On the Welfare Significance of National Product for Economic Growth and Sustainable Development

This paper formulates an investment value transversality condition in a continuous-time growth model, which characterizes competitive paths along which current net national product measures the welfare achieved along the path. This transversality condition requires that the present value of net investment goes to zero asymptotically. An example provided shows that, in general, competitive paths do not necessarily satisfy this condition. It is also shown that, in a standard growth model including an exhaustible resource as an essential factor of production, competitive paths always satisfy this condition. Implications regarding national income accounting procedures and sustainable development policies are discussed.
JEL Classification Numbers: D90; O11; O41; Q32.     

The Information Content of Dividend Initiations and Firm Size: An Analysis Using Bid-Ask Spreads

The bid-ask spread of stock prices is examined for a sample of dividend initiating firms. The average percentage and dollar bid-ask spreads increase significantly on the day preceding the Wall Street Journal Index announcement date, possibly reflecting, on average, the market maker's anticipatory uncertainty. The day -1 increase in spread is inversely associated with firm size, an information environment proxy, after considering the simultaneous effects of dividend yield, returns variance, dollar trading volume and share price. The average percentage spread declines significantly on day 0 from its day -1 level and remains lower, on average, over a 365 day post-announcement period than 90 day pre-announcement levels. Similar results are obtained for dollar spread averages. The post-announcement percentage spread decline suggests a resolution of uncertainty, and is positively associated with the dividend yield. Dividend initiation announcements appear to reduce informational asymmetry.     

开放式基金在上升市场与下降市场中选时与选股能力的实证研究

选取我国10只开放式基金作为样本,根据它们的收益情况并联系市场背景,对它们基金管理人的选时与选股能力进行的实证研究表明,不同基金在市场上升和市场下降过程中所表现出来的选时能力指标和选股能力指标与整体指标并不相同.这一结论可用于基金整体性指标研究.     

An E-Valuation Framework for Developing Net-Enabled Business Metrics Through Functionality Interaction

Managers engaged in net-enabled business planning seek metrics to help them analyze the success of their e-business investments. Likewise, researchers require metrics to build analytical models and conduct empirical research on the impact of e-business strategies on firm performance. In this article, the authors develop a comprehensive E-Valuation Framework for identifying net-enabled applications and their resulting user-based functionalities for activities across the value chain. The authors propose that the real value from net-enabled applications can be found in functionality interactions, where one application enables or enhances functionality in another application. The comprehensive framework can be used to generate three types of metrics managers can use to evaluate their net-enabled strategic initiatives. Further, a classification of net-enabled organizations provides the basis for selecting applications critical to a firm's strategic thrusts. We make use of the resource-based view of the firm and real-options analysis to discuss how successful application deployment is based on the resources and assets the firm possesses as well as managing the rollout of an applications portfolio over time. The framework allows managers to map their organization's net-enabled initiatives into a coherent, easily understood visual representation and provides direction for researchers evaluating the efficacy of net-enabled business strategies.     

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.