A variable impact neural network analysis of dividend policies and share prices of transportation and related companies

The purpose of this research is to investigate dividend policy,
including its impact on share prices of transportation providers
and related service companies, by comparing generalized regression
neural networks with conventional regressions. Our results
using regressions reveal that for Europe and for the US and Canada
the market-to-book-value, as a surrogate for growth opportunities,
fulfils expectations of pressures on dividends leading to a negative
association with dividend yields in accordance with the pecking
order theory. Neural network analysis indicates a clear role for
growth opportunities for the US and Canada pointing to an underlying
confidence on the part of transportation companies in their
own internal policies. Finally, risk is rewarded especially in Europe.