Is Machine Learning Better in Prediction of Direction or Value?
Building machine learning models for trading is full of nuances, and one important but often overlooked question is: what exactly should we try to predict—the direction of the next market move or the actual value of the asset’s return? A recent paper by Cheng, Shang, and Zhao, titled “Direction is More Important than Speed” offers a clear and practical answer. Their research shows that focusing on direction—simply whether returns will be positive or negative—leads to better model accuracy and, more importantly, stronger real-world investment performance. This is especially true when using machine learning methods, where predicting the direction allows models to better capture downside risks and build more effective trading strategies. For anyone using ML in finance, this paper makes a strong case that predicting where the market is headed is often more valuable than predicting how far it will go.