The article shows that while predicting stock returns is hard, the direction and volatility of returns are more predictable. By breaking down returns into parts based on their direction and size, the researchers found a new way to forecast returns better than traditional methods. This new model considers how these parts interact with each other, capturing complexities that previous methods miss. The study of US stock data revealed that this decomposition approach leads to more accurate and valuable predictions compared to standard methods.