Managers and researchers alike have long recognized the importance of corporate textual risk disclosures. Yet it is a nontrivial task to discover and quantify variables of interest from unstructured text. In this paper, we develop a variation of the latent Dirichlet allocation topic model and its learning algorithm for simultaneously discovering and quantifying risk types from textual risk disclosures. We conduct comprehensive evaluations in terms of both conventional statistical fit and substantive fit with respect to the quality of discovered information. Experimental results show that our proposed method outperforms all competing methods, and could find more meaningful topics (risk types). By taking advantage of our proposed method for measuring risk types from textual data, we study how risk disclosures in 10-K forms affect the risk perceptions of investors. Different from prior studies, our results provide support for all three competing arguments regarding whether and how risk disclosures affect the risk perceptions of investors, depending on the specific risk types disclosed. We find that around two-thirds of risk types lack informativeness and have no significant influence. Moreover, we find that the informative risk types do not necessarily increase the risk perceptions of investors—the disclosure of three types of systematic and liquidity risks will increase the risk perceptions of investors, whereas the other five types of unsystematic risks will decrease them. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1930 . This paper was accepted by Alok Gupta, special issue on business analytics.

The Internet is growing rapidly in terms of both use and infrastructure. Unfortunately, demand is outpacing the capacity of the infrastructure, as evidenced by unacceptably long response times. To support current load and further growth, we must address this problem. Several caching strategies have been proposed in the literature; many have been implemented to improve the quality of service on the Web. In this paper, we identify the main causes of delay on the Web, and provide a review of the various caching strategies employed to mitigate these delays. We also survey the application of Operations Research/Management Science (OR/MS) techniques to caching on the Web. Finally, we identify several open OR/MS research problems related to Web caching.