New‐CEO earnings news exhibits asymmetric effects on stock prices. Stock prices rise more on good earnings news announced by firms with new CEOs compared with those with established CEOs. By contrast, stock prices tend to fall by a smaller amount on bad earnings news for new CEOs. Both the new‐CEO quality effect and the new‐CEO honeymoon effect are more pronounced for CEOs appointed during challenging situations. The new‐CEO quality effect is stronger for firms followed by fewer analysts, while the honeymoon effect is stronger for firms followed by more analysts – illustrating the importance of a transparent information environment.

No abstract

Following CEO turnovers, US firms adjust real business activities to manage earnings downwards (REM bath). This effect is most pronounced in firms with low levels of institutional ownership. REM baths early in CEOs' tenure can be confounded with legitimate adjustments to business activities. However, we show that they are not accompanied by increases in R&D or capital expenses, nor are they explained by restructuring expenses. CEOs with short tenure record more negative REM measures in their first year of tenure, when compared with CEOs with long tenure.

No abstract

We use machine learning for relative valuation and peer firm selection. In out‐of‐sample tests, our machine learning models substantially outperform traditional models in valuation accuracy. This outperformance persists over time and holds across different types of firms. The valuations produced by machine learning models behave like fundamental values. Overvalued stocks decrease in price and undervalued stocks increase in price in the following month. Determinants of valuation multiples identified by machine learning models are consistent with theoretical predictions derived from a discounted cash flow approach. Profitability ratios, growth measures, and efficiency ratios are the most important value drivers throughout our sample period. We derive a novel method to express valuation multiples predicted by our machine learning models as weighted averages of peer firm multiples. These weights are a measure of peer–firm comparability and can be used for selecting peer‐groups.