It is well-known that exchange rates are difficult to forecast using observed macro-fundamental variables. This discrepancy between economic theory and empirical results is called the Meese and Rogoff puzzle. The purpose of this study is to address this puzzle from a new approach. Rather than pursuing a linkage between macro-fundamentals and exchange rates, we focus on the market sentiment index as a factor that could possibly enhance exchange rate predictability. The analysis folds into three phases. First, we conducted an assessment of the traditional exchange rate predictability model, as well as the augmented traditional model incorporating the market sentiment index. Second, we predicted the exchange rate by applying the market sentiment index, based on the contrarian opinion investment strategy commonly used by foreign exchange dealers. Finally, we analyzed if the machine learning model incorporating both economic fundamentals and market sentiment index could enhance the predictability of the exchange rate.