Research

We show that firms anticipate information spillover from peers' investment decisions and delay project exercise to learn from them. While this information improves project selection, the cost of waiting erodes these gains. To establish causality, we exploit local exogenous variation from the 1800s that shapes the number of peers that a firm can learn from today. The effect is most salient when information is scarce, costs of waiting are low, projects have low expected profitability, and the source information is more relevant. Finally, the anticipation of spillovers dampens aggregate investment, suggesting a role for this mechanism in macro-investment models.

We study when and why firms exercise real options. Using detailed project-level investment data, we find that the likelihood that a firm exercises a real option is strongly related to peer exercise behavior. Peer exercise decisions are as important in explaining exercise behavior as variables commonly associated with standard real option theories, such as volatility. We identify peer effects using localized exogenous variation in peer project exercise decisions and find evidence consistent with information externalities being important for exercise behavior.

We uncover the mental models financial professionals use to explain their quantitative forecasts, and show how they shape beliefs and return predictability. Using the near-universe of 2.1 million equity analyst reports, we collect the valuation methods analysts adopt to compute their price targets, together with their reasoning, measured as attention to topics, and their associated valuation channels, time horizons, and sentiments. To validate the reliability of our output, we introduce a multi-step LLM prompting strategy and new diagnostic tools. Consistent with a model of top-down and bottom-up attention, we then uncover three sets of facts. First, analysts' mental models are sparse and rigid, and the choice of attention allocation and valuation methods are jointly determined by both analyst- and firm-characteristics. Second, analysts' reasoning translates into their quantitative forecasts. Both attention and valuation methods contribute to differences in valuations over time and across analysts, but variation in attention plays a bigger role. Third, we study the extent to which different topics contribute to over and underreaction to information, and show how biases in analysts' reasoning are reflected in asset prices.

Valuation combines expectations about cash flows and risk, yet little is known about how experts subjectively assess and incorporate risk into their models. Using a comprehensive sample with detailed information on valuation-model design and discount-rate inputs, we provide new evidence about valuation practice and how analysts assess firm-level risk. Analysts anchor their discount rates in the Capital Asset Pricing Model but apply subjective adjustments: they incorporate firm-specific characteristics they deem relevant and account for estimation noise when updating toward the benchmark. These adjustments strengthen the risk-return trade-off captured by subjective betas, resulting in a steeper subjective Security Market Line. While this process strengthens the relation between betas and future idiosyncratic risk, it weakens their link to systematic risk. More broadly, our findings illustrate how formal models and expert judgment interact: normative frameworks serve as disciplining anchors, but expectations ultimately reflect context-dependent judgment extending beyond, and improving upon, the model itself.

I show that managers discriminate against idiosyncratic risk in capital budgeting: marginal projects with greater idiosyncratic risk exposure are associated with higher required rate of return. To establish causality, I exploit quasi-exogenous within-region variation in project-specific idiosyncratic risk. I then decompose the measure of idiosyncratic risk into a good and a bad component and show that managers penalize projects for their exposure to downside risk. Finally, I explore how costly external financing, internal monitoring frictions, and CEOs' personal exposure to idiosyncratic risk affect those adjustments. Overall, financial and operational frictions induce managers to account for idiosyncratic risk when determining projects' required rate of return.

Valuation models lie at the core of both financial theory and practice, yet we lack systematic evidence on how professionals value assets, which models perform best, and why. To make progress on these questions, we analyze valuation models in 1.1 million equity analyst reports. While, on average, simpler multiples-based models generate more accurate forecasts than more complex discounted cash flow (DCF) models, this masks important heterogeneity: skilled analysts produce superior forecasts with DCF models, especially for hard-to-value firms, underscoring the importance of expertise when employing complex models. To establish that model-specific expertise matters, we exploit a quasi-exogenous shock that forced some analysts to switch valuation models, and show that their forecast accuracy subsequently declines relative to analysts with established experience using the new approach.

We study how forecasters form beliefs about very long-run firm growth using data on terminal growth rate (TGR) expectations, textual discussions, and demographics. TGR expectations are distinct from shorter-horizon beliefs, correlated with firms’ long-run growth, and uncontaminated by expected returns. Compared to short-run expectations, persistent forecaster heterogeneity carries more weight, accounting for 69% of explained variation in TGR. Local extrapolation is strongest in distant and unfamiliar contexts. TGR heterogeneity reflects differential textual emphasis on macroeconomic, industry, and geographic topics, yielding a unifying insight: when outcomes are distant and fundamental anchors are weak, individual background and experience dominate in shaping beliefs.

Economic models develop conceptual frameworks for fundamental decisions but rarely prescribe a specific estimation approach. Using novel data on the inputs and assumptions in professional stock valuations, we study how financial analysts address estimation ambiguity when calculating a firm's cost of capital. Analysts use the same return-generating model (CAPM) but diverge in their estimation choices for key inputs, such as equity betas. Such estimation choices are driven by idiosyncratic analyst-specific criteria, persist throughout their career and across brokerages, and generate large cross-analyst variation in discount rates for the same stock.

Using granular data on managerial expenditures, we uncover heuristics in capital budgets, such as nominal rigidity, anchoring, and reset deadlines. Such heuristics are associated with managerial opportunism. Managers with budget surpluses increase spending before fiscal deadlines, and these projects underperform. Managers with budget shortfalls halt spending until refill dates, irrespective of investment options. These effects intensify at hierarchical firms with higher monitoring costs but weaken under strong principals.

Using hand-collected data on CEOs' personal assets, we find that CEOs prioritize corporate investment projects that increase the value of CEOs' private assets. Such projects are implemented sooner, receive more capital, and are less likely to be dropped. This investment strategy delivers large personal gains to the CEO but selects lower-NPV projects for the firm and erodes its investment efficiency. Consistent with value erosion, investment announcements by self-dealing CEOs generate negative announcement returns.