Forecasting,uncertainty and risk; perspectives on clinical decision-making in preventive and curative medicine

Should we screen the population routinely for the presence of breast or prostatic cancer? This simple proposition masks a landscape of complexities, including the economics of screening, the prevalence and probability of the disease, desired frequencies of intervention and a willingness to adopt preventive screening. These ‘risk factors’ form part of a broader landscape of uncertainty that characterises the intricacies of clinical decision-making. Deepening our understanding of the way in which clinicians evaluate risk and uncertainty requires a framing of insights into language, communication, psychology and epistemology. This paper explores perspectives in forecasting and uncertainty as a basis for understanding individuals’ perceptions of risk when assessing their options in both preventative and curative medicine. At the heart of the issue is the prevalence of diagnostic error; this paper argues for the use of systematised approaches to medical error management and inclusive approaches to research in the field. The advantages of augmenting the management of unforeseen consequences through an improved understanding of the issues that are of concern to patients, carers, and medical practitioners alike forms a key construct in this paper. We conclude with an exploration of potential opportunities for improvements in medical practice: changes that may reduce the disbenefits of uncertainty and enhance the management of the general risks associated with clinical decision-making.     

Dynamic interaction of risk–return trade-offs between oil market and China’s stock market: An analysis from the risk preferences perspective

The risk–return trade-off refers to the compensation required by investors for bearing risks, which can be viewed as the risk preference of investors in a market. The current study investigates the dynamic interdependence of risk–return trade-offs between China’s stock market and the crude oil market from the perspective of risk preference of investors, which is designed to explore the transmission process of investors’ risk preference in both markets. Specifically, this study applies the time-varying parameter GARCH-M model, namely TVP-GARCH-M model, to characterize the time-dependent risk–return trade-offs (investors’ risk preferences) in the crude oil and China’s stock markets, then examines their relationship through Granger causality tests. Results show that a variation in risk preferences of the oil market investors can dramatically cause a variation in risk preferences of the Chinese stock market investors, while the risk preference of investors in the Chinese stock market does not lead to that in the crude oil market, which is in accordance with expectations. The dynamic effect of investors’ risk appetite in the crude oil market is further examined by the TVP-VAR model. The findings of this work suggest that there generally exists a positive impact of investors’ risk preference in the oil market and that the effect is time-varying to a greater degree during the short and medium term. Moreover, responses of the Chinese stock market investors’ risk preference were more significant during the 2008 financial crisis. Additionally, the empirical results remain robust when applying alternative crude oil prices and China’s stock prices.     

The Politics of Standards in Modern Management: Making ‘The Project’ a Reality*

abstract In this paper, we explore the standardization of contemporary management knowledge, focusing in particular upon the role of ‘standards’ in creating and reifying ‘organizational objects’, with powerful consequences and with often unrecognized ethical implications. It is our argument that modernist beliefs in ‘general, abstract and timeless ideas’ ( Brunsson et al., 2000 , p. 173), enshrined in a universal and abstract rationality, results in the marginalization of more reflexive forms of rationality and the suppression of autonomy, creativity and discretion in organizations. To investigate the consequences of standardization, we take as the focus of our analysis a specific management model which has a significant and growing impact on many sectors of contemporary industry; that of project management. Drawing on the work of Timmermans and Berg (1997) , Bowker and Star (1999) and Brunsson et al. (2000) , we draw attention to the reification of the object of management; in this case, the project itself, as a transhistorical, ‘real world’ object. By tracing efforts to establish and institutionalize ‘standards’ in this and other fields of management, in particular through the creation and dissemination of a universal ‘body of knowledge’ for this field, we draw attention to the political and moral significance of the ‘blackboxing’ of knowledge. It is our broader intention here to help to denaturalize this organizational object, to legitimize other modes of knowledge and practice in the field, and thereby to reopen debate in this and other arenas of standardization.     

Incentive Asymmetries in the Mergers and Acquisitions Process*

abstract This paper focuses on the relationship between incentive asymmetries and some potentially undesirable outcomes along the mergers and acquisitions (M&A) process which could potentially destroy the shareholder value of the merged corporate entity. Incentive asymmetries are seen as belonging to the three categories of ‘risk‐antecedent’, ‘information‐antecedent’ and ‘pure self‐interest antecedent’. We propose that incentive asymmetries are responsible for increases in the number of M&A projects and that they might create a ‘lemons problem’ with M&A candidates. Incentive asymmetries are also suggested to lead to prolonged contract‐writing phases, biased financial evaluations and acquisition price escalation, as well as undermined post‐M&A integration plans. If resolutions to these problems are sought, the use of high‐ and low‐powered incentive schemes will need to reflect risk, information and pure self‐interest.     

Metaphor and the Dynamics of Knowledge in Organization Theory: A Case Study of the Organizational Identity Metaphor*

abstract Despite the increased salience of metaphor in organization theory, there is still very little conceptual machinery for capturing and explaining how metaphor creates and/or reorders knowledge within organization theory. Moreover, prior work on metaphor has insufficiently accounted for the context of interpreting a metaphor. Many metaphors in organization theory, including the ‘organizational identity’ metaphor, have often been treated in singular and monolithic terms; seen to offer a similar or largely synonymous interpretation to theorists and researchers working along the entire spectrum of disciplines (e.g. organizational behaviour, organizational psychology) in organization theory. We argue in this paper that contextual variation however exists in the interpretation of metaphors in organization theory. This argument is developed by proposing and elaborating on a so‐called image‐schematic model of metaphor, which suggests that the image‐schemata (abstract imaginative structures) that are triggered by the metaphorical comparison of concepts may vary among individuals. Accordingly, once different schemata are triggered the completion and interpretation of a metaphor may equally vary among different individuals or, indeed, research communities. These points associated with the image‐schematic model of metaphor are illustrated with a case study of the ‘organizational identity’ metaphor. The case study shows that this particular metaphor has spiralled out into different research communities and has been comprehended in very different ways as different communities work from very different conceptions, or image‐schemata, of ‘organization’ and ‘identity’, and use different theoretical frameworks and constructs as a result. The implications of the image‐schematic view of metaphor for knowledge development and theoretical progress in organization theory are discussed.     

Covid-19 attacks the body of purchasing and supply management: A medical check of the immune system

Much like the immune system of the body, the ‘immune system’ of purchasing and supply management (PSM) is also affected by the Covid-19 virus. Medicine must hinder the spread of the virus and outbreak of disease, just as PSM must prevent risk events and handle supply disruptions. The existing debate on supply resilience and robustness can be demonstrated using this medical analogy. The purpose of this article was to perform a medical check of the ‘PSM immune system’ to identify lessons and research gaps when confronted with a low-frequency-high-impact event such as the pandemic. As a provocative note, this article identifies research gaps in elements of the immune system of PSM (e.g., helper cells – consultancy support or memory cells – feedback loops). The results call for a more holistic debate on the immune system of PSM. Two approaches for research on ‘conventional’ or ‘alternative’ risk management schools of thought are presented as a basis for future discourse on how to improve the PSM immune system.     

Role of Knowledge in Value Creation in Business Nets*

abstract This paper focuses on the role of knowledge in intentionally created business networks called nets. Nets are seen to offer firms collective benefits beyond those of a single firm or market transaction. We propose that the types of knowledge and learning required in the management of different types of business net are dependent on the value creation characteristics of the net types. Based on this we suggest a classification of three generic net types –‘current business nets’, ‘business renewal nets’, and ‘emerging new business nets’– and argue that they pose different conditions for management in nets. Using this framework and integrating notions from the industrial network approach, strategic management and dynamic capabilities view, and organizational learning we make a number of observations and propositions about the role of knowledge and learning in the three types of business net. The paper contributes to the emerging theory of network management.     

Forecasting extreme financial risk: A score-driven approach

This paper develops a new class of dynamic models for forecasting extreme financial risk. This class of models is driven by the score of the conditional distribution with respect to both the duration between extreme events and the magnitude of these events. It is shown that the models are a feasible method for modeling the time-varying arrival intensity and magnitude of extreme events. It is also demonstrated how exogenous variables such as realized measures of volatility can easily be incorporated. An empirical analysis based on a set of major equity indices shows that both the arrival intensity and the size of extreme events vary greatly during times of market turmoil. The proposed framework performs well relative to competing approaches in forecasting extreme tail risk measures.     

Industry analysis and investment decision-making under conditions of uncertainty

Evaluating risk is a key element in successful investment decision-making. A major risk in forecasting company performance is associated with projecting its cash flow streams by product-line which in turn is strongly related to the expected industry outlook and likely variability about this outlook. This paper sets out a methodology for evaluating this aspect of the investment decision by developing measures of expected real cash flow growth (reward) and expected annual variability of this growth (risk). These measures are constructed for 77 industry classifications at the two and three digit SIC (Standard Industrial Classification) level of detail by solving a dynamic input–output model under various economic secenarios. Aside from producing results which are relevant for producing company valuation bands, the analysis strongly suggests that perceptions of which industries are ‘winners’ and which are ‘losers’ are significantly altered when both ‘reward’ and ‘risk’ are used as criteria as opposed to either one alone. Finally, the methodology also produces results which measures the sources of cash flow growth in terms of relative price performance, productivity, and demand for output. Since there measures can be used as indicators of the quality of industry real cash flow growth, industry performance can be further arrayed within the broad categories of winning and losing industries. Several examples of how this is done are offered.     

Playing the synthesizer with Canadian data: Adding polls to a structural forecasting model

Election forecasting has become a fixture of election campaigns in a number of democracies. Structural modeling, the major approach to forecasting election results, relies on ‘fundamental’ economic and political variables to predict the incumbent’s vote share usually a few months in advance. Some political scientists contend that adding vote intention polls to these models—i.e., synthesizing ‘fundamental’ variables and polling information—can lead to important accuracy gains. In this paper, we look at the efficiency of different model specifications in predicting the Canadian federal elections from 1953 to 2015. We find that vote intention polls only allow modest accuracy gains late in the campaign. With this backdrop in mind, we then use different model specifications to make ex ante forecasts of the 2019 federal election. Our findings have a number of important implications for the forecasting discipline in Canada as they address the benefits of combining polls and ‘fundamental’ variables to predict election results; the efficiency of varying lag structures; and the issue of translating votes into seats.     

Density Nowcasts and Model Combination: Nowcasting Euro‐Area GDP Growth over the 2008–09 Recession*

Combined density nowcasts for quarterly Euro‐area GDP growth are produced based on the real‐time performance of component models. Components are distinguished by their use of ‘hard’ and ‘soft’, aggregate and disaggregate, indicators. We consider the accuracy of the density nowcasts as within‐quarter indicator data accumulate. We find that the relative utility of ‘soft’ indicators surged during the recession. But as this instability was hard to detect in real‐time it helps, when producing density nowcasts unknowing any within‐quarter ‘hard’ data, to weight the different indicators equally. On receipt of ‘hard’ data for the second month in the quarter better calibrated densities are obtained by giving a higher weight in the combination to ‘hard’ indicators.     

Unmanning the homeland

This article traces some of the history of and logic behind the automation of weapons systems in relation to their planned deployment as an integral element of ‘homeland security’ in the US after the September 11th attacks. We focus primarily on unmanned combat air vehicles that have long been part of the Air Force’s R&D plans, but have now apparently found an event that justifies their production and deployment. What, we wonder, will be the effects on the urban landscape by using unmanned vehicles capable of ‘intelligently’ selecting a target and firing upon it, especially when the technology that allows these weapons systems to find the target as satellite coordinates relies on the same information technology that provides the infrastructure for ‘smart’ communities, and are already deployed in virtually all urban settings. By exploring the logic of speed and surprise that has driven the increased move toward and reliance on automated and intelligent weapons systems, we reveal how the arguments used in favor of developing and deploying such weapons systems also work as arguments against them. The technology that makes a weapons system intelligent, for example, also means that it has to be capable of choosing the wrong target, hence its ‘intelligence’. Similarly, the need to control the element of surprise has its logical end in the pursuit of the ultimate hidden vantage point, exemplified by the dark side of the moon. The technology and logic of the current ‘War on Terror’ fits neatly into already extant long–range military strategic planning, and could have a massive impact on the shape of urban environments in the immediate and distant futures. Cet article reprend en partie l’historique et la logique qui sous–tendent l’automatisation des systèmes d’armes dans le cadre de leur déploiement en tant qu’élément constitutif de la ‘séécurité de la patrie‘ aux Etats–Unis après les attaques du 11 septembre. Il se consacre surtout aux aéronefs de combat sans pilote qui font partie des stratégies de R&D de l’Air Force depuis longtemps même si, apparemment, ils ont dorénavant trouvé un événement justifiant leur production et leur déploiement. Quels seraient les effets sur le paysage urbain d’un recours à ces véhicules sans pilote capables de choisir ‘intelligemment’ une cible et de la viser? Et ce, en particulier si la technologie qui leur permet de trouver la cible sous forme de coordonnées par satellite s’appuie sur cette même informatique qui fournit l’infrastructure aux communautés ‘intelligentes’, celle–ci étant déjà installée sur quasiment toutes les scènes urbaines. En s’intéressant à la logique de rapidité et surprise qui a motivé la tendance croissante et la dépendance à légard des systèmes d’armes automatisés intelligents, l’article révèle comment les arguments favorables au développement et au déploiement de ces systèmes fonctionnent aussi comme arguments contraires. La technologie qui rend intelligent un tel système signifie, par exemple, qu’il doit être capable de choisir la mauvaise cible, donc son ‘intelligence’. De même, le besoin de maîtriser l’élément de surprise trouve sa fin logique dans la recherche de la position cachée optimale, caractérisée par la face cachée de la lune. Technologie et logique de la ‘guerre’ actuelle contre la terreur s’adaptent parfaitement aux plans stratégiques militaires de grande envergure déjà en place et pourraient avoir une incidence énorme sur la conformation des environnements urbains dans un avenir à la fois immédiat et lointain.     

The LeChatelier principle for changes in risk

In this paper, we analyze the comparative statics of changes in risk in the context of problems with multiple decision variables. We demonstrate, in particular, that the Samuelson–LeChatelier principle extends naturally to the comparative statics of changes in risk: in the presence of positive feedbacks between the decision variables, the unrestricted response to an increase in risk is in the same direction and stronger in magnitude than the restricted response (i.e. the choice when other decision variables are fixed). We define the concepts of Nth-degree risk complements and Nth-degree risk substitutes and we show that it is in any one of these two cases (and only in these cases) that we will observe positive feedbacks between the decision variables. We also analyze the extent to which the same principle can be applied to strategic settings under uncertainty.     

Conducting a sales forecasting audit

Continuous improvement in sales forecasting is a worthy goal for any organization. This paper describes a methodology for conducting a sales forecasting audit, the goal of which is to help a company understand the status of its sales forecasting processes and identify ways to improve those processes. The methodology described here has been developed over a 5-year period, involving multiple auditors, and has been implemented (to date) at 16 organizations. This methodology revolves around three distinct phases: the ‘as-is’ phase, in which the audit team seeks to understand fully a company’s current forecasting process; the ‘should-be’ phase, in which the audit team presents a vision of what world-class forecasting should look like at the audited company, and the ‘way-forward’ phase, in which the audit team presents a roadmap of how the company can change its forecasting processes to achieve world-class levels. Those companies that have responded positively to the audit process have experienced significant improvement in their forecasting performance. The paper concludes by presenting lessons from audits conducted to date, as well as implications for management practice and future research.     

In search for yield? Survey-based evidence on bank risk taking

Monetary policy can have an impact on economic and financial stability through the risk taking of banks. Falling interest rates might induce investment into risky activities. This paper provides evidence on the link between monetary policy and bank risk taking. We use a factor-augmented vector autoregressive model (FAVAR) for the US for the period 1997–2008. Besides standard macroeconomic indicators, we include factors summarizing information provided in the Federal Reserve’s Survey of Terms of Business Lending (STBL). These data provide information on banks׳ new loans as well as interest rates for different loan risk categories and different banking groups. We identify a risk-taking channel of monetary policy by distinguishing responses to monetary policy shocks across different types of banks and different loan risk categories. Following an expansionary monetary policy shock, small domestic banks increase their exposure to risk. Large domestic banks do not change their risk exposure. Foreign banks take on more risk only in the mid-2000s, when interest rates were ‘too low for too long’.     

Adaptive learning forecasting,with applications in forecasting agricultural prices

We introduce a new forecasting methodology, referred to as adaptive learning forecasting, that allows for both forecast averaging and forecast error learning. We analyze its theoretical properties and demonstrate that it provides a priori MSE improvements under certain conditions. The learning rate based on past forecast errors is shown to be non-linear. This methodology is of wide applicability and can provide MSE improvements even for the simplest benchmark models. We illustrate the method’s application using data on agricultural prices for several agricultural products, as well as on real GDP growth for several of the corresponding countries. The time series of agricultural prices are short and show an irregular cyclicality that can be linked to economic performance and productivity, and we consider a variety of forecasting models, both univariate and bivariate, that are linked to output and productivity. Our results support both the efficacy of the new method and the forecastability of agricultural prices.     

The Violence of Abstract Space: Contested Regional Developments in Southern Mexico

The article proposes a reinterpretation of Henri Lefebvre's concept of abstract space, emphasizing the significance of the ‘violence of abstraction’ within the concept itself, and within the concrete process of the capitalist production of space. This interpretation of abstract space is developed through the case of the Plan Puebla Panama (PPP) as an ‘actually existing’ abstract space. Launched in 2001 and abandoned in 2008, the PPP was a regional development programme for southern Mexico and Central America, which aimed to transform this region from a peripheral zone of peasant agriculture and social unrest into a modernized node of the global economy through the construction of infrastructure networks and the restructuring of economic activity. Focusing on southern Mexico, I explore the symbolic, structural and direct forms of violence embodied in the PPP: its abstraction from the lived spaces of the region; its incorporation of the region into global circuits of capital; and its repression of a network of place‐based resistances.     

Non-linear volatility dynamics and risk management of precious metals

In this paper, we investigate the value-at-risk predictions of four major precious metals (gold, silver, platinum, and palladium) with non-linear long memory volatility models, namely FIGARCH, FIAPARCH and HYGARCH, under normal and Student-t innovations’ distributions. For these analyses, we consider both long and short trading positions. Overall, our results reveal that long memory volatility models under Student-t distribution perform well in forecasting a one-day-ahead VaR for both long and short positions. In addition, we find that FIAPARCH model with Student-t distribution, which jointly captures long memory and asymmetry, as well as fat-tails, outperforms other models in VaR forecasting. Our results have potential implications for portfolio managers, producers, and policy makers.     

Maximizing bidder surplus in simultaneous online art auctions via dynamic forecasting

This paper presents a novel intelligent bidding system, called SOABER (Simultaneous Online Auction BiddER), which monitors simultaneous online auctions of high-value fine art items. It supports decision-making by maximizing bidders’ surpluses and their chances of winning an auction. One key element of the system is a dynamic forecasting model, which incorporates information about the speed of an auction’s price movement, as well as the level of competition both within and across auctions. Other elements include a wallet estimator, which gauges the bidders’ willingness to pay, and a bid strategizer, which embeds the forecasting model into a fully automated decision system. We illustrate the performance of our intelligent bidding system on an authentic dataset of online art auctions for Indian contemporary art. We compare our system with several simpler ad-hoc approaches, and find it to be more effective in terms of both the extracted surplus and the resulting winning percentage.     

Forecasting the UK economy with a medium-scale Bayesian VAR

We estimate a Bayesian VAR (BVAR) for the UK economy and assess its performance in forecasting GDP growth and CPI inflation in real time relative to forecasts from COMPASS, the Bank of England’s DSGE model, and other benchmarks. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, their performances when forecasting CPI were similar. We also find that the BVAR density forecasts outperformed those of COMPASS, despite under-predicting inflation at most forecast horizons. Both models over-predicted GDP growth at all forecast horizons, but the issue was less pronounced in the BVAR. The BVAR’s point and density forecast performances are also comparable to those of a Bank of England in-house statistical suite for both GDP and CPI inflation, as well as to the official Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies.