No abstract

This paper presents a formal analysis of the relation between monitoring and limit order submission risk. With heterogeneous information, limit order traders face two types of risk. First, they may be ''picked off'' when prices change unexpectedly after the limit order is entered (known as free trading option risk). Second, they face the possibility that their limit order will not result in a trade. To mitigate these risks, traders can monitor information and prices and cancel or revise their orders as needed. But such monitoring is costly, resulting in a trade-off between the cost of monitoring and the risks of limit order submission. The model predicts that if the stock is actively traded, limit order submission risks and order cancellations/revisions are positively related. Further, shares with a wide bid-ask spread will tend to have a lower rate of order cancellations and revisions than shares with small bid-ask spreads. Finally, the model suggests that if larger capitalization stocks have lower costs of gathering information (and hence more intense monitoring of limit orders), there will be more cancellations and revisions in limit orders. A sample of 23 liquid stocks provides evidence that is consistent with these three main hypotheses. r

The findings have significant implications for promoting quality outcomes through models of palliative care service delivery in residential facilities.

The checklist can be used to support the integration of specialist palliative care into residential care to drive up quality care, provide staff with focused case-based education, maximise planning and reduce symptom burden for people at end of life.

We model and predict that politicians have incentives to delay bank failure in election years and that this incentive is exacerbated if the election is close. Our empirical application using the US data supports these predictions. At the bank level, we show that bank failure in an election year is four times less likely to occur if the election was among the most competitive (top quartile). At the state level, bank failure is about 1.8 times less likely to occur in an election year. A three point swing in the competitiveness of the election increases this election year bias to 2.2.