Our objective in this paper is to examine whether one can use option-implied information to improve mean-variance portfolio selection with a large number of stocks, and to document which aspects of option-implied information are most useful for improving the out-of-sample performance of...

We study whether investors can exploit stock return serial dependence to improve out-of- sample portfolio performance. To do this, we first show that a vector-autoregressive (VAR) model estimated with ridge regression captures daily stock return serial dependence in a stable manner. Second, we...

In this paper, we wish to evaluate the performance of simple asset-allocation strategies such as allocating 1/N to each of the N assets available. To do this, we compare the out-of-sample performance of such simple allocation rules to about ten models of optimal asset-allocation (including both...

In this paper, we evaluate the out-of-sample performance of the portfolio policy from the sample-based mean-variance portfolio model and the various extensions of this model, designed to reduce the impact of estimation error relative to the benchmark strategy of investing a fraction 1/N of...

We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1/N portfolio. Of the 14 models we evaluate across seven empirical datasets, none is consistently better than the 1/N rule in terms of...