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...

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...

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,...

We show that much of the market premium for the year occurs on a handful of days, identifiable well in advance, on which several of the market's most famous, high-media-attention firms simultaneously announce earnings after the market close. Puzzlingly, the market surges occur during the 24...

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...

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...

Geographic diversification of listed companies expands the distance between parent and subsidiary in space geographically; thus, it is urgent to clarify whether the geographic distance between parent and subsidiary companies inhibits or promotes corporate innovation performance. This paper...