Deep learning technology is rapidly adopted in financial market settings. Using a large data set from the Chinese stock market, we propose a return-risk trade-off strategy via a new transformer model. The empirical findings show that these updates, such as the self-attention mechanism in technology, can improve the use of time-series information related to returns and volatility, increase predictability, and capture more economic gains than other nonlinear models, such as LSTM. Our model employs Shapley additive explanations (SHAP) to measure the “economic feature importance” and tabulates the different important features in the prediction process. Finally, we document several economic explanations for the TF model. This paper sheds light on the burgeoning field on asset allocation in the age of big data.