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In modern industrial processes, the need of reducing lead time is imperious. This goal is pursued by “Holonic” structures, which are systems based on a network of collaboration. The purpose of this paper is to appraise the possible benefits, in terms of production duration, of such organizations compared to non-holonic arrangements. In addition, this analysis represents virtual teams, which are a significant and strategic attribute of holonic manufacturing systems.

The experiment performed in this paper is a simulation of an automobile assembly process under both a holonic and a non-holonic structure to observe lead times and the distinctive characteristics of each organization. Other targets of this research are to monitor the advantages that either strategy may offer in terms of efficiency and to examine a possible model of how virtual teams can be used in holonic networks.

The findings are unexpected. An initial expectation might lead to the belief that, given the better coordination and communication of holonic networks, lead times would be diminished. This experiment, utilizing virtual teams of university students, indicated otherwise.

It should be noticed, however, that future experiments in real manufacturing assembly processes are recommended to complement the findings of this study.

The management of lead times is indeed a complex task that includes a diversity of variables. Holonic structures should balance several factors that might play a role in lead times and ultimately in the success of a project. An original experiment with the participation of many universities in different countries is presented in this study. The exposure of global characteristics of modern manufacturing structures constitutes the main value of this research.

The purpose of this paper is to test whether investor sentiment is a significant predictor of future Mexican stock market returns. It also estimates the dynamic correlation between investor sentiment and equity returns. Finally, it examines if investor sentiment innovations impact unexpected returns for a variety of portfolios.

This study utilizes predictive regressions to determine if sentiment can predict Mexican equity returns. Multivariate GARCH models are estimated to examine the time-varying correlations between investor sentiment and equity returns.

The results show that Mexican investor sentiment is a significant predictor of Mexican equity returns for up to 24 months ahead. The findings show that high levels of sentiment today are associated with lower equity returns over the near term. Furthermore, multivariate GARCH estimations indicate that the correlation between investor sentiment and equity returns is not static and varies considerably over time. Finally, the findings indicate that sentiment innovations are significantly correlated with unexpected returns, reinforcing the notion that unexplained sentiment fluctuations lead to unexplained changes in stock market returns. Overall, these results suggest that investor sentiment is a significant source of risk for the Mexican stock market.

This study seeks to further our understanding of how behavioral factors influence and predict Mexican equity returns.