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Long-term forecasts about commodity market indicators play an important role in informing policy and investment decisions by governments and market participants. Our study examines whether the accuracy of the multi-step forecasts can be improved using deep learning methods.

We first formulate a supervised learning problem and set benchmarks for forecast accuracy using traditional econometric models. We then train a set of deep neural networks and measure their performance against the benchmark.

We find that while the United States Department of Agriculture (USDA) baseline projections perform better for shorter forecast horizons, the performance of the deep neural networks improves for longer horizons. The findings may inform future revisions of the forecasting process.

This study demonstrates an application of deep learning methods to multi-horizon forecasts of agri-cultural commodities, which is a departure from the current methods used in producing these types of forecasts.

This study aims to examine how the Information Technology (IT) sector in India is changing sporadically to be relevant to be able to meet the changing demands. Companies are striving hard to be able to leverage any such factor that adds to the competitive advantage needed to survive in this dynamic market. Therefore, an organization has to be able to keep innovation as its primary objective.

This study used a cross-sectional research design and the data from 303 IT professionals were used to validate the relationships among psychological capital (PSC), cognitive diversity (COD), temporal ambidexterity (TEA), innovative work behaviour (IWB) and adaptive performance (ADP). Hypotheses testing was done through the SEM and mediation analysis was conducted using bootstrap estimation in SPSS AMOS.

The results suggest that PSC significantly affects both IWB and ADP. COD has a significant effect only on IWB. In addition, directly impacts ADP. Mediation analysis indicates that COD has full mediation between the relationship of IWB and ADP, whereas PSC has a partial mediation. However, TEA has no mediation indicating that an employee needs to have support from his organization to be ambidextrous.

This study shows how significant innovation and IWBs are in the context of the IT sector and how positive thinking, a diverse set of people and the balance between short-term and long-term goals could promote IWBs in an individual leading to better ADP.