Dimension reduction and variable selection play important roles in high dimensional data analysis. The sparse MAVE, a model-free variable selection method, is a nice combination of shrinkage estimation, Lasso, and an effective dimension reduction method, MAVE (minimum average variance...

Minimum average variance estimation (MAVE, Xia et al. (2002) [29]) is an effective dimension reduction method. It requires no strong probabilistic assumptions on the predictors, and can consistently estimate the central mean subspace. It is applicable to a wide range of models, including time...

We study how firm characteristics are correlated with stock price levels by measuring the long-term discount rates (defined as the internal rate of return) of anomaly portfolios over a long horizon. We develop a simple, non-parametric methodology to estimate the long-term equity discount rate...

I develop a powerful test to evaluate individual investor's stock-picking skills by constructing counterfactual return distributions as the benchmark. The test leverages information in portfolio holdings to examine the entire distribution of investor's performance and develops robustness to...

Long-term discount rate is different from short-term expected return. Long-term discount rate determines the level of equity valuation, whereas short-term return reflects the change in valuation. Long-term discount rate is relevant to corporate managers as it summarizes a firm's financing cost,...