Nonparametric estimation for self-selected interval data collected through a two-stage approach

Self-selected interval data arise in questionnaire surveys when respondents are free to answer with any interval without having pre-specified ranges. This type of data is a special case of interval-censored data in which the assumption of noninformative censoring is violated, and thus the standard methods for interval-censored data (e.g. Turnbull’s estimator) are not appropriate because they can produce biased results. Based on a certain sampling scheme, this paper suggests a nonparametric maximum likelihood estimator of the underlying distribution function. The consistency of the estimator is proven under general assumptions, and an iterative procedure for finding the estimate is proposed. The performance of the method is investigated in a simulation study.     

Fairness in Financial Markets: The Case of High Frequency Trading

Recent concern over “high frequency trading” (HFT) has called into question the fairness of the practice. What does it mean for a financial market to be “fair”? We first examine how high frequency trading is actually used. High frequency traders often implement traditional beneficial strategies such as market making and arbitrage, although computers can also be used for manipulative strategies as well. We then examine different notions of fairness. Procedural fairness can be viewed from the perspective of equal opportunity, in which all market participants are treated alike. The same rules apply to HFT as to other traders. Another approach to fairness is in the equality of outcomes. Many HFT strategies are beneficial to other market participants, so one cannot categorically denounce the practice as unfair. Other strategies, for both high and low frequency trading, are not. It is thus important to distinguish between the technology and the use of the technology to make judgments on fairness.     

Determinants of Electronic Word-of-Mouth on Social Networking Sites About Negative News on CSR

Journal of Business Ethics - Social network sites are a new communication channel to convey CSR information. They are interactive channels that let users participate, spread content and generate...     

Picking Your Tick: Toward a New Theory of Stock Splits

Thanks to stock splits, the average nominal share price has been amazingly stable in the United States. The average NYSE share price has fluctuated within the $30 to $40 range since the late 1930s—a period in which most consumer prices have increased by a factor of 10 and the S&P index has risen over 1,500%. Why has this nominal price been so stable when every other price has increased so much? And why do typical stock prices vary so greatly among different countries? For example, the median nominal stock price ranges from about $2 in Hong Kong and $7 in the U.K., to $103 in France and over $600 in Switzerland. The author's recent research suggests that typical stock prices vary across countries in ways that reflect primarily differences in how markets in each country set their “tick” rules—the rules governing the minimum price variation that can occur in a stock (in the U.S., for example, the tick was recently reduced from $1/8 to $1/16). Companies, on average, appear to respond to the resulting differences in tick size by adjusting the number of their shares outstanding so that the tick size relative to the nominal share price remains relatively constant. In fact, a tick size equal to about 25 basis points of the median share price “appears to be a universal norm” across global markets. This article explores how and why a company might wish to affect the relative tick size for its stock by splitting—and, in so doing, it suggests a “new theory” of stock splits. The theory also suggests that the optimal tick size for any given company will vary according to its size, visibility, and riskiness.     

Lean production and supplier relations: a survey of practices in the Aragonese automotive industry

This paper analyzes the main results of a survey to 28 automotive suppliers in the Spanish region of Aragón. It has been found in a regression analysis that the rotation of tasks and teamworking are positively correlated with the training and the use of modular components. Nearly half of the companies cooperate with customers, suppliers, and technological centers to improve their production processes but only two companies cooperate with their customers in component development and design which indicates an underinvolvement of the surveyed companies with the automotive manufacturers. The companies are much more integrated with the automakers in the delivery process, since more than half of the companies have daily deliveries and directly to the assembly line of the automaker.     

WHEN‐ISSUED SHARES,SMALL TRADES,AND THE VARIANCE OF RETURNS AROUND STOCK SPLITS

The increases in volatility after stock splits have long puzzled researchers. The usual suspects of discreteness and bid‐ask spread do not provide a complete explanation. We provide new clues to solve this mystery by examining the trading of when‐issued shares that are available before the split. When‐issued trading permits noise traders to compete with a more homogenous set of traders, decreasing the volatility of the stock before the split. Following the split, these noise traders reunite in one market and volatility increases. Thus, the higher volatility after the ex date of a stock split is a function of the introduction of when‐issued trading, the new lower price level after the split date, and the increased activity of small‐volume traders around a stock split.