The existing literature about portfolio management has investigated how to update a portfolio allocation, conditional on the information that possibly predicts asset returns and volatilities. We add several innovations to fill the lacuna of prior research in the contexts of global asset allocation. First, we suggest a simple method of how to rebalance portfolios automatically and dynamically in order to exploit potential market inefficiencies. The existing literature has not developed such a strategy. Out-of-sample tests demonstrate that our strategy dominates both static allocation and dynamic strategies that do not account for possible mispricing. Thus, our strategy can contribute not only to academia, but also to practical portfolio managers who endeavor to beat markets. Second, we elaborate portable alpha strategies using the new dynamic strategy. Once we add an alpha portfolio to existing portfolios, then they perform better in terms of mean and risk. Thus, it makes our alpha portfolio portable, i.e., we can apply the alpha portfolio to any fund and can enhance its performance. Third, our dynamic strategy implies a convenient method to estimate a conditional mean and covariance matrix as functions of predictive information while ensuring positive definiteness of the covariance matrix without consuming much computational power. Such estimation strategy can be useful to practical risk managers and traders who need to control the risks of large target portfolios on a real time basis.