This study incorporates economic and financial cycles into asset allocation to enhance portfolio efficiency. Utilizing wavelet decomposition and principal component analysis, the paper constructs variables that broadly represent economic and financial cycles. Then, this study proposes the WISE Clock, a new data-driven investment strategy that extend the traditional Merrill Lynch Investment Clock by incorporating financial cycle. Employing machine learning techniques and cyclical information, this study forecasts asset returns for the U.S. and Chinese markets and integrates these predictions into a Black-Litterman framework. Backtesting results confirm that the approach significantly surpasses traditional models, enhancing portfolio risk-return performance.