Corporate Sustainability Performance and Idiosyncratic Risk: A Global Perspective

Does investing in sustainability leaders affect portfolio performance? Analyzing two mutually exclusive leading and lagging global corporate sustainability portfolios (Dow Jones) finds that (1) leading sustainability firms do not underperform the market portfolio, and (2) their lagging counterparts outperform the market portfolio and the leading portfolio. Notably, we find leading (lagging) corporate social performance (CSP) firms exhibit significantly lower (higher) idiosyncratic risk and that idiosyncratic risk might be priced by the broader global equity market. We develop an idiosyncratic risk factor and find that its inclusion significantly reduces the apparent difference in performance between leading and lagging CSP portfolios.     

De dienst van het goud

Rotterdam, Nov. 1911     

The External Funding of Academic Finance Research

We analyze the external funding of academic finance research. We show that funding is uncommon, particularly for U.S.‐based faculty, and is related to predictable attributes of an author's reputational capital. Further, when research is funded we find it is associated with better articles, as measured by publication in the most prestigious journals and the receipt of increased citations over time. Our study has relevance for every stakeholder in the university's research mission in finance.     

A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression*

Graph‐theoretic methods of causal search based on the ideas of Pearl (2000), Spirtes et al. (2000), and others have been applied by a number of researchers to economic data, particularly by Swanson and Granger (1997) to the problem of finding a data‐based contemporaneous causal order for the structural vector autoregression, rather than, as is typically done, assuming a weakly justified Choleski order. Demiralp and Hoover (2003) provided Monte Carlo evidence that such methods were effective, provided that signal strengths were sufficiently high. Unfortunately, in applications to actual data, such Monte Carlo simulations are of limited value, as the causal structure of the true data‐generating process is necessarily unknown. In this paper, we present a bootstrap procedure that can be applied to actual data (i.e. without knowledge of the true causal structure). We show with an applied example and a simulation study that the procedure is an effective tool for assessing our confidence in causal orders identified by graph‐theoretic search algorithms.