‘Driving Forces of Investment Decisions in Mutual Funds’

Unfortunately, in India it is a fact that most of the investors are not interested in mutual funds. Those who are investing, they are investing only very small amounts. But what is important to be noted here is that when compared to other financial instruments, investments in mutual funds are safer and also yields more returns on the investment portfolio. Moreover as an investment avenue mutual fund is available for those investors who are not willing to take any exposure directly in the security market. It also helps such investors to build their wealth over a period of time. At the retail level, investors are unique and are highly heterogeneous, and the mutual fund schemes' selection will also differ depends on their expectations. Hence, investors’ expectation is a very important factor in this regard that needs to be analysed by all the investment houses. Hence, the factors that drive the investment decisions of individual investors to meet their expectations by investing money in mutual funds need an in-depth analysis. These driving forces include the preference of investors on mutual fund compared to various available avenues of financial investments, risk attitude of investors, influence of characteristics of instruments of mutual funds on investors, the investment specific attitudes of investors, and influence of qualities of fund management on investors. The success of any mutual fund, a popular means of investment, depends on how effectively an Asset Management Company has been able to understand the level of influence of these factors on the decision of investors to invest in mutual funds. For a substantial growth in the mutual fund market, there must be a high level precision in the design and marketing of the products of mutual funds taking into account these driving forces by the Asset Management Companies. Therefore, there is a need to conduct a detailed study on investments in mutual funds in this direction. A review of available literature also revealed that no detailed study on mutual funds has so far been attempted in this direction; hence the present study on Driving Forces of Investment Decisions in Mutual Funds is undertaken.

High Selectivity Filter Employing Stepped Impedance Resonators, Series Capacitors and Defected Ground Structures for Ultra Wide Band Applications

The paper presents a compact planar Ultra Wide Band ¯lter employing folded stepped impedance resonators with series capacitors and dumb bell shaped defected ground structures. An interdigital quarter wavelength coupled line is used for achieving the band pass characteristics. The transmission zeros are produced by stepped impedance resonators. The ¯lter has steep roll o® rate and good attenuation in its lower and upper stop bands, contributed by the series capacitor and defected ground structures respectively.

Alignment by Maximization of Mutual Information

A new information-theoretic approach is presented for finding the pose of an object in an image. The technique does not require information about the surface properties of the object, besides its shape, and is robust with respect to variations of illumination. In our derivation, few assumptions are made about the nature of the imaging process. As a result the algorithms are quite general and can foreseeably be used in a wide variety of imaging situations. Experiments are presented that demonstrate the approach registering magnetic resonance (MR) images with computed tomography (CT) images, aligning a complex 3D object model to real scenes including clutter and occlusion, tracking a human head in a video sequence and aligning a view-based 2D object model to real images. The method is based on a formulation of the mutual information between the model and the image called EMMA. As applied here the technique is intensity-based, rather than feature-based. It works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust than traditional correlation. Additionally, it has an efficient implementation that is based on stochastic approximation. Finally, we will describe a number of additional real-world applications that can be solved efficiently and reliably using EMMA. EMMA can be used in machine learning to find maximally informative projections of high-dimensional data. EMMA can also be used to detect and correct corruption in magnetic resonance images (MRI).

Estimation of zero-sequence impedance of undergrounds cables for single-phase fault location in distribution systems with electric arc

This paper focus on the problem of locating single-phase faults in mixed distribution electric systems, with overhead lines and underground cables, using voltage and current measurements at the sending-end and sequence model of the network. Since calculating series impedance for underground cables is not as simple as in the case of overhead lines, the paper proposes a methodology to obtain an estimation of zero-sequence impedance of underground cables starting from previous single-faults occurred in the system, in which an electric arc occurred at the fault location. For this reason, the signal is previously pretreated to eliminate its peaks voltage and the analysis can be done working with a signal as close as a sinus wave as possible

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An image of a user receiving help at Hartley Library reception desk, taken 2009.

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An image of a user receiving help at Hartley Library reception desk, taken 2009

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An image of a user receiving help at the Hartley Library reception desk, taken 2009

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