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Quantitative equity portfolio management has become a fundamental building block of the investment management. The development of general equilibrium asset pricing models enables statistical arbitrage strategies to capture the effect factors of the market returns. In empirical analysis, a crucial step in the model-building process is the selection of an essential factors which may contribute to the positive excess returns. However, it could be challenging since thousands of candidate factors can be obtained. In this study, we employed a factor-augmented model to identify the effect factors for excess returns, and rank the portfolios according the selected factors. A trading strategy of the combination of buying stock portfolio and stock index futures hedging is then perform for Alpha arbitrage. Keywords—factor-augmented; predictors; quantile regression; arbitrage strategy

Quantile Factor-augmented Prediction Model and Its Applications to Alpha-arbitrage Strategy in China’s Stock Market