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The purpose of this paper is to illustrate a profitable and original index options trading strategy.

The methodology is based on auto regressive integrated moving average (ARIMA) forecasting of the S&P 500 index and the strategy is tested on a large database of S&P 500 Composite index options and benchmarked to the generalized auto regressive conditional heteroscedastic (GARCH) model. The forecasts validate a set of criteria as follows: the first criterion checks if the forecasted index is greater or lower than the option strike price and the second criterion if the option premium is underpriced or overpriced. A buy or sell and hold strategy is finally implemented.

The paper demonstrates the valuable contribution of this option trading strategy when trading call and put index options. It especially demonstrates that the ARIMA forecasting method is a valid method for forecasting the S&P 500 Composite index and is superior to the GARCH model in the context of an application to index options trading.

The strategy was applied in the aftermath of the 2008 credit crisis over 60 months when the volatility index (VIX) was experiencing a downtrend. The strategy was successful with puts and calls traded on the USA market. The strategy may have a different outcome in a different economic and regional context.

This study aims to propose a non-linear model to describe the effect of macroeconomic shocks on delinquency rates of three kinds of bank loans. Indeed, a wealth of literature has recognized significant evidence of the linkage between macro conditions and credit vulnerability, perceiving the importance of the high amount of bad loans for economic stagnation and financial vulnerability.

Generally, this linkage was represented by linear relationships, but the strong dependence of bank loan default on the economic cycle, subject to changes in regime, could suggest non-linear models as more appropriate. Indeed, macroeconomic variables affect the performance of bank’s portfolio loan, but such a relationship is subject to changes disturbing the stability of parameters along the time. This study is an attempt to model three different kinds of bank loan defaults and to forecast them in the case of the USA, detecting non-linear and asymmetric behaviors by the adoption of a Markov-switching (MS) approach.

Comparing it with the classical linear model, the authors identify evidence for the presence of regimes and asymmetries, changing in correspondence of the recession periods during the span of 1987–2017.

The data are at a quarterly frequency, and more observations and more extended research periods could ameliorate the MS technique.

The good forecasting performance of this model could be applied by authorities to fine-tune their policies and deal with different types of loans and to diversify strategies during the different economic trends. In addition, bank management can refer to the performance of macroeconomic conditions to predict the performance of their bad loans.

The authors show a clear outperformance of the MS model concerning the linear one.

International Journal of Operations and Production Management (IJOPM)'s Impact Pathway (IP) section has been launched in 2020 to host short contributions grounded in current managerial practices and/or policy development, challenging established operations and supply chain management (OSCM) knowledge and highlighting innovative and relevant research directions. This commentary reflects on the achievements of the section, delineates the key features of IP papers and stimulates further development.

This commentary provides a brief overview of the IJOPM's IP section, taking stock of the contributions that have been published so far, analysing their topics, methodologies, insights and impact.

The 19 contributions published over the last three years have dealt with a variety of emerging topics, ranging from the COVID-19 response to additive manufacturing, leveraging on key evidence from managerial practice that challenges consolidated knowledge and theory, providing clear research directions as well as managerial and/or policy guidelines.

The commentary reflects on the importance of phenomenon-driven research that seeks to bridge the gap between theory and practice, thus increasing the impact and reach of OSCM research. This is a call for contributions from scholars, business leaders and policymakers to develop further impact-oriented research.

A significant body of literature suggests that shipping companies operate in an extremely volatile and risky environment, relying on the effective use of information to remain competitive. However, decision-making in this market is demanding because of the high uncertainty, market competition and significant capital investments. Moreover, the rapid spread of COVID-19 renders information uncertainty a daunting challenge for companies engaged in global trade. Hence, this study aims to explore the information behavior of managers in a time of crisis seems compelling.

This study provides novel insights into the information behavior of senior managers by adopting a qualitative approach. Forty-nine semi-structured face-to-face interviews with individuals from Hellenic shipping companies were conducted. Moreover, this study explores the extant theory qualitatively, using the grounded theory methodology and shows that an unprecedented event (pandemic crisis) can redefine the information behavior of managers.

This study highlights the importance of information in decision-making. Moreover, the results show that, during a pandemic, managers resort to alternative information sources, adopt collaborative information behaviors and take advantage of digital technology.

There is limited research in exploring the information behavior of managers in times of pandemics. This research underscores the fact that during a crisis, managers seek information from digital information resources and decision-making assumes a more decentralized form. This study concludes with a discussion of the theoretical and practical implications of these findings.

The study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.

This work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.

This research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.

This research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.

This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

The link between confidence and economic decisions has been widely covered in the economic literature, yet it is still an unexplored field in tourism. The purpose of this paper is to address this gap, and investigate benefits in forecast accuracy that can be achieved by combining the UNWTO Tourism Confidence Index (TCI) with statistical forecasts.

Research is conducted in a real-life setting, using UNWTO unique data sets of tourism indicators. UNWTO TCI is pooled with statistical forecasts using three distinct approaches. Forecasts efficiency is assessed in terms of accuracy gains and capability to predict turning points in alternative scenarios, including one of the hardest crises the tourism sector ever experienced.

Results suggest that the TCI provides meaningful indications about the sign of future growth in international tourist arrivals, and point to an improvement of forecast accuracy, when the index is used in combination with statistical forecasts. Still, accuracy gains vary greatly across regions and can hardly be generalised. Findings provide meaningful directions to tourism practitioners on the use opportunity cost to produce short-term forecasts using both approaches.

Empirical evidence suggests that a confidence index should not be collected as input to improve their forecasts. It remains a valuable instrument to supplement official statistics, over which it has the advantage of being more frequently compiled and more rapidly accessible. It is also of particular importance to predict changes in the business climate and capture turning points in a timely fashion, which makes it an extremely valuable input for operational and strategic decisions.

The use of sentiment indexes as input to forecasting is an unexplored field in the tourism literature.

Using an abductive perspective, this study aims to review the scientific literature about the governance and management of the digital supply chain (DSC) in the context of the business organizations, providing an overview of the state of the art of the research and outlining a future research agenda with a knowledge management (KM) focus.

After investigating the Scopus database, 54 articles were identified as relevant and then subjected to an initial discernment. After this assessment, 34 articles focusing on operations management were further analyzed through both a bibliometric analysis and a content analysis.

The DSC represents a research area of increasing attention, with relevant contributions to several aspects of the field, as well as about KM. At the same time, the results show that the scientific literature on DSC models, solutions and applications is fragmented. Although the analysis has found a heterogeneous literature, two main streams of research seem to emerge: KM in the business culture development about DSC and KM in the business technological evolution about DSC.

Although there exists growing interest in the scientific community, or perhaps because of this, area of research remains fragmented and under-theorized, thus requiring more systematic studies considering both economic and social aspects of the DSC. This study aims to provide innovative insights about this evolution, especially highlighting the two main contributions of KM in DSCs that have been revealed (business culture development and business technological evolution).

Recently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received little attention. We aimed to address this knowledge gap, particularly in the context of the UAE and investigated the embodied energy associated with the use of concrete and other materials commonly used in residential buildings in the hot desert climate of the UAE.

Using input–output based hybrid analysis, we quantified the life-cycle embodied energy of a villa in the UAE with over 50 years of building life using the average, minimum, and maximum material service life values. Mathematical calculations were performed using MS Excel, and a detailed bill of quantities with >170 building materials and components of the villa were used for investigation.

For the base case, the initial embodied energy was 57% (7390.5 GJ), whereas the recurrent embodied energy was 43% (5,690 GJ) of the life-cycle embodied energy based on average material service life values. The proportion of the recurrent embodied energy with minimum material service life values was increased to 68% of the life-cycle embodied energy, while it dropped to 15% with maximum material service life values.

The findings provide new data to guide building construction in the UAE and show that recurrent embodied energy contributes significantly to life-cycle energy demand. Further, the study of material service life variations provides deeper insights into future building material specifications and management considerations for building maintenance.