising efforts on total supply chain profit. Mathematical models comprising two manufacturers and a single retailer are developed to capture the impact of bundling policy and advertisement strategy under three power-balance structures, namely Manufacturer Stackelberg, Retailer Stackelberg and Vertical Nash. Following game theory models and numerical examples, the study found that the total profit of the supply chain is undifferentiated under the manufacturer Stackelberg and Vertical Nash case in the manufacturer bundling and retailer bundling strategies. However, total supply chain profit under manufacturer bundling strongly dominates under retailer bundling in Retailer Stackelberg and Vertical Nash, and remains valid under multiple settings of market size, price elasticity and advertising elasticity. It is also found that manufacturer bundling is significantly affected by advertising effort compared to retailer bundling. The study contributes to the literature interfacing supply chain and marketing by studying bundling policy and advertising strategy simultaneously for homogenous products, under various power-balance structures and price competition. [ABSTRACT FROM AUTHOR]

This paper proposes novel multi-objective optimization strategies to develop a weighted ensemble model. The comparison of the performance of the proposed strategies against simulated data suggests that the multi-objective strategy based on joint entropy is superior to other proposed strategies. For the application, generalization, and practical implications of the proposed approaches, we implemented the model on two real datasets related to the prediction of credit risk default and the adoption of the innovative business model by firms. The scope of this paper can be extended in ordering the solutions of the proposed multi-objective strategies and can be generalized for other similar predictive tasks. [ABSTRACT FROM AUTHOR]