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The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. [ABSTRACT FROM AUTHOR]

Substantial R &D efforts are currently directed towards the development of combined heat and power (CHP) systems that automatically and seamlessly connect to the power grid. In this paper we develop a real options model to assess the impact that the operational flexibility characterizing such systems will have on the optimal timing and capacity associated with investments in CHP plants. We take the viewpoint of a manufacturer operating in an energy-intensive industry who contemplates investing in CHP. We discuss and compare investments in two types of CHP systems: a standard one that is operationally rigid and a technologically advanced one that is operationally flexible. The interaction between temporal and operational flexibility under uncertainty and irreversibility is central to our analysis. We show that operational flexibility guarantees earlier investment but has an ambiguous effect in terms of capacity. In particular, when operational flexibility is very valuable the potential investor is opting for investing in a plant with larger productive capacity. The potential investor chooses a smaller CHP unit if otherwise. A numerical exercise calibrated using data from the Italian pulp and paper and electricity industries complements our theoretical analysis. [ABSTRACT FROM AUTHOR]

The COVID-19 global health crisis forced border closings, strained resources and tightened funding, forcing humanitarian organisations to innovate. This paper aims to identify gaps in the literature on innovation in humanitarian supply chains, and to develop an appropriate framework for future research through a systematic literature review. We use a systematic literature review approach and synthesis the discussion of innovation in humanitarian supply chains after reviewing 43 papers. The synthesis identifies the different contexts for and outcomes of innovation in humanitarian supply chains. Our findings indicate that research on innovation in humanitarian supply chains is an underdeveloped topic. Gaps we identified in regards to the humanitarian context are: (1) a limited discussion of the contribution by the beneficiary to the supply chain; (2) a limited discussion of reconstruction innovations; (3) a lack of study on field application for complex innovations; (4) the lack of discussion of the role of individual knowledge in humanitarian supply chain innovation and finally (5) a lack of study of position innovations where humanitarian organisations use supply chains as a way to market effectively towards donors. [ABSTRACT FROM AUTHOR]

In recent years, emerging technologies have gained popularity and being implemented in different fields. Thus, critical leading-edge technologies such as artificial intelligence and other related technologies (blockchain, simulation, 3d printing, etc.) are transforming the operations and other traditional fields and proving their value in fighting against unprecedented COVID-19 pandemic outbreaks. However, due to this relation's novelty, little is known about the interplay between emerging technologies and COVID-19 and its implications to operations-related fields. In this vein, we mapped the extant literature on this integration by a structured literature review approach and found essential outcomes. In addition to the literature mapping, this paper's main contributions were identifying literature scarcity on this hot topic by operations-related fields; consequently, our paper emphasizes an urgent call to action. Also, we present a novel framework considering the primary emerging technologies and the operations processes concerning this pandemic outbreak. Also, we provided an exciting research agenda and four propositions derived from the framework, which are collated to operations processes angle. Thus, scholars and practitioners have the opportunity to adapt and advance the framework and empirically investigate and validate the propositions for this and other highly disruptive crisis. [ABSTRACT FROM AUTHOR]

The paper examines whether the structure of the risk factor disclosure in an IPO prospectus helps explain the cross-section of first-day returns in a sample of Chinese initial public offerings. This paper analyzes the semantics and content of risk disclosure based on an unsupervised machine learning algorithm. From both long-term and short-term perspectives, this paper explores how the information effect and risk effect of risk disclosure play their respective roles. The results show that risk disclosure has a stronger risk effect at the semantic novelty level and a more substantial information effect at the risk content level. A novel aspect of the paper lies in the use of text analysis (semantic novelty and content richness) to characterize the structure of the risk factor disclosure. The study shows that initial IPO returns negatively correlate with semantic novelty and content richness. We show the interaction between risk effect and information effect on risk disclosure under the nature of the same stock plate. When enterprise information transparency is low, the impact of semantic novelty and content richness on the IPO market is respectively enhanced. [ABSTRACT FROM AUTHOR]

Continuous changes in today's market have exposed supply chain (SC) management to severe challenges. Such changes in the market are divided into two general categories: (1) Time-dependent changes such as political, economic, and cultural changes occurring over time and require decision-makers to consider the dynamics of decisions; and (2) Sudden changes and events such as diseases and natural disasters disrupting SCs. This paper investigates the impact of both changes on the decisions of a green supply chain. Moreover, these challenges may affect the organizations' activities in the market and arouse a conflict in the members' goals, leading to competition among members. This paper studies a game model for a dynamic–stochastic supply channel SC with one manufacturer and one retailer, where two types of products (green and non-green) are sold. In this study, two models are dynamically considered: The first model without demand disruptions and the second model with demand disruptions. Finally, the results are compared. On the other hand, due to environmental goals and to survive manufacturers under disruption, the government supports manufacturers in producing green products. Implementing this cooperation, the manufacturer also invests more in improving the greening level of green products, thereby enhancing its demand. Regarding numerous disruptions common in the automotive industry, this case study is conducted on the automotive supply chain to evaluate the models, and the results are presented in the form of numerical examples. Finally, to extract managerial insights, sensitivity analysis is performed on the main parameters of the problem. [ABSTRACT FROM AUTHOR]

This paper examines demand information value and acquisition strategy for a supply chain composed of a supplier and a retailer under supplier encroachment, and considers the situation that both the enterprises can obtain information directly from the market. The problem is formulated with a Bayesian game model. A signaling mechanism is considered, i.e., the supplier's demand information will be signaled to the retailer through wholesale pricing. Under the signaling mechanism, this paper analyzes whether it is still worthy to the supplier and the retailer of acquiring information at a non-ignorable cost under dual information asymmetry. Moreover, this paper examines the interaction between channel substitution rate and information acquisition. Furthermore, subsidies for inducing information sharing and the signaling effects on the payoffs are also explored. The results propose strategy selections of encroachment and information acquisition based on cost thresholds. A double information inaccuracy effect is discovered, which comes from the fact that an enterprise receives the signal from another supply chain member as well as acquires information from the market. It is interesting to find that the information inaccuracy does not necessarily have a negative effect due to double information inaccuracy. The inaccuracy rate has a positive effect first and then a negative effect on the payoffs of the supply chain under certain conditions. The value of signaling of demand information is analyzed, revealing that lower substitution rate will make the signaling mechanism have a positive impact on the enterprises payoffs. Some managerial insights are presented in the end. [ABSTRACT FROM AUTHOR]

In this paper, we study capital allocation for dynamic risk measures, with an axiomatic approach but also by exploiting the relation between risk measures and BSDEs. Although there is a wide literature on capital allocation rules in a static setting and on dynamic risk measures, only a few recent papers on capital allocation work in a dynamic setting and, moreover, those papers mainly focus on the gradient approach. To fill this gap, we then discuss new perspectives to the capital allocation problem going beyond those already existing in the literature. In particular, we introduce and investigate a general axiomatic approach to dynamic capital allocations as well as an approach suitable for risk measures induced by g-expectations under weaker assumptions than Gateaux differentiability. [ABSTRACT FROM AUTHOR]

Digital Contact Tracing (DCT) has been proved to be an effective tool to counteract the new SARS-CoV-2 or Covid-19. Despite this widespread effort to adopt the DCT, less attention has been paid to the organisation of the health logistics system that should support the tracing activities. Actually, the DCT poses a challenge to the logistics of the local health system in terms of number of daily tests to be collected and evaluated, especially when the spreading of the virus is soaring. In this paper we introduce a new optimisation problem called the Daily Swab Test Collection (DSTC) problem, that is the daily problem of collecting swab tests at home in such a way to guarantee a timely testing to people notified by the app to be in contact with a positive case. The problem is formulated as a variant of the team orienteering problem. The contributions of this paper are the following: (i) the new optimisation problem DSTC that complements and improves the DCT approach proposed by Ferretti et al. (Science https://doi.org/10.1126/science.abb6936, 2020), (ii) the DSCT formulation as a variant of the TOP and a literature review highlighting that this variant can have useful application in healthcare management, (iii) new realistic benchmark instances for the DSTC based on the city of Turin, (iv) two new efficient and effective hybrid algorithms capable to deal with realistic instances, (v) the managerial insights of our approach with a special regard on the fairness of the solutions. The main finding is that it possible to optimise the underlying logistics system in such a way to guarantee a timely testing to people recognised by the DCT. [ABSTRACT FROM AUTHOR]

The anti-epidemic supply chain plays an important role in the prevention and control of the COVID-19 pandemic. Prior research has focused on studying the facility location, inventory management, and route optimization of the supply chain by using certain parameters and models. Nevertheless, uncertainty, as a vital influence factor, greatly affects the supply chain. As such, the uncertainty that comes with technological innovation has a heightened influence on the supply chain. Few studies have explicitly investigated the influence of technological innovation on the anti-epidemic supply chain under the COVID-19 pandemic. Hence, the current research aims to investigate the influences of the uncertainty caused by technological innovation on the supply chain from demand and supply, shortage penalty, and budget. This paper presents a three-level model of the anti-epidemic supply chain under technological innovation and employs an interval data robust optimization to tackle the uncertainties of the model. The findings are obtained as follows. Firstly, the shortage penalty will increase the costs of the objective function but effectively improve demand satisfaction. Secondly, if the shortage penalty is sufficiently large, the minimum demand satisfaction rate can ensure a fair distribution of materials among the affected areas. Thirdly, technological innovation can reduce costs. The technological innovation related to the transportation costs of the anti-epidemic material distribution center has a greater influence on the optimal value. Meanwhile, the technological innovation related to the transportation costs of the supplier has the least influence. Fourthly, both supply and demand uncertainty can influence costs, but demand uncertainty has a greater influence. Fifthly, the multi-scenario budgeting approach can decrease the calculation complexity. These findings provide theoretical support for anti-epidemic dispatchers to adjust the conservativeness of uncertain parameters under the influence of technological innovation. [ABSTRACT FROM AUTHOR]

Industry 4.0 represents the most advanced stage of organization of industrial companies, allowing them to respond to an uncertain and changing environment, particularly as accentuated by the recent crisis resulting from COVID-19. Management innovation (MI) contributes to this process of permanent adaptation. The MI implementation phase is a critical step in MI generation that can generate many potential obstacles. This study focuses on these obstacles while considering the different activities (or subprocesses) embedded in this phase and the different actors involved in this complex process. We conducted a longitudinal case study in real time to investigate the implementation of MI internally generated by a multinational industrial company. Our results show that the obstacles encountered during the MI implementation phase may differ depending on the different activities and actors of this phase, thus leading us to question current implementation frameworks. This paper contributes by refining the theoretical model of MI generation and providing a better understanding of the obstacles that occur during the MI implementation phase. From a managerial perspective, this paper highlights key management principles to overcome the obstacles identified. [ABSTRACT FROM AUTHOR]

In this paper, we utilize the Chicago Board Option Exchange (CBOE) implied volatility indices to estimate the time-frequency information transmission among financial markets from 01.08.2008 to 31.10.2019. In doing so, we utilize the rolling window wavelet correlation (RWWC), Diebold & Yilmaz (The Economic Journal 119: 158–171, 2012), and Barunik & Krehlik (Journal of Financial Econometrics 16: 271–296, 2018). Our empirical findings suggest short-term and long-term dynamic connectedness between implied volatility indices of alternative assets. The long-term analysis findings suggest potential hedging and diversification opportunities that can be exploited by taking offsetting positions across volatility indices. The findings confirm heterogeneity between short-term and long-term connectedness results. Our findings also show superior out of sample hedging effectiveness of GVZ. The implications of the findings are further discussed in the paper. [ABSTRACT FROM AUTHOR]

This paper examines the impact of data-driven innovation (DDI) on firm performance, based on an exploratory case study of a manufacturing firm in China's textile and apparel industry. It explores the influence of various contextual variables on the firm's DDI and suggests ways to enhance DDI and thereby firm performance. Extending the literature on DDI, the paper proposes and validates a theoretical framework that incorporates the influence of various contextual factors on firms' DDI. The findings show that (1) individual context is associated with DDI; (2) organizational context is associated with DDI; and (3) DDI is associated with firm performance. This paper extends our understanding of how firm performance can be improved through DDI and shows that DDI should match a firm's contextual environment. [ABSTRACT FROM AUTHOR]

This paper aims to understand the impact of big data analytics on the retail supply chain. For doing so, we set our context to select the best big data practices amongst the available alternatives based on retail supply chain performance. We have applied TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) for the selection of the best big data analytics tools among the identified nine practices (data science, neural networks, enterprise resource planning, cloud computing, machine learning, data mining, RFID, Blockchain and IoT and Business intelligence) based on seven supply chain performance criteria (supplier integration, customer integration, cost, capacity utilization, flexibility, demand management, and time and value). One of the intriguing understandings from this paper is that most of the retail firms are in a dilemma between customer loyalty and cost while implementing the big data practices in their organization. This study analyses the dominance of the big data practices at the retail supply chain level. This helps the newly emerging retail firms in evaluating the best big data practice based on the importance and dominance of supply chain performance measures. [ABSTRACT FROM AUTHOR]

We consider the problem of a project manager of a matrix organization responsible for the timely completion of projects arriving over time and requiring the usage of a set of resources under his/her purview. A project is defined by a set of activities, precedence relations between activities, resource requirements, a customer due date, and a priority (a weight). Arriving projects are released to a flow control system that monitors the progress of activities and schedules the set of available activities, i.e., activities that are ready to be processed, as appropriate. A critical feature of such control systems is the decision process for choosing the next activity to seize a given resource. This is the focus of this paper. In the past several decades, various heuristic priority rules have been proposed in the literature to support this type of decision in differing settings such as the job shop problem and the deterministic resource-constrained project scheduling problem. A gap exists with respect to testing the various rules all together in the more realistic dynamic-stochastic multi-project environment when the objective is to minimize weighted project tardiness. The purpose of this paper is to fill this gap. Results show that the priority rule "Weighted Critical Ratio and Shortest Processing Time" (W (CR + SPT)) is the best performing rule with respect to minimizing weighted project tardiness. W (CR + SPT) is shown to be a variant of the family of "Modified Due Date" rules first introduced by Baker and Bertrand (J Oper Manage 1(3):37–42, 1982). Repeated application of Duncan's Multiple Range test demonstrates the robustness of our findings. For the environmental parameters (due date tightness, variation of expected activity durations, and utilization of resource), the W (CR + SPT) rule is dominant with respect to weighted project tardiness among the eleven priority rules tested. Only when the number of resources is very modest (either 1 or 3 resources) or under a purely parallel resource network is W (CR + SPT) not the dominant rule. In those cases, its variant W (CR + GSPT) is the best performing rule with respect to weighted project tardiness. [ABSTRACT FROM AUTHOR]

This paper considers the problem of scheduling independent jobs with release dates and tails on m identical machines to minimize the makespan. The three main bounds are the preemptive bound, the energetic bound and the Jackson Pseudo Preemptive bound. It is known that they are very close in practice. The aim of this paper is to provide worst case instances where the slack between them is maximal and to prove tight bounds on the slack. We show that the slack is smaller than the maximal processing time. [ABSTRACT FROM AUTHOR]

Project risk management which has been rarely considered, especially in software projects, is a crucial process to complete projects successfully. This paper aims to propose a novel project risk management framework both to evaluate the project risks and to predict the success or failure of software projects based on their risk. This new framework uses Machine Learning (ML) and Multi-Attribute Decision-Making under a cloud model to effectively manage uncertainty. Based on the proposed framework, in the first stage, the important risks of the software projects are identified by an organized approach. Then, the risks are evaluated based on their probability and impacts on time, cost, and quality. In the second stage, the obtained results of the previous stage are entered into a new COPRAS method under the cloud model to rank the risks. Then, the risks are classified into various groups according to their rank. It helps project managers to gain a profound awareness of their high-priority project risks. In this paper, data on risks for fifty software projects has been collected. All the steps of the second stage are implemented on these projects in order to assess their risks. As a result, a data set whose features are nine types of software project risks and the label of success or failure of the projects is created. To recognize the pattern between risks' values and the success or failure of the projects, various efficient ML algorithms such as Naive Bayes, Logistic regression, Decision Tree, Bagging, Random Forest, and AdaBoost are applied. This framework can predict the success or failure of software projects based on their risks with good accuracy. The results depict that the Naive Bayes algorithm has the best results compared to others. [ABSTRACT FROM AUTHOR]

In some production processes, the effort associated with a certain activity for its execution can vary over time. In this case, the amount of work per time unit devoted to each activity, so as its duration, is not univocally determined. This kind of problem can be represented by an activity project network with the so-called feeding precedence relations, and activity variable execution intensity. In this paper, we propose a forward recursion algorithm able to find the earliest start and finish times of each activity, in O (m log n) time, with n and m being the number of activities and the number of precedence relations, respectively. In particular, this requires the calculation of the (optimal) execution intensity profile, for each activity, that warrants the earliest start schedule and the minimum completion time of the project. [ABSTRACT FROM AUTHOR]

In this paper, two dynamic models, recently proposed to describe the adaptive repeated choices of a boundedly rational consumer, are joined together. One considers a consumer adjusting the consumption level of a given good over time according to the observed discrepancy between expected and realized utility gain and modifies the utility function according to past consumption experience, also including saturation effects when past consumption is excessive. The other one considers the same adjustment mechanism with constant preferences but with a behavioral effect that introduces a tendency (or bias) to imitate a reference group of consumers. Merging these two models, a two-dimensional nonlinear dynamical system is obtained which describes consumers that decide their next period consumption of a given good by following two different (sometimes contrasting) criteria: their own utility maximization on the one side and imitation of a reference group of consumers on the other side. This leads to a greater uncertainty with respect to the model without the behavioral bias. Such uncertainty is studied through a numerical exploration of the long-run dynamics, guided by some global dynamical features of the nonlinear model, such as the folding action of the critical curves that characterize the behavior of the iterated noninvertible map and the singularities related to the presence of a vanishing denominator, namely focal points and prefocal curves. So, the aim of the paper is twofold: on the one side, it tries to contribute to the literature on the economic theory of boundedly rational consumers represented by evolutionary and behavioral approaches; On the other side, it tries to contribute to the recent literature about the global analysis of discrete dynamical systems characterized by contact bifurcations leading to the creation of complex topological structures of the attractors and their basins of attraction. [ABSTRACT FROM AUTHOR]

In this paper we analyze the role of pollution for industry location and residence choice. We present a new economic geography (NEG) model in which manufacturing generates local pollution (that does not accumulate) and uses two types of labour input: unskilled workers that cannot migrate and work where they live; and high-skilled entrepreneurs that choose where to produce and where to live. Taking on board costless commuting or, in alternative, distance working, entrepreneurs can live in a different location from production. Both types of households enjoy utility from consuming all commodities (locally and imported variants) and suffer from local pollution. The resulting model is of the footloose entrepreneur variant, but involves two dynamic equations: the standard one governing the residential choice of entrepreneurs, and another one governing where production is located. The current paper analyses the discrete time dynamic process defined by a two-dimensional piecewise smooth map. Depending on parameters this map can have possibly coexisting attractors of various types (fixed points, cycles, closed curves as well as chaotic attractors). We analytically obtain stability conditions for the fixed points. Using numerical methods we describe also some global dynamic properties of the considered map. Finally, we propose an economic interpretation of the results concerning local stability analysis and global dynamics. [ABSTRACT FROM AUTHOR]

Critical chain buffer management (CCBM) has been extensively studied in recent years. This paper investigates a new formulation of CCBM, the multimode chance-constrained CCBM problem. A flow-based mixed-integer linear programming model is described and the chance constraints are tackled using a scenario approach. A reinforcement learning (RL)-based algorithm is proposed to solve the problem. A factorial experiment is conducted and the results of this study indicate that solving the chance-constrained problem produces shorter project durations than the traditional approach that inserts time buffers into a baseline schedule generated by solving the deterministic problem. This paper also demonstrates that our RL method produces competitive schedules compared to established benchmarks. The importance of solving the chance-constrained problem and obtaining a project buffer tailored to the desired probability of completing the project on schedule directly from the solution is highlighted. Because of its potential for generating shorter schedules with the same on-time probabilities as the traditional approach, this research can be a useful aid for decision makers. [ABSTRACT FROM AUTHOR]

The paper analyzes the optimal lockdown policy using the SQAIRD model over a network with three population groups (young, adult, and old). We show that different lockdown policies may be justified by different socioeconomic structures (objective cost functions that are either convex or concave). We also show that a lockdown policy is always better than a laissez-faire policy, and a targeted policy specific to each group outperforms a uniform policy. In our benchmark example, we consider the case of Italy. Our simulations show that: (a) a lockdown policy is always better than the laissez-faire policy because it limits the costs generated by the pandemic in an uncontrolled situation; (b) a group-specific targeted lockout policy is more effective than a uniform policy to the extent that the groups differ. The latter is a less expensive targeted policy (as it optimally minimizes direct, indirect, and vaccination costs), and it is equally effective in controlling the pandemic. One finding of particular interest is that the optimal lockdown rate should be higher for the young and elderly than for adults. This is motivated by the fact that younger individuals are more likely to spread the virus in question asymptomatically. [ABSTRACT FROM AUTHOR]

This paper assesses the robustness of the "natalist bias" of pollution control in de la Croix and Gosseries (J Environ Econ Manage 63:271–287, 2012), according to which taxing emissions encourage agents to shift from production to procreation, further deteriorating the environment and gradually impoverishing the next generations. We relax the assumptions that human capital does not depend on environmental quality and that society does not allocate resources to pollution control. Using a similar Overlapping Generations (OLG) growth model, our findings indicate that taxation does not necessarily encourage agents to permanently shift away from production because living under better environmental conditions enhances productivity through human capital formation. As the government increases the emissions price, agents reduce consumption and education spending, hurting output in the short term. However, in the long run, the reduction in emissions that follows taxation more than compensates for the initial adverse effects, provided that the sensitivity of human capital accumulation to environmental degradation is strong enough. Furthermore, as we increase the coefficient capturing such pollution externality, a Neimark–Sacker bifurcation occurs, making the system compatible with persistent endogenous fluctuations. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the delayed-advanced Kaldorian business cycle model and consider how time-delay and time-advance affect economic fluctuations. Given the asymptotic stability of the original Kaldor model, we first show that an approximated Kaldor model by a Taylor series expansion can accurately describe the dynamics of the delayed Kaldor model. We also confirm that the delay has a destabilizing effect. When time-delay is replaced with time-advance, we have an advanced Kaldor model. Taking the advanced capital stock formulation, we derive the stability conditions and find that the time-advance has a stabilizing effect. Lastly, we examine these opposite-signed effects in a modified delayed-advanced model. [ABSTRACT FROM AUTHOR]

In the present paper, we investigate the complex dynamics arising from a behavioral exchange rate discontinuous model with heterogeneous agents. Unlike previous works explaining the emergence of chaos in the exchange rate models as the resulting of nonlinearity, our model is able to produce endogenous exchange rate dynamics due to the presence of discontinuity induced by a sentiment index, which affects the way investors take their trading decisions. In particular, it affects the level of optimism/pessimism of fundamentalists regarding their perception on the value of fundamental. Moreover, it also affects the strength to which one kind of chartists places her buying/selling orders. We show that our model, represented by a two-dimensional discontinuous map, has the ability to produce interesting endogenous exchange rate dynamics. In addition, when each component of the map is buffeted by a stochastic component, the model closely replicates the stylized facts of the EUR/USD and EUR/JPY exchange rate markets. [ABSTRACT FROM AUTHOR]

Forecasting an ongoing project's actual duration is an essential aspect of project management which received considerable attention in the research community. In studies using Earned Value Management forecasting, it has been argued that the network topology is a driver to indicate the accuracy of these forecasts. However, a new project indicator has been recently defined, i.e. the project regularity, which reflects the value accrue according to the plan. It has shown to outperform the serial/parallel network topology indicator in specifying the accuracy of project forecasts. This paper introduces a novel way to define the project regularity, which provides project managers with an improved indication of the expected forecasting accuracy for their projects. The study is carried out on an empirical database consisting of 100 projects from different sectors, and the results are compared to the academic literature. The experiments show that the new indicator provides a better categorisation compared to the existing approaches. Further, they have shown that the ability of project categorisers to indicate the expected forecasting accuracy is affected by industry sector and project size. [ABSTRACT FROM AUTHOR]

Budget allocation in project risk response is a vital issue in project risk management since it relates to a reasonable utilization of strict project budgets and effective mitigations of the risks that typify projects. This paper presents an integrated method based on an optimization model and fault tree analysis for allocating a risk response budget from a preventive and protective perspective. The proposed method consists of three main steps. The first step is to analyse and calculate risk probabilities and risk losses which involves identifying risk causes that may trigger a risk event to occur using fault tree analysis. It also identifies consequences once the risk event occurs, evaluates the occurrence probabilities of risk causes and expected financial losses of consequences. The second step is to build a relationship between the budget allocated to risk response strategies and the corresponding response effects. The third step is to construct an optimization model aiming at minimizing the total risk cost. We present proofs for the optimal risk response strategy in special cases of the budget allocation model. Furthermore, a detailed computational experiment is performed to gain insights into the three-phased budget allocation model for more general cases. The results show that an optimal risk response budget can be determined and the structure of the risk network has a significant impact on the preferred risk response strategy. [ABSTRACT FROM AUTHOR]

In this paper, we study unimodality conditions for distributions that describe markets with stochastic demand. Such conditions naturally emerge in the analysis of game-theoretic models of market competition (Cournot games) and supply chain coordination (Stackelberg games). We express the price elasticity of expected demand in terms of the mean residual life (MRL) function of the demand distribution and characterize optimal prices or equivalently, points of unitary elasticity, as fixed points of the MRL function. This leads to economic interpretable conditions on the demand distribution under which such fixed points exist and are unique. We find that markets with increasing price elasticity of expected demand that eventually become elastic correspond to distributions with decreasing generalized mean residual life (DGMRL) and finite second moment. DGMRL distributions strictly generalize the widely used increasing generalized failure rate (IGFR) distributions. We further elaborate on the relationship of the two classes, link their limiting behavior at infinity and examine moment and closure properties of DGMRL distributions that are important in economic applications. The DGMRL unimodality condition is useful in the analysis of optimal decisions under uncertainty in settings that are not covered by the widely-used IGFR condition; thus, it can be of broader interest to the game-theory and operations research literature. [ABSTRACT FROM AUTHOR]

The study on how equilibria behave when perturbations occur in the data of a game is a fundamental theme, since actions and payoffs of the players may be affected by uncertainty or trembles. In this paper, we investigate the asymptotic behavior and the variational stability of the subgame perfect Nash equilibrium (SPNE) in one-leader one-follower Stackelberg games under perturbations both of the action sets and of the payoff functions. To pursue this aim, we consider a general sequence of perturbed Stackelberg games and a set of assumptions that fit the usual types of perturbations. We study if the limit of SPNEs of the perturbed games is an SPNE of the original game and if the limit of SPNE-outcomes of perturbed games is an SPNE-outcome of the original game. We fully positively answer when the follower's best reply correspondence is single-valued. When the follower's best reply correspondence is not single-valued, the answer is positive only for the SPNE-outcomes; whereas the answer for SPNEs may be negative, even if the perturbation does not affect the sets and affects only one payoff function. However, we show that under suitable non-restrictive assumptions it is possible to obtain an SPNE starting from the limit of SPNEs of perturbed games, possibly modifying the limit at just one point. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a notion of mergeable weighted majority games with the aim of providing the first characterization of the Colomer–Martínez power index (Colomer and Martínez in J Theor Polit 7(1):41–63, 1995). Furthermore, we define and characterize a new power index for the family of weighted majority games that combines ideas of the Public Good (Holler in Polit Stud 30(2):262–271, 1982) and Colomer–Martínez power indices. Finally, we analyze the National Assembly of Ecuador using these and some other well-known power indices. [ABSTRACT FROM AUTHOR]

To achieve a competitive advantage, corporations are growingly adopting strategies to effectively promote their market demand. Trade credit payment and pricing strategies provided by corporates can efficiently influence customers' purchasing behavior. Although granting a trade credit strategy can increase corporations' market share, such a strategy is a risk-based financing program for corporations. Therefore, corporates should choose whether to use trade credit financing in their long-term. This paper proposes an analytical model to investigate the evolutionary behaviors of retailers regarding pricing and trade credit strategies in the long term. In the study under investigation, retailers can use two financing strategies: risk-based trade credit and non-trade credit (i.e., pricing). This study provides both numerical and analytical findings. Our findings demonstrate that the risk-based trade credit strategy is the stationary financing solution for retailers in the long term. The result indicates that when customers are financially constrained, providing a trade credit scheme to customers is a successful marketing policy in both short-term and long-term frameworks. [ABSTRACT FROM AUTHOR]

We consider a distributed computing setting wherein a central entity seeks power from computational providers by offering a certain reward in return. The computational providers are classified into long-term stakeholders that invest a constant amount of power over time and players that can strategize on their computational investment. In this paper, we model and analyze a stochastic game in such a distributed computing setting, wherein players arrive and depart over time. While our model is formulated with a focus on volunteer computing, it equally applies to certain other distributed computing applications such as mining in blockchain. We prove that, in Markov perfect equilibrium, only players with cost parameters in a relatively low range which collectively satisfy a certain constraint in a given state, invest. We infer that players need not have knowledge about the system state and other players' parameters, if the total power that is being received by the central entity is communicated to the players as part of the system's protocol. If players are homogeneous and the system consists of a reasonably large number of players, we observe that the total power received by the central entity is proportional to the offered reward and does not vary significantly despite the players' arrivals and departures, thus resulting in a robust and reliable system. We then study by way of simulations and mean field approximation, how the players' utilities are influenced by their arrival and departure rates as well as the system parameters such as the reward's amount and dispensing rate. We observe that the players' expected utilities are maximized when their arrival and departure rates are such that the average number of players present in the system is typically between 1 and 2, since this leads to the system being in the condition of least competition with high probability. Further, their expected utilities increase almost linearly with the offered reward and converge to a constant value with respect to its dispensing rate. We conclude by studying a Stackelberg game, where the central entity decides the amount of reward to offer, and the computational providers decide how much power to invest based on the offered reward. [ABSTRACT FROM AUTHOR]

This paper studies a simple process of demand adjustment in cooperative games. In the process, a randomly chosen player makes the highest possible demand subject to the demands of other coalition members being satisfied. This process converges to the aspiration set; in convex games, this implies convergence to the core. We further introduce perturbations into the process, where players sometimes make a higher demand than feasible. These perturbations make the set of separating aspirations, i.e., demand vectors in which no player is indispensable in order for other players to achieve their demands, the one most resistant to mutations. We fully analyze this process for 3-player games. We further look at weighted majority games with two types of players. In these games, if the coalition of all small players is winning, the process converges to the unique separating aspiration; otherwise, there are many separating aspirations and the process reaches a neighbourhood of a separating aspiration. [ABSTRACT FROM AUTHOR]

This paper deals with α -core of set payoffs games with finitely many players and finite coalition α -core of set payoffs games with infinitely many players. We present an existence theorem of α -core of set payoffs games with finitely many players. By virtue of finite intersection property, we also establish an existence theorem of finite coalition α -core of set payoffs games with infinitely many players. Besides, we obtain some stability results of solution mapping of α -core of set payoffs games, where set payoff functions and action sets of agents involve perturbed parameters. Moreover, as applications, we obtain the corresponding results for α -core of vector payoffs games. [ABSTRACT FROM AUTHOR]

In this paper, we examine how mutual fund managers behave to fluctuations in market volatility. We use a sample of daily return from countries around the world to evaluate how manager perform to publicly available information. There is a lack of empirical studies that examine the relation between conditional market returns and conditional volatility on a global scale; we provide evidence across countries to answer this question. Our study provides new evidence about conditional mutual fund performance across countries. We find that during periods of high market volatility mutual funds reduce market exposure across all countries; this implies that systemic risk is particularly sensitive to changes in market volatility around the world. [ABSTRACT FROM AUTHOR]

This paper introduces the new class of two-bound core games, where the core can be described by a lower bound and an upper bound on the payoffs of the players. Many classes of games turn out to be two-bound core games. We show that the core of each two-bound core game can be described equivalently by the pair of exact core bounds, and study to what extent the exact core bounds can be stretched while retaining the core description. We provide explicit expressions of the nucleolus for two-bound core games in terms of all pairs of bounds describing the core, using the Talmud rule for bankruptcy problems, and study to what extent these expressions are robust against game changes. [ABSTRACT FROM AUTHOR]

We carry on a long term analysis for Bitcoin price, which is currently among the most renowned crypto assets available on markets other than Forex. In the last decade Bitcoin has been under spotlights among traders all world wide, both because of its nature of pseudo–currency and for the high volatility its price has frequently experienced. Considering that Bitcoin price has earned over five orders of magnitude since 2009, the interest of investors has been increasingly motivated by the necessity of accurately predicting its value, not to mention that a comparative analysis with other assets as silver and gold has been under investigation, too. This paper reports two approaches for a long term Bitcoin price prediction. The first one follows more standard paradigms from regression and least squares frameworks. Our main contribution in this regard fosters conclusions which are able to justify the cyclic performance of Bitcoin price, in terms of its Stock–to–Flow. Our second approach is definitely novel in the literature, and indicates guidelines for long term forecasts of Bitcoin price based on Machine Learning (ML) methods, with a specific reference to Support Vector Machines (SVMs). Both these approaches are inherently data–driven, and the second one does not require any of the assumptions typically needed by solvers for classic regression problems. [ABSTRACT FROM AUTHOR]

The paper Zhao et al. (Ann Oper Res 226:727–739, 2015) shows that mean-CVaR-skewness portfolio optimization problems based on asymetric Laplace (AL) distributions can be transformed into quadratic optimization problems for which closed form solutions can be found. In this note, we show that such a result also holds for mean-risk-skewness portfolio optimization problems when the underlying distribution belongs to a larger class of normal mean–variance mixture (NMVM) models than the class of AL distributions.We then study the value at risk (VaR) and conditional value at risk (CVaR) risk measures of portfolios of returns with NMVM distributions.They have closed form expressions for portfolios of normal and more generally elliptically distributed returns, as discussed in Rockafellar and Uryasev (J Risk 2:21–42, 2000) and Landsman and Valdez (N Am Actuar J 7:55–71, 2003). When the returns have general NMVM distributions, these risk measures do not give closed form expressions. In this note, we give approximate closed form expressions for the VaR and CVaR of portfolios of returns with NMVM distributions.Numerical tests show that our closed form formulas give accurate values for VaR and CVaR and shorten the computational time for portfolio optimization problems associated with VaR and CVaR considerably. [ABSTRACT FROM AUTHOR]

Previous literature shows that prevalent risk measures such as value at risk or expected shortfall are ineffective to curb excessive risk-taking by a tail-risk-seeking trader with S-shaped utility function in the context of portfolio optimisation. However, these conclusions hold only when the constraints are static in the sense that the risk measure is just applied to the terminal portfolio value. In this paper, we consider a portfolio optimisation problem featuring S-shaped utility and a dynamic risk constraint which is imposed throughout the entire trading horizon. Provided that the risk control policy is sufficiently strict relative to the Sharpe ratio of the asset, the trader's portfolio strategies and the resulting maximal expected utility can be effectively constrained by a dynamic risk measure. Finally, we argue that dynamic risk constraints might still be ineffective if the trader has access to a derivatives market. [ABSTRACT FROM AUTHOR]

In the last decades, the production of fuel ethanol from corn has spread as a valid renewable alternative to pursue sustainability goals. However the uncertain nature of both input (corn) and output (gasoline) prices, together with price dependent operational decisions, combine to make this difficult plant valuation require a real options approach. Moreover, this project is characterized by various sequential stages that contribute to increase its valuation difficulties. The purpose of this paper is to provide a reliable valuation methodology of a corn ethanol plant project able to consider the characteristics of the project. We apply the compound Real Options Approach to price a corn ethanol plant project considering that the corn and gasoline prices both follow a skew-geometric Brownian motion. We also propose a case study to show a real implementation of our theoretical model. The results show that the corn ethanol plant is financially attractive as renewable investment since the uncertainties inherent in the project add value, via managerial flexibility, to the real option valuation. [ABSTRACT FROM AUTHOR]

To profit from price oscillations, investors frequently use threshold-type strategies where changes in the portfolio position are triggered by some indicators reaching prescribed levels. In this paper we investigate threshold-type strategies in the context of ergodic control. We make the first steps towards their optimization by proving ergodic properties of related functionals. Assuming Markovian price increments satisfying a minorization condition and (one-sided) boundedness we show, in particular, that for given thresholds, the distribution of the gains converges in the long run. We also extend recent results on the stability of overshoots of random walks from the i.i.d. increment case to Markovian increments, under suitable conditions. [ABSTRACT FROM AUTHOR]

In this paper we deal with the optimal bankruptcy problem for agents who can optimally allocate their consumption rate, the amount of capital invested in the risky asset, as well as their leisure time. In our framework, the agents are endowed by an initial debt, and they are required to repay their debt continuously. Declaring bankruptcy, the debt repayment is exempted at the cost of a wealth shrinkage. We implement the duality method to solve the problem analytically and conduct a sensitivity analysis to the bankruptcy cost and benefit parameters. Introducing the flexible leisure/working rate, and therefore the labour income, into the bankruptcy model, we investigate its effect on the optimal strategies. [ABSTRACT FROM AUTHOR]

This paper characterizes the probability of a market failure defined as the default of two or more globally systemically important banks (G-SIBs) in a small interval of time. The default probabilities of the G-SIBs are correlated through the possible existence of a market-wide stress event. The characterization employs a multivariate Cox process across the G-SIBs, which allows us to relate our work to the existing literature on intensity-based models. Various theorems related to market failure probabilities are derived, including the probability of a market failure due to two banks defaulting over the next infinitesimal interval, the probability of a catastrophic market failure, the impact of increasing the number of G-SIBs in an economy, and the impact of changing the initial conditions of the economy's state variables. We also show that if there are too many G-SIBs, a market failure is inevitable, i.e., the probability of a market failure tends to 1. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of transition risk on a firm's low-carbon production. As the world is facing global climate change, the Intergovernmental Panel on Climate Change (IPCC) has set the idealized carbon-neutral scenario around 2050. In the meantime, many carbon reduction scenarios, known as Shared Socioeconomic Pathways (SSPs) have been proposed in the literature for different production sectors in a more comprehensive socio-economic context. We consider, on the one hand, a firm that aims to optimize its emission level under the double objectives of maximizing its production profit and respecting the emission mitigation scenarios. Solving the penalized optimization problem provides the optimal emission according to a given SSP benchmark. On the other hand, such transitions affect the firm's credit risk. We model the default time by using the structural default approach. We are particularly concerned with how, by following different SSPs scenarios, the adopted strategies may influence the firm's default probability. [ABSTRACT FROM AUTHOR]

This paper addresses errors in mean return estimates in continuous-time asset allocation models. A standard approach postulates that stochastic factors explain expected asset returns. The problem is then to estimate these factors from observed asset prices via filtering. Recent advances have also combined asset prices with expert opinions to improve the estimates. However, these methods have limitations: stocks prices favor momentum strategies, and expert opinions require careful debiasing. To resolve these issues, we propose a jump-diffusion risk-sensitive benchmarked asset management model in which investors estimate the factors from both traditional and alternative data. We show that this model admits a unique C 1 , 2 solution, and we derive the optimal investment policy in quasi-closed form. We find that investors construct their portfolios from a passive core and an active satellite. The passive core adds considerations for jump risk to a simple benchmark replication. The active satellite blends security selection, and factor tilts with event-driven strategies unique to jump-diffusion problems. Thus, our model explains the most popular investment strategies. Furthermore, the improved expert forecast model and the introduction of alternative data provide factor tilters with new tools to sharpen their asset allocation. [ABSTRACT FROM AUTHOR]

In this paper we explore optimal liquidation in a market populated by a number of heterogeneous market makers that have limited inventory-carrying and risk-bearing capacity. We derive a reduced form model for the dynamics of their aggregated inventory considering a proper scaling limit. The resulting price impact profile is shown to depend on the characteristics and relative importance of their inventories. The model is flexible enough to reproduce the empirically documented power law behavior of the price impact function. For any choice of the market makers characteristics, optimal execution within this modeling approach can be recast as a linear-quadratic stochastic control problem. The value function and the associated optimal trading rate can be obtained semi-explicitly subject to solving a differential matrix Riccati equation. Numerical simulations are conducted to illustrate the performance of the resulting optimal liquidation strategy in relation to standard benchmarks. Remarkably, they show that the increase in performance is determined by a substantial reduction of higher order moment risk. [ABSTRACT FROM AUTHOR]

The anticipative information refers to some information about future events that may be disclosed in advance. This information may regard, for example, financial assets and their future trends. In our paper, we assume the existence of some anticipative information in a market whose risky asset dynamics evolve according to a Brownian motion and a Poisson process. Using Malliavin calculus and filtration enlargement techniques, we derive the information drift of the mentioned processes and, both in the pure jump case and in the mixed one, we compute the additional expected logarithmic utility. Many examples are shown, where the anticipative information is related to some conditions that the constituent processes or their running maximum may verify, in particular, we show new examples considering Bernoulli random variables. [ABSTRACT FROM AUTHOR]

Most of the existing literature on optimal trade execution in limit order book models assumes that resilience is positive. But negative resilience also has a natural interpretation, as it models self-exciting behaviour of the price impact, where trading activities of the large investor stimulate other market participants to trade in the same direction. In the paper we discuss several new qualitative effects on optimal trade execution that arise when we allow resilience to take negative values. We do this in a framework where both market depth and resilience are stochastic processes. [ABSTRACT FROM AUTHOR]

In this paper, we study the pricing of contracts in fixed income markets under volatility uncertainty in the sense of Knightian uncertainty or model uncertainty. The starting point is an arbitrage-free bond market under volatility uncertainty. The uncertainty about the volatility is modeled by a G-Brownian motion, which drives the forward rate dynamics. The absence of arbitrage is ensured by a drift condition. Such a setting leads to a sublinear pricing measure for additional contracts, which yields either a single price or a range of prices and provides a connection to hedging prices. Similar to the forward measure approach, we define the forward sublinear expectation to simplify the pricing of cashflows. Under the forward sublinear expectation, we obtain a robust version of the expectations hypothesis, and we show how to price options on forward prices. In addition, we develop pricing methods for contracts consisting of a stream of cashflows, since the nonlinearity of the pricing measure implies that we cannot price a stream of cashflows by pricing each cashflow separately. With these tools, we derive robust pricing formulas for all major interest rate derivatives. The pricing formulas provide a link to the pricing formulas of traditional models without volatility uncertainty and show that volatility uncertainty naturally leads to unspanned stochastic volatility. [ABSTRACT FROM AUTHOR]

We analyze the problem of constructing multiple buy-and-hold mean-variance portfolios over increasing investment horizons in continuous-time arbitrage-free stochastic interest rate markets. The orthogonal approach to the one-period mean-variance optimization of Hansen and Richard (Econometrica 55(3):587–613, 1987) requires the replication of a risky payoff for each investment horizon. When many maturities are considered, a large number of payoffs must be replicated, with an impact on transaction costs. In this paper, we orthogonally decompose the whole processes defined by asset returns to obtain a mean-variance frontier generated by the same two securities across a multiplicity of horizons. Our risk-adjusted mean-variance frontier rests on the martingale property of the returns discounted by the log-optimal portfolio and features a horizon consistency property. The outcome is that the replication of a single risky payoff is required to implement such frontier at any investment horizon. As a result, when transaction costs are taken into account, our risk-adjusted mean-variance frontier may outperform the traditional mean-variance optimal strategies in terms of Sharpe ratio. Realistic numerical examples show the improvements of our approach in medium- or long-term cashflow management, when a sequence of target returns at increasing investment horizons is considered. [ABSTRACT FROM AUTHOR]