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What is the difference between an efficient portfolio and the optimal portfolio?
Wiki User
∙ 9y ago
The difference is that an efficient portfolio is one that offers the lowest risk for the greatest return or vice versa. An optimal portfolio is one that is preferred by investors because it is tailored specifically to the individual's risk preferences.
Wiki User
∙ 9y ago
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What is the difference between stratified random sampling and cluster sampling?
Basically in a stratified sampling procedure, the population is first partitioned into disjoint classes (the strata) which together are exhaustive. Thus each population element should be within one and only one stratum. Then a simple random sample is taken from each stratum, the sampling effort may either be a proportional allocation (each simple random sample would contain an amount of variates from a stratum which is proportional to the size of that stratum) or according to optimal allocation, where the target is to have a final sample with the minimum variabilty possible. The main difference between stratified and cluster sampling is that in stratified sampling all the strata need to be sampled. In cluster sampling one proceeds by first selecting a number of clusters at random and then sampling each cluster or conduct a census of each cluster. But usually not all clusters would be included.
How can you draw a statistics sample that assumes a 50-50 split on an issue?
100
What is the advantages and disadvantages of multiple regression analysis?
Advantages: The estimates of the unknown parameters obtained from linear least squares regression are the optimal. Estimates from a broad class of possible parameter estimates under the usual assumptions are used for process modeling. It uses data very efficiently. Good results can be obtained with relatively small data sets. The theory associated with linear regression is well-understood and allows for construction of different types of easily-interpretable statistical intervals for predictions, calibrations, and optimizations. Disadvantages: Outputs of regression can lie outside of the range [0,1]. It has limitations in the shapes that linear models can assume over long ranges The extrapolation properties will be possibly poor It is very sensitive to outliers It often gives optimal estimates of the unknown parameters.
Define conditional probability with an example?
there are 3 types of conditional probability: 1. the indicate: if antecedent happens, then evidence manifests itself example -> when tossing a coin, if it lands on tails, then you win the game 2. the subjunctive: if antecedent would happen, then evidence would manifest itself example -> when a coin was tossed, if it would have landed on tails, i would have won the game it is recommended for optimal bayesian inferences that your a priori distribution is indicative. If not, you could be dealing with inproper, uninformative, or hyper priors, which make decision-making and posterior determination more complex, if even possible. Posterior distributions could very well be subjunctive. Suppose i have won the game, i could have tossed tails, but i could also have tossed heads.
Advantages and disadvantages of data mining?
Advantages of Data MiningMarketing / RetailData mining helps marketing companies to build models based on historical data to predict who will respond to new marketing campaign such as direct mail, online marketing campaign and etc. Through this prediction, marketers can have appropriate approach to sell profitable products to targeted customers with high satisfaction. Data mining brings a lot of benefit s to retail company in the same way as marketing. Through market basket analysis, the store can have an appropriate production arrangement in the way that customers can buy frequent buying products together with pleasant. In addition, it also help the retail company offers a certain discount for particular products what will attract customers.Finance / BankingData mining gives financial institutions information about loan information and credit reporting. By building a model from previous customer's data with common characteristics, the bank and financial can estimate what are the god and/or bad loans and its risk level. In addition, data mining can help banks to detect fraudulent credit card transaction to help credit card's owner prevent their losses. ManufacturingBy applying data mining in operational engineering data, manufacturers can detect faulty equipments and determine optimal control parameters. For example semi-conductor manufacturers had a challenge that even the conditions of manufacturing environments at different wafer production plants are similar, the quality of wafer are lot the same and some for unknown reasons even contain defects. Data mining has been applied to determine the ranges of control parameters that lead to the production of golden wafer. Then those optimal control parameters are used to manufacture wafers with desired quality. GovernmentsData mining helps government agency by digging and analyzing records of financial transaction to build patterns that can detect money laundering or criminal activity. Disadvantages of data miningPrivacy IssuesThe concerns about the personal privacy have been increasing enormously recently especially when internet is booming with social networks, e-commerce, forums, blogs…. Because of privacy issues, people are afraid of their personal information is collected and used in unethical way that potentially causing them a lot of trouble. Businesses collect information about their customers in many ways for understanding their purchasing behaviors trends. However businesses don't last forever, some days they may be acquired by other or gone. At this time the personal information they own probably is sold to other or leak. Security issuesSecurity is a big issue. Businesses owns information about their employee and customers including social security number, birthday, payroll and etc. However how properly this information is taken is still in questions. There have been a lot of cases that hackers were accesses and stole big data of customers from big corporation such as Ford Motor Credit Company, Sony… with so much personal and financial information available, the credit card stolen and identity theft become a big problem. Misuse of information/inaccurate informationInformation collected through data mining intended for marketing or ethical purposes can be misused. This information is exploited by unethical people or business to take benefit of vulnerable people or discriminate against a group of people. In addition, data mining technique is not perfectly accurate therefore if inaccurate information is used for decision-making will cause serious consequence.
Is there a connection between the Sharpe optimal ratio and the CAPM?
The portfolio with the highest Sharpe ratio is on the efficient frontier, according CAPM. The Excel spreadsheet at the related link allows you to calculate a Sharpe optimal portfolio
What is the difference between feasible and optimal solution?
The optimal solution is the best feasible solution
What does the tangency point M represent in the Capital Market Line?
The tangency point M represents the optimal portfolio on the efficient frontier that is a combination of the risk-free asset and the risky portfolio with the highest Sharpe ratio. It represents the portfolio that maximizes the risk-adjusted return, providing the best trade-off between risk and return for an investor.
What is the difference between tolerance range and optimal range?
tolerance range is the abiotic conditions. Optimal range is the preferred range of a species
What are the differences between capital allocation line CAL and capital market line CML?
CML = CAL for the entire market, assuming everyone has the same mean variance expectations ( E(R), variances, correlations). CAL is just the CML for individual investors. CAL and CML both combine the risk free asset with the optimal portfolio, only with CML that optimal portfolio is the market portfolio (tangency point of CML).
State the difference between a feasible solution basic feasible solution and an optimal solution of a lpp?
the optimal solution is best of feasible solution.this is as simple as it seems
What has the author Eduardo S Schwartz written?
Eduardo S. Schwartz has written: 'Illiquid assets and optimal portfolio choice' -- subject(s): Portfolio management, Liquidity (Economics)
What is Efficient scale?
Scale efficiency is the potential productivity gain from achieving optimal size of a firm
What is optimal commodity taxation?
it is the best and most efficient form of taxing commodities with least if not non tax burden
Which behavioral skills are critical to ensure successful and optimal care during resuscitation?
Teamwork, leadership, efficient communication
Why all digestive enzymes have their optimal temperature at around 37C?
Digestive enzymes are optimal around 37C due to the temperature allowing for maximum metabolism. The enzymes are most efficient at their highest metabolism and activity.
What is the similar property between dynamic programming and greedy approach?
Both are using Optimal substructure , that is if an optimal solution to the problem contains optimal solutions to the sub-problems
