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).
Based on expectancy theory, goal-setting theory and control theory, we propose a model in which perceived fairness mediates the relationship between characteristics of employee performance management (PM) systems and their perceived effectiveness by employees. PM system characteristics we propose are the frequency and length of formal reviews, the frequency of informal reviews and feedback, whether the formal conversation focused on evaluation or development and finally the degree of participation. The model was tested on a cross-industry sample of 3192 employees in Belgium. The measurement and structural models were simultaneously tested using structural equation modeling, and we used a bootstrapping approach to test the mediation hypothesis. Our findings indicate that performance review focus and employee participation strongly relate to perceptions of appraisal fairness and PM system effectiveness and that the frequency of informal performance reviews is stronger related to PM system effectiveness than the frequency of formal performance reviews. This suggests that the manifest expressions of PM have more impact on PM system effectiveness rather than the more latent characteristics of PM systems. The findings advance research to the role and functionality of PM systems by showing that (a) the manner in which PM systems are shaped and executed is of fundamental importance for their effectiveness, (b) fairness partially mediates the relationship between PM system characteristics and their effectiveness and (c) the three motivational theories appear useful for understanding the consequences of PM practices on individual employees.
It is commonly accepted nowadays that external knowledge sources are important for firms' innovative performance. However, it is still not clear, what dimensions of firms' external knowledge search strategy are crucial in determining their innovation success and whether these search strategies are contingent on different innovation modes. In this study, we analyse how the innovative performance is affected by the scope, depth, and orientation of firms' external search strategies. We apply this analysis to firms using STI (science, technology and innovation) and DUI (doing, using and interacting) innovation modes. Based on a survey among firms in China, we find that greater scope and depth of openness for both innovation modes improves innovative performance indicating that open innovation is also relevant beyond science and technology based innovation. Furthermore, we find that decreasing returns in external search strategies, suggested by Laursen and Salter (2006), are not always present and are contingent on the innovation modes. Next, we find that the type of external partners (we label it “orientation of openness”) is crucial in explaining innovative performance and that firms using DUI or STI innovation modes have different sets of relevant innovation partners. This shows that the orientation of openness is an important dimension—in addition to the scope and depth of openness. As respondents are located in China, this study provides evidence that open innovation is also relevant in developing countries.
Dewettinck, Koen; Vroonen, Wim (Vlerick Business School, 2013)
Export search results
The export option will allow you to export the current search results of the entered query to a file. Different
formats are available for download. To export the items, click on the button corresponding with the preferred download format.
By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.
To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export.
The amount of items that can be exported at once is similarly restricted as the full export.
After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.