“When are you going to change this Children’s Safe Drinking Water program and make money for your company? Surely Procter & Gamble wants you to profit on the water purification technology—you can’t sustain your program as a non-profit!” Greg Allgood sighed internally at this question, as it seemed to surface frequently despite the continued and rapid growth of the Children’s Safe Drinking Water (CSDW) program at P&G. Allgood (Director of the CSDW program) was not actually frustrated with the query, as he had an easy answer ready. Rather, he wished that people could more easily see how his team’s non-profit work was adding greater value to the $80 billion dollar company than a for-profit sales model ever could. Procter & Gamble is a data-driven company, and after 24 years as a “Proctoid” he knew this better than anyone. Greg had significant qualitative and some quantitative information to support the idea that, in some cases, a non-profit business model could do much more for the bottom line than could a for-profit model. However, he knew that he needed to do even more to clarify this point for others.
In this paper we present a framework for linking smart products (with embedded real-time diagnostics and prognostics based health management capabilities) to a service provisioning
system to create a system of ―self-aware product-centric systems. The framework includes a powerful ―learning engine capable of monitoring, analyzing and interpreting patterns of system/product behavior in real-time. The learning engine provides the capability of information feedback for real-time, ―in-the-loop control. This concept enables the service-provisioning network to provide customer services such as product health management at reduced maintenance costs, improved responsiveness to customer needs during use, and generally more efficient operations.
We analyze the contribution of returns around earnings announcements to typical estimates of the “prices lead earnings” relation. We find that prior returns' ability to explain earnings is concentrated disproportionally in returns on earnings announcement dates, suggesting that a substantial portion of the estimated timeliness of returns in previous studies is empirically indistinguishable from the information content of earnings.
Three-tiered private-label (PL) portfolio strategies (low-quality tier: economy PLs, mid-quality tier: standard PLs, and top-quality tier: premium PLs) are gaining interest around the world. Drawing on the context-effects literature, the authors postulate how the introduction of economy and premium PLs may affect the choice of mainstream-quality and premium-quality national brands (NBs) and the choice of the retailer's existing PL offering.
This research utilizes data from the World Bank Investment Climate Survey to examine the use of external capital for almost 70,000 small and medium-sized firms in 103 developing and developed countries.
We analyze why companies that receive private equity investments outperform their rivals. We show that rivals experience a decrease in their stock prices and their operating performance around private equity (PE) investments in their industry.
Applied financial econometrics subjects are featured in this second volume, with papers that survey important research even as they make unique empirical contributions to the literature. These subjects are familiar: portfolio choice, trading volume, the risk-return tradeoff, option pricing, bond yields, and the management, supervision, and measurement of extreme and infrequent risks.
The investigations following the attacks of September 11, 2001, showed that our ability to verify a person’s identity is crucial to our national security. As pointed out by The 9/11 Commission Report (National Commission on Terrorists Attacks Upon the United States, 2004), travel documents are as important as weapons for terrorists. To carry out an attack on American soil, foreign terrorists must cross our borders—which requires passing an identification screening. A valid passport also allows a terrorist to obtain other valid documents (e.g., driver’s license, credit cards, health insurance card) that are important to performing normal life activities while maintaining a low profile and avoiding detection. Four projects, currently in different stages of implementation, use Radio Frequency Identification (RFID) or Machine-Readable Zones (MRZ) technologies for verification and validation of identity in the United States. These programs are (1) e-Passport, (2) PASS Card, (3) Real ID, and (4) Enhanced Driver’s License. The use of RFID enables data to be stored electronically in chips embedded in identification documents and shared quickly in digital format by law enforcement personnel. Documents with RFID chips and a secure networking environment to exchange data are deemed more secure and less prone to counterfeiting than conventional, non-electronic documents. However, there is still debate about how to best balance the security benefits from RFID-enabled identification documents with concerns about privacy.
We model the threat of such liquidation through the intermediation of an activist shareholder. Among other things, our model predicts that MDPs are more likely to be adopted by funds that appear to be less effective in providing portfolio services to their investors and that are relatively easy to liquidate or ‘attack’. We test the model on a panel of 236 CEFs and find good agreement with our model.
We investigate bivariate regime‐switching in daily futures‐contract returns for the US stock index and ten‐year Treasury notes over the crisis‐rich 1997–2005 period.
Our national security depends on a safe and secure food supply that is free of contamination, whether unintentional or the result of a terrorist act. In December 2006, Congress and the White House passed the Pandemic and All-Hazards Preparedness Act (PAHPA), establishing the goal of near-real-time electronic situational awareness to enhance early detection of, rapid response to, and management of public health threats in order to minimize their impact. Meeting this challenge for food safety depends on our ability to collect, interpret, and disseminate electronic information across organizational and jurisdictional boundaries. While events such as 9/11 have elevated the need to share critical intelligence related to security threats, these events have also promoted the proliferation of multiple data systems and tools whose lack of interoperability hinders effective intelligence gathering and timely response. Further, most of the public health and food safety informatics work in the United States—from early detection of food-related outbreaks by local and state health departments to confirmation by the Centers for Disease Control and Prevention (CDC) through “fingerprinting” of pathogenic contaminants—takes place at different local, state, and federal jurisdictional levels. As a result, large gaps exist in our ability to meet the challenge of food safety in the United States with regard to PAHPA.
This study explores the process of organizational change by examining localized social learning in organizational subunits. Specifically, we examine participation in university technology transfer, a new organizational initiative, by tracking 1,780 faculty members, examining their backgrounds and work environments, and following their engagement with academic entrepreneurship.
In its original conception the Kerr Tar Hub was broadly envisioned as a tech-intensive, locally driven regional park potentially providing a wide variety of infrastructure and service offerings intended to attract and support the location of emergent firms from within selected RTRP targeted industries.
We examine whether time variation in the comovements of daily stock and Treasury bond returns can be linked to measures of stock market uncertainty, specifically the implied volatility from equity index options and detrended stock turnover.
Multinational companies increasingly rely upon the work of virtual teams to manage their global intellectual assets and encourage innovation. Spanning functional, geographical and corporate boundaries, virtual team members work together on various projects but are based in different locations nationwide or worldwide.
We propose a class of two factor dynamic models for duration data and related risk analysis in finance and insurance. Empirical findings suggest that the conditional mean and (under) overdispersion of times elapsed between stock trades feature various patterns of temporal dependence.
This paper evaluates the role of various volatility specifications, such as multiple stochastic volatility (SV) factors and jump components, in appropriate modeling of equity return distributions.
Simulation-based estimation methods have become more widely used in recent years. We propose a set of tests for structural change in models estimated via simulated method of moments (see Duffe and Singleton (Econometrica 61 (1993) 929).
To enhance our understanding of emerging markets, we study a data set from the Casablanca stock exchange containing all the transaction records over a long span. The exchange was included in 1996 in the International Finance Corporation (IFC) data base roughly 3 years after important market reforms.
We examine several autoregressive-based estimators for the parameters of a moving average process, including the estimators initially proposed by Galbraith and Zinde-Walsh [1994] and Gouriéroux, Monfort and Renault [1993]. We also propose over-identified asymptotic-least-squares based variants of the former, and extensions of the latter based on Gallant and Tauchen's [1996] simulated method of moments.
Time series are demeaned when sample autocorrelation functions are computed. By the same logic it would seem appealing to remove seasonal means from seasonal time series before computing sample autocorrelation functions. Yet, standard practice is only to remove the overall mean and ignore the possibility of seasonal mean shifts in the data.
The paper evaluates the performance of several recently proposed tests for structural breaks in the conditional variance dynamics of asset returns. The tests apply to the class of ARCH and SV type processes as well as data‐driven volatility estimators using high‐frequency data.
We use panel data on ISO 9000 quality certification in 85 countries between 1993 and 1998 to better understand the cross-national diffusion of an organizational practice. Following neoinstitutional theory, we focus on the coercive, normative, and mimetic effects that result from the exposure of firms in a given country to a powerful source of critical resources, a common pool of relevant technical knowledge, and the experiences of firms located in other countries. We use social network theory to develop a systematic conceptual understanding of how firms located in different countries influence each other's rates of adoption as a result of cohesive and equivalent network relationships.
In this paper we examine the prevalence of data, specification, and parameter uncertainty in the formation of simple rules that mimic monetary policymaking decisions. Our approach is to build real-time data sets and simulate a real-time policy-setting environment in which we assume that policy is captured by movements in the actual federal funds rate, and then to assess what sorts of policy rule models and what sorts of data best explain what the Federal Reserve actually did.
The importance of geographic location to different types of industries is tested for by linking the geographic concentration in manufacturing industries to industry specific characteristics, most notably the relative importance of knowledge spillovers. The spatial distribution of innovative capacity as well as the geographic concentration of production are examined.
