OWNERSHIP STRUCTURE AND FIRM CASH HOLDINGS: EVIDENCE FROM THE PUBLIC FLOAT IN IPOS

Department of Management EngineeringI examine the effect of insider ownership on the level of cash holding by measuring the percentage of shares issued to the public, namely public float. Using a sample of 4,402 IPOs between 1990 and 2013, I find that public float has significantly negative relation with the level of firm???s cash holdings. Specifically, the reduced insider ownership by large percentage of shares issued to the public seems to motivate insiders to waste more cash, resulting in decrease in the level of cash holding. This relation persists even after controlling for various firm characteristics. High public float (or small insider ownership) also exacerbate agency problem evidenced by public float being positively associated with discretionary accrual proxy for agency problem. The level of cash holding reduced further when we interact public float with discretionary accrual term. Collectively, this finding suggests that large sales in insider ownership in IPO market worsen the agency problem and consequently motivate insider to squander firm???s cash holding.ope

Shapeshift

http://deepblue.lib.umich.edu/bitstream/2027.42/91012/1/maithitr_1334787374.pd

Collaborative dispositions, knowledge co-construction and monitoring in collaborative problem solving

Abstract. Dispositions are trends or frequencies of acts performed consciously, habitually and automatically, influenced by beliefs, attitudes, personal values or commitments (Katz & Raths, 1985; NCATE, 2002). Thus, collaborative learning dispositions are students’ commitments, beliefs, contributions, or attitudes towards collaboration (Wu, Ho, Lin, Chang, & Chen, 2013). In a similar context, a person who has a certain level of disposition will display certain behaviors, so dispositions can be used to predict behaviors that may occur (Katz & Raths, 1985). To enhance learners’ collaborative learning skill, it is necessary to start from teacher students’ collaborative learning dispositions, which may potentially have impacts on their future students’ learning opportunities. This study aims to investigate what kinds of activities students focus on during collaborative learning processes. Also, the research explores whether there is any difference in the way students demonstrate and contribute diversely in group work when they possess different collaborative disposition scores, measured by questionnaires, which were based on research by Wang, MacCann, Zhuang, Liu and Roberts (2009). Videos from five groups of teacher students (N = 14) were collected and observed. First, the process-oriented qualitative analysis was carried out to assign meaningful events to separate categories of knowledge co-construction and monitoring activities. Then, quantitative analyses were conducted to explore activities executed most regularly as well as correlation between collaborative disposition scores and students’ contributions. The results of video data, gathered from the PREP21 project indicate that participants were actively sharing ideas, showing approval or disapproval about members’ contributions. Also, they frequently monitored how group tasks had progressed, then suggested following actions. Unexpectedly, there was no considerable relationship between measured collaborative disposition levels and enacted individual level collaborative problem-solving contributions. However, in a case study analysis, active and passive students displayed differently. Additionally, interconnection between knowledge co-construction and monitoring was shown

Freedom on the Net 2014 - Tightening the Net: Governments Expand Online Controls (Summary)

Internet freedom around the world has declined for the fourth consecutive year, with a growing number of countries introducing online censorship and monitoring practices that are simultaneously more aggressive and more sophisticated in their targeting of individual users. This booklet is a summary of findings for the 2014 edition of "Freedom on the Net.

Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media

Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for brands to observe user opinions about their products, this task is thus increasingly crucial. However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels. This kind of data makes the existing works suffer from many difficulties to handle, especially ones using deep learning approaches. In this paper, we propose an approach to handle this problem. This work is extended from our previous work, in which we proposed to combine the typical deep learning technique of Convolutional Neural Networks with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. In this work, we further improve our architecture by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, the combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements, specifically aiming to handle short and informal messages, help us to enjoy significant improvement in performance once experimenting on real datasets.Comment: A Preprint of an article accepted for publication by Inderscience in IJCVR on September 201