Because the all or some could me working different way then others<span />
Answer:
e). None of the above, because a perfect hedge does not exist
A perfect hedge is nearly impossible
Explanation:
A perfect hedge is a position undertaken by an investor that would eliminate the risk of an existing position, or a position that eliminates all market risk from a portfolio. In order to be a perfect hedge, a position would need to have a 100% inverse correlation to the initial position.
At the time of taking an opposite position in Derivatives Market, Perfect Hedge would mean covering the risk involved in the Cash Market Position completely, i.e. 100%. 2. Imperfect Hedge: When the position in the cash market is not completely hedged or not hedged to 100%, then such a hedge is called Imperfect Hedge.
Answer: $42,400
Explanation:
The family currently spends $40,000 on living expenses.
Inflation is expected to be 6% and as Inflation is used to refer to the general rise in prices, this means that the family will be spending 6% more in one year.
They will therefore be spending;
= 40,000 * (1 + 6%)
= $42,400
Answer:
The options for this question are the following:
a. 1
b. 2
c. 0.5
d. 1.5
The correct answer is a. 1
.
Explanation:
Group analysis or grouping is the task of grouping a set of objects in such a way that the members of the same group (called a cluster) are more similar, in some sense or another. It is the main task of exploratory data mining and is a common technique in the analysis of statistical data. It is also used in multiple fields such as machine learning, pattern recognition, image analysis, information search and retrieval, bioinformatics, data compression and graphic computing.
Group analysis is not in itself a specific algorithm, but the task pending solution. Clustering can be done using several algorithms that differ significantly in your idea of what constitutes a group and how to find them efficiently. Classical group ideas include small distances between members of the group, dense areas of the data space, intervals or particular statistical distributions. Clustering, therefore, can be formulated as a multi-objective optimization problem. The appropriate algorithm and the values of the parameters (including values such as the distance function to use, a density threshold or the number of expected groups) depend on the set of data analyzed and the use that will be given to the results. Grouping as such is not an automatic task, but an iterative process of data mining or interactive multi-objective optimization that involves trial and failure. It will often be necessary to pre-process the data and adjust the model parameters until the result has the desired properties.
