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 movements and improving their long-term performance. Our model outperforms the most popular methods from the literature in terms of forecast accuracy, bias, and earnings response coefficient. Furthermore, using our estimates in the implied cost of capital calculation leads to a substantially stronger correlation with realized returns compared to earnings estimates from extant cross-sectional models.

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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 an extension of the model of Hann, Ogneva, and Ozbas (), we analyze mergers and acquisitions in the US over the period 1985 to 2014. We find the coinsurance effect to decrease the cost of capital by 36  basis points for the average firm. However, at the same time, we observe a 7  basis points increase in the cost of capital related to the inefficiency of the firm's internal capital market. Both effects are statistically significant and robust to endogeneity concerns, different empirical specifications, and variable measurement.

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