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In this study, we analyze the power of the individual return-to-volatility security performance heuristic (ri/stdi) to simplify the identification of securities to buy and, consequently, to form the optimal no short sales mean–variance portfolios. The heuristic ri/stdi is powerful enough to identify the long and shorts sets. This is due to the positive definiteness of the variance–covariance matrix – the key is to use the heuristic sequentially. At the investor level, the heuristic helps investors to decide what securities to consider first. At the portfolio level, the heuristic may help us find out whether it is a good idea to invest in equity to begin with. Our research may also help to integrate individual security analysis into portfolio optimization through improved security rankings.

Online social networks are increasingly important venues for businesses to promote their products and image. However, information propagation in online social networks is significantly more complicated compared to traditional transmission media such as newspaper, radio, and television. In this chapter, we will discuss research on modeling and forecasting diffusion of virally marketed content in social networks. Important aspects include the content and its presentation, the network topology, and transmission dynamics. Theoretical models, algorithms, and case studies of viral marketing will be explored.

Organization development is often mourned as stagnant or perhaps dead, but most of these declarations seem to be insular, being supported primarily by anecdotal or survey research among organization development scholars and practitioners. This exploratory study seeks a more objective understanding of the state of organization development by examining big data from the social media platform Twitter. Drawn from over 5.7 million tweets extracted through Twitter's Application Program Interface (API) during 2 months in 2018, this research approaches the state of organization development through a quantitative, abductive study utilizing social network analyses. Organization development is examined through its characteristics as a social network on Twitter and how it relates to and interacts with other familial networks from management and organization studies. Findings show that organization development is relatively inactive as a social network on Twitter, as compared to other familial networks, and the relationships between the organization development network and these familial networks tend to be ones of inequality. Organization development references familial networks much more than any of the familial networks reference organization development. This inequality in social media presence is particularly surprising since several of these familial networks were founded from the field and principles of organization development. We locate organization development's generalist status, as compared to familial networks' specialist status, as generating this interaction disparity drawing on recent research that suggests specialized fields fare better in times of rapid change compared to generalist fields. We discuss the potential for greater specialization of organization development with a reemphasis on its process philosophy and focus.

This chapter investigates the behavior of Reddit’s news subreddit users and the relationship between their sentiment on exchange rates. Using graphical models and natural language processing, hidden online communities among Reddit users are discovered. The data set used in this project is a mixture of text and categorical data from Reddit’s news subreddit. These data include the titles of the news pages, as well as a few user characteristics, in addition to users’ comments. This data set is an excellent resource to study user reaction to news since their comments are directly linked to the webpage contents. The model considered in this chapter is a hierarchical mixture model which is a generative model that detects overlapping networks using the sentiment from the user generated content. The advantage of this model is that the communities (or groups) are assumed to follow a Chinese restaurant process, and therefore it can automatically detect and cluster the communities. The hidden variables and the hyperparameters for this model are obtained using Gibbs sampling.