The Black Box of Mutual Fund Fees

This paper re-examines the determinants of mutual fund fees paid by mutual fund shareholders for management costs and other expenses. There are two novelties with respect to previous studies. First, each type of fee is explained separately. Second, the paper employs a new dataset consisting of Spanish mutual funds, making it the second paper to study mutual fund fees outside the US market. Furthermore, the Spanish market has three interesting characteristics: (i) both distribution and management are highly dominated by banks and savings banks, which points towards potential conflicts of interest; (ii) Spanish mutual fund law imposes caps on all types of fees; and (iii) Spain ranks first in terms of average mutual fund fees among similar countries. We find significant differences in mutual fund fees not explained by the fund’s investment objective. For instance, management companies owned by banks and savings banks charge higher management fees and redemption fees to nonguaranteed funds. Also, investors in older non-guaranteed funds and non-guaranteed funds with a lower average investment are more likely to end up paying higher management fees. Moreover, there is clear evidence that some mutual funds enjoy better conditions from custodial institutions than others. In contrast to evidence from the US market, larger funds are not associated with lower fees, but with higher custody fees for guaranteed funds and higher redemption fees for both types of funds. Finally, fee-setting by mutual funds is not related to fund before-fee performance.

MW, Medical Watches

My project is a business plan about the set up of a company and the development of a new and innovative product aimed for the elders. I decide do this project when I discover that one of the more important needs that have the elders is to remember the medicines that they have to take. I thought that a good way could be through a smart watch. My watch have an only function, is a cheap device, easy to use, easy to understand and easy to set up, because the elders usually do not know to use complex electronics devices. There are other similar smart watches and other devices but do not have the necessary characteristics to be a good reminder for elders. My watch is centred to improve the life of the elders, but my product could also be useful for ill people who have to take many medicines during the day. After realizing this business plan, I have proved that my company is viable in the environment and profitable in the market.

Towards Application of One-Class Classification Methods to Medical Data

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques-Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description-using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.