Patrick Bielstein

EDHEC-Risk Institute · Scientific Beta

We propose a novel method to forecast corporate earnings, which combines the accuracy of analysts’ forecasts with the unbiasedness of a cross-sectional model. We build on recent insights from the earnings forecasts literature to improve analysts’ forecasts in two ways: reducing their sluggishness with respect to information in recent stock price mo...

This study argues that in corporate diversification there is a bright side (coinsurance effect) and a dark side (diversification discount). While diversification might reduce systematic risk by its impact on the cost of financial distress, it might increase systematic risk because of inefficient cross‐subsidization at the same time. Building on a t...

Despite its theoretical appeal, Markowitz mean-variance portfolio optimization is plagued by practical issues. It is especially difficult to obtain reliable estimates of a stock’s expected return. Recent research has therefore focused on minimum volatility portfolio optimization, which implicitly assumes that expected returns for all assets are equ...

The Black and Litterman (Financ Anal J 48(5):28–43, 1992) (BL) approach to portfolio optimization requires investor views on expected asset returns as an input. I demonstrate that the market implied cost of capital (ICC) is ideal for quantifying those views on a country level. I benchmark this approach against a BL optimization using time-series mo...

We propose a novel method to forecast corporate earnings which combines the accuracy of analysts' forecasts with the unbiasedness of a mechanical model. Our choice of variables is driven by recent insights from the earnings forecasts literature and the resulting model outperforms all analyzed methods in terms of accuracy, bias, and earnings respons...