The purpose of this paper is to model analysts’ forecasts. The paper differs from the previous research in that we do not focus on how accurate these predictions may be. Accuracy may indeed be an important quality but we argue instead that another equally important aspect of the analysts’ job is to predict and describe the impact of jump events. In effect, the analysts’ role is one of scenario prediction. Using a Bayesian-inspired generalised method of moments estimation procedure, we use this notion of scenario prediction combined with the structure of the Morgan Stanley analysts’ forecasting database to model normal (base), optimistic (bull) and pessimistic (bear) forecast scenarios for a set of reports from Asia (excluding Japan) for 2007–2008. Since the estimation procedure is unique to this paper, a rigorous derivation of the asymptotic properties of the resulting estimator is also provided.

Since 2008, Risk-Reward Views have been the basis for the recommendations on all the stocks covered by Morgan Stanley's equity research analysts globally. The firm's analysts use this systematic approach to communicate a broader range of fundamental insights about expected returns and risks, and to articulate more clearly the logic underlying their price targets and calls, and the level of conviction associated with them. The rationale for this approach is to align the firm's research product with its clients' thinking and investment discipline while also creating a link between traditional equity analysis and widely accepted principles of modern portfolio management. Too many sell-side analysts still try to manifest expertise and conviction with one-sided investment theses backed by single-point estimates and “table pounding.” That does a disservice to investors who are looking to sell-side analysts for an ongoing dialogue about the future with experts on company fundamentals. Risk-Reward Views are designed to produce a more complete view of the risk-reward trade-off in a given stock. They are meant to supplement the use of quant-only risk models that, while offering at least the illusion of precision, are also often opaque and backward looking. The approach aims to increase transparency while avoiding unnecessary complexity by focusing on a handful of critical uncertainties and modeling a manageable number of coherent scenarios that are relevant to investor debates and cover a full range of plausible outcomes. This article focuses on the theoretical underpinnings of the department's Risk-Reward initiative. For a more detailed discussion of the institutional setting and the processes followed to implement these ideas, readers are referred to the recently published Harvard Business School case study, “The Risk-Reward Framework at Morgan Stanley Research” (Harvard Business School Case N9–111–011).