Comparing Forecasting Models for Daily Asset Returns

You are comparing two forecasting models for daily asset returns: Model A, a simple linear factor model, and Model B, a regularized machine-learning model. Both output next-day predicted returns $\hat{r}^{(A)}_t$ and $\hat{r}^{(B)}_t$ for a universe of assets. Each day, you form a zero-cost long-short portfolio by going long the top-predicted assets and short the bottom-predicted assets, scaled so that each portfolio has unit ex-ante volatility. **(a)** Describe how you would construct a rolling cross-validation scheme on this time-series data to fairly compare the out-of-sample Sharpe ratios of the two models. Be specific about the fold structure, what is trained on what, and how you prevent look-ahead bias. **(b)** Propose a statistical test to assess whether Model B's Sharpe ratio is significantly higher than Model A's. State the null and alternative hypotheses clearly, define the test statistic, and discuss its distributional assumptions. **(c)** You are evaluating not just two models but an entire library of candidate models. Discuss how transaction costs, turnover constraints, and multiple comparisons should factor into the evaluation.