Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm

The extraction of useful information for decision making is a challenge in many different domains. Association rule mining is one of the most important techniques in this field, discovering relationships of interest among patterns. Despite the mining of association rules being an area of great interest for many researchers, the search for well-grouped continuous values is still a challenge, discovering rules that do not comprise patterns which represent unnecessary ranges of values. Existing algorithms for mining association rules in continuous domains are mainly based on a non-deterministic search, requiring a high number of parameters to be optimised. These parameters hinder the mining process, and the algorithms themselves must be known to those data mining experts that want to use them. We therefore present a grammar guided genetic programming algorithm that does not require as many parameters as other existing approaches and enables the discovery of quantitative association rules comprising small-size gaps. The algorithm is verified over a varied set of data, comparing the results to other association rule mining algorithms from several paradigms. Additionally, some resulting rules from different paradigms are analysed, demonstrating the effectiveness of our model for reducing gaps in numerical features

The Dynamics of Optimal Risk Sharing

We study a dynamic-contracting problem involving risk sharing between two parties – the Proposer and the Responder – who invest in a risky asset until an exogenous but random termination time. In any time period they must invest all their wealth in the risky asset, but they can share the underlying investment and termination risk. When the project ends they consume their final accumulated wealth. The Proposer and the Responder have constant relative risk aversion R and r respectively, with R > r > 0. We show that the optimal contract has three components: a non-contingent flow payment, a share in investment risk and a termination payment. We derive approximations for the optimal share in investment risk and the optimal termination payment, and we use numerical simulations to show that these approximations offer a close fit to the exact rules. The approximations take the form of a myopic benchmark plus a dynamic correction. In the case of the approximation for the optimal share in investment risk, the myopic benchmark is simply the classical formula for optimal risk sharing. This benchmark is endogenous because it depends on the wealths of the two parties. The dynamic correction is driven by counterparty risk. If both parties are fairly risk tolerant, in the sense that 2 > R > r, then the Proposer takes on more risk than she would under the myopic benchmark. If both parties are fairly risk averse, in the sense that R > r > 2, then the Proposer takes on less risk than she would under the myopic benchmark. In the mixed case, in which R > 2 > r, the Proposer takes on more risk when the Responder’s share in total wealth is low and less risk when the Responder’s share in total wealth is high. In the case of the approximation for the optimal termination payment, the myopic benchmark is zero. The dynamic correction tells us, among other things, that: (i) if the asset has a high return then, following termination, the Responder compensates the Proposer for the loss of a valuable investment opportunity; and (ii) if the asset has a low return then, prior to termination, the Responder compensates the Proposer for the low returns obtained. Finally, we exploit our representation of the optimal contract to derive simple and easily interpretable sufficient conditions for the existence of an optimal contract.

Leverage Choice and Credit Spread Dynamics when Managers Risk Shift

We develop a structural model of the leverage choices of risk-averse managers who are compensated with cash and stock. We further characterize credit spread dynamics over the life of the debt. Managers optimally balance the tax benefits of debt with the utility cost that results from their ex-post asset substitution choices. Our model predicts the existence of a U-shaped relationship between the cash component of pay and leverage levels: when cash compensation is low, safe debt with a high face value is issued and when cash compensation is high, risky debt with a high face value is issued. At moderate levels of the cash-to-stock value ratio low leverage is chosen but credit spreads can be significant and again relate to compensation terms. The model illustrates the quantitative importance of including agency costs in the tradeoff theory of capital structureCredit Spreads, Capital Structure, Agency Costs of Debt

Interactive Data Exploration with Smart Drill-Down

We present {\em smart drill-down}, an operator for interactively exploring a relational table to discover and summarize "interesting" groups of tuples. Each group of tuples is described by a {\em rule}. For instance, the rule $(a, b, \star, 1000)$ tells us that there are a thousand tuples with value $a$ in the first column and $b$ in the second column (and any value in the third column). Smart drill-down presents an analyst with a list of rules that together describe interesting aspects of the table. The analyst can tailor the definition of interesting, and can interactively apply smart drill-down on an existing rule to explore that part of the table. We demonstrate that the underlying optimization problems are {\sc NP-Hard}, and describe an algorithm for finding the approximately optimal list of rules to display when the user uses a smart drill-down, and a dynamic sampling scheme for efficiently interacting with large tables. Finally, we perform experiments on real datasets on our experimental prototype to demonstrate the usefulness of smart drill-down and study the performance of our algorithms

Optimal Investment Strategies and Performance Sharing Rules for Pension Schemes with Minimum Guarantee

There is a potential conflict of interest between a pension fund sponsor and future pensioners when they share unequally in the pension fund performance. Thus, when a scheme offers a yearly guaranteed minimum return to pensioners, as is presently the case with German Pensionskassen, the sponsors cannot afford to invest in risky assets and consequently, pensioners end up with safe but very low expected returns. We examine optimal investment strategies for sponsors under alternative performance sharing rules and seek the rules that are most beneficial to pensioners. We find that the current yearly performance sharing rule imposed on Pensionskassen could be tilted in favor of sponsors without impairing the welfare of pensioners. We also find that the welfare of pensioners would be greatly enhanced if the guaranteed minimum return were applied to the cumulative return since inception of the scheme rather than to yearly returns. The ensuing credit risk taken by pensioners on sponsors could be kept to a minimum by proper regulation; this would induce sponsors to adopt safe constant proportionality portfolio insurance (CPPI) style investment strategies.Private pension schemes, benefit-sharing rules, capital guaranteed products, constant mix strategies, constant proportionality portfolio insurance strategies, utility theory

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Stockholder and Bondholder Reactions To Revelations of Large CEO Inside Debt Holdings: An Empirical Analysis (CRI 2009-005)

We conduct an event study of stockholders’ and bondholders’ reactions to companies’ initial reports of their CEOs’ inside debt positions, as required by SEC disclosure regulations that became effective early in 2007. Results show that bond prices rise, equity prices fall, and the volatility of both securities drops at the time of disclosures by firms whose CEOs have sizeable pensions or deferred compensation. The results indicate a transfer of value from equity toward debt, as well as an overall destruction of enterprise value, when a CEO’s inside debt holdings are large

Robust Statistics

The first example involves the real data given in Table 1 which are the results of an interlaboratory test. The boxplots are shown in Fig. 1 where the dotted line denotes the mean of the observations and the solid line the median. We note that only the results of the Laboratories 1 and 3 lie below the mean whereas all the remaining laboratories return larger values. In the case of the median, 7 of the readings coincide with the median, 24 readings are smaller and 24 are larger. A glance at Fig. 1 suggests that in the absence of further information the Laboratories 1 and 3 should be treated as outliers. This is the course which we recommend although the issues involved require careful thought. For the moment we note simply that the median is a robust statistic whereas the mean is not. --