IDENTIFICATION OF THREE ESSENTIAL ELEMENTS IN RESEARCH PROPOSAL
A research proposal basically has nine elements that make the research proposal attractive and effective. Out of the nine elements, three elements are the most essential in a research proposal. The three elements are as follows;
Statement of the Problem
The first element is providing the reader with clear and concise statement of the research or the goals of the project. In simpler words, this section includes the specific question(s) that must be answered along with the significance of the research proposal (Sekaran, 2006). In addition, it has also been indicated in several researches that this particular section provides an explanation of how the expected result could/will contribute to the existing body of knowledge. This section work towards seeking answers to particular questions. As the question leads to a particular problem, it becomes necessary to solve the problem through thorough research.
Literature Review
This section of the proposal aims to provide the reader with understanding of relevant bodies of literature along with the information regarding how the information fits in the context. The literature review could be regarded as the technique to develop broad knowledge of what is already known in the field along with questions that are still unanswered. This section is particularly known for narrowing the scope of investigation. By doing so, the researcher is able to highlight theories that could be of significant help to support the developed hypothesis (Maylor, r& Blackman, 2005). The literature review aims to seek the latest information to seek acceptance and to convince the audience that the research is conducted on the basis of recent and latest information (Patton, 2002). In addition, the process of literature review assures that the investigation being conducted is not focused on reinventing the wheel but to extend the scope of knowledge.
Methodology
Last but not the least important element in the research proposal is the section where the collection of information along with its analysis is discussed. This information that is discussed in this particular section includes 1) instruments that will be used, 2) importance of instruments and its relevance for the study, 3) evidence of instrument’s reliability and validity, 4) procedures to be followed in data analysis. Similarly, the proposed methodology should be concise and precise to provide enough detail to indicate that the researcher has significant knowledge and information of what is being done in the research (Jankowicz, 2005).
The methodology in research proposal can be simple put as the overall design of the research project that consists of methods and procedures that would be used in the research. The research design could either be quantitative or qualitative in nature. It is also possible that the research project could have mixture of both the approaches (Saunders, Thornhill, & Lewis, 2009).
IDENTIFICATION OF THREE FACTORS LEADING TO POOR RESEARCH PROPOSAL
The overall acceptance rate has provided significant information regarding the reasons due to which research proposals were rejected outright. It has been indicated that in the year 2003, 240 proposals were presented from which only 39 proposals i.e. representing 16 percent of the proposals were accepted forthright while 91 proposals were rejected outright. It was also indicated that the rest of the proposals were submitted after significant revision of the content. The basic problem associated with the problem was lack of quality information along with several other problems (Kikula, and Qorro, 2007).
The three factors that eventually lead to poor research proposal are as follows;
Introduction of the Proposal
The major factor that leads to poor research proposal is the inability of the researcher to introduce the topic. It has been indicated that 72 percent of the proposals that were rejected were due to unsatisfactory introduction in the research proposal. For a research proposal to be accepted outright, it must include proper idea and use of proper vocabulary to gain reader’s attention. The use of improper and irrelevant information in the introduction also leads to poor research proposal. On the other hand, use of outdated and obsolete data and information also was the prominent reason for the rejection of research proposals. It is quite evident that use of outdated data and information in the introduction of research proposal leads to problems in the study.
It has also been indicated that researchers are usually misrepresenting the data to gain reader’s attention. This is one of the major factors that lead to poor research proposal.
Appropriateness/Adequacy of Literature Review
Literature is the most prominent section that is not taken seriously by most of the researchers in their study. The lack of researcher’s interest to conduct literature review was one of the identified factors that led to poor research proposals. For the researcher to review appropriate literature is quite essential as it provides the basis for the rest of the proposal. With inadequacy in the literature review along with lack of focus of the researchers while writing research proposal accounted for more than 50 percent. Such high percentage eventually reduces the researcher’s ability to produce effective and breathtaking research proposal. On the other hand, it has also been indicated that researcher’s lack of knowledge and inability to gain critical literature was also among major reasons to provide consistent and accurate literature in the research proposal.
Appropriateness of Methods and Research Instruments
It has been indicated in a research that most of the researchers have the ability to design appropriate methods that would be used in the research proposals. But clarification of the appropriate research method and research instrument was found to be a major problem in the research proposals. The use of appropriate method in the research proposal does not lead to poor research proposal but lack of clear presented idea certainly does. On the other hand, lack of information regarding the method or instrument that will be used in the research proposal also makes the research proposal to look weak and poor quality. For a researcher to reduce such factor, he/she must provide relevant information regarding the method to be used in the research along with particular reasons associated with the use of particular methods and instruments.
Mixtures of Normal Distributions and Their Applications
Introduction
Mixture of normal distributions has been considered by variety of mathematicians as it has a long history in the field of statistics. The first ever person to use the concept of mixture of normal distribution was Newcomb. The idea was used to handle large files. As the concept of mixture of normal distributions is quite flexible, it is used to analyze the financial records.
The project would focus on discussing the mixture of normal distributions along with its analysis by using mixtools package of R. In addition, the application of mixture of normal distributions would also be the concern of the analysis. This will significantly help to evaluate and observe the process of fitting log-return with a mixture of normal distributions.
Problem Statement
Log return is the most prominent practice used by variety of people rather than price or raw return. The benefits associated with the use of Log-return have made the practice unique to various users in various ways. The long-tailed distribution entails the use of hypothesis that focuses on describing the distribution of change in price through variety of normal contribution under the same mean but with different variances. The departures could be explained with the help of normality that highly relies on the use of mixtures of distributions.
This research project would highlight the concept of log-return with the mixtures of normal distribution. The process to fit log-return with a mixture of normal distribution would be the main theme of the proposal. In order to do so, the Austral Stock would be taken into consideration. The data that would be used include the daily closing prices of stock from January 2003 to March 2013 particularly for the Austral Stock Market indices.
Aims and Objectives
The aim of the project would be to elaborate the model that fits the log-return with a mixture of normal distribution. The research proposal’s aim would be to provide relevant answers to the following questions;
- What is the concept of log-return with a mixture of normal distribution?
- What is the process of fitting log-term with a mixture of normal distributions?
- What is the rationale of using normal mixture log returns?
- What is the possible way of fitting log-return using option price?
Literature Review
Normal distribution is one of the most prominent and commonly used models to analyze the daily changes in market variables. Different research studies have revealed that the concept of return in equity, foreign exchanges, and the commodity markets are consistently analyzed with fat tails. It has also been indicated that the concept and assumptions associated with normality are often inappropriate that eventually leads to flaws in the findings (Wang, 2001). On the other hand, the concept of mixture of normal is quite flexible in analyzing the daily changes in the market data. The kurtosis and skewness in the market are the dominant variables that are taken into account with such mixtures. In addition, it has also been revealed that the normal distribution, in particular, is a special case in terms of mixture of normal distributions (Wang, & Taaffe, 2000).
The concept of normal distribution could be described as the distribution of data that is symmetrical. In addition, the concept could also be explained as the distribution of data that forms a bell shaped curve when plotted. On the other hand, Kurtosis is the measurement of peak i.e. high or flat, while the concept of skewness focuses on the measurement of symmetry in terms of data. This eventually indicates that the mixture of normal distributions can easily be created by adding variety of normal distributions with different kurtosis and skewness at the same time. In addition, it has been identified that the mixture of normal distribution can significantly accommodate the characteristics and even the non-normality of the data.
The mixture of normal distributions is quite flexible method to analyze and model wide variety of random phenomena due to which, the concept of mixture of normal distributions has remained the focus of variety of users. Moreover, the concept of mixture of normal distribution has also been playing an essential role in marketing, economics and finance.
Mixtools package is also a prominent concept that focuses on the examination of sample of measurement to evaluate the subgroups of individuals associated with the sample. On the other hand, the finite mixture model focuses on the examination of subgroups rather than the identification of individuals to those subgroups (Benaglia, Chauveau, Hunter, & Young, 2009). One of the essential packages is known as ‘R’ mixtools. The aim of the package is to estimate the centers of the peak in the curve that are then termed as the means of the distributions. After doing so, the standard deviations are adjusted with the mixing percentages to match the width of the peak along with the height. The algorithm that is consistently used in the package is known as the expectation-maximization (EM) algorithm. The formula that is used in the mixtools models is as follows;
fxt=π1∅1x+π2∅2x
fxt=π∅1μ1, σ21+(1-π)∅2μ2, σ22
With the help of the formulae, the normal values of one data could be significantly compared to the normal values for the second deviation. With the use of same data different values could be gained which would either be positive or negative.
Similarly, ARIMA model is another essential concept. ARIMA is an acronym for Auto Regressive Integrated Moving Averages. This indicates that the model is a combination of AR model (p) and integrated moving average model (q). In addition, it has also been indicated that the autoregressive model is a linear model that focuses on predicting the value of the present time series with the help of past values of the time series.
The AR model (p) is basically abbreviated as follows;
Return on Assets works as an indicator of profitability of the company. This means that the organization can evaluate the performance by measuring the return on assets (Gitman, 2003). In simpler words, return on assets is a fundamental concept that provides the organization with an insight regarding the efficient use of assets to generate significant amount of income (Brealey, Myers, Allen, & Mohanty, 2007).
The formula that is used to calculate the return on assets is:
Return on assets =Net income Total assets
The net income used in the formula indicates the income after tax that can be easily found on the income statement of organizations (Besley, & Brigham, 2007). On the other hand, the total assets are calculated by dividing the number of assets at the beginning and end of the year by ‘2’. The total assets can also be found on the balance sheet of two consecutive years.
The concept of log-return is also an essential concept to identify the mixture of normal distribution. The use of log-return provides the users with variety of benefits (McLaney, 2009) It has been observed that the use of log-return provides the individual with an ability to observe changes in the variable that can be directly compared with other variables that have different base values (Anderson, Sweeney, & Williams, 2011)
Methodology
Data would be collected through secondary means. All the data would already be available over the internet for the Austral Stock. The data that would be collected would provide essential information regarding the daily closing prices from January 2003 to March 2013. The mixture of normal distributions would remain the focus of the research proposal that will eventually help to evaluate the fitting log-return with mixture of normal distributions.
References
Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2011). Statistics for business and economics. Cincinnati: South-Western Pub.
Benaglia, T., Chauveau, D., Hunter, D. R., & Young, D. S. (2009). mixtools: An R package for analyzing finite mixture models. Journal of Statistical Software, vol. 32, no. 6, pp. 1-29.
Besley, S., & Brigham, E. (2007). Essentials of Managerial Finance, 14 edn. USA: Thomson Higher Education.
Brealey, R., Myers, S., Allen, F., & Mohanty, P. (2007). Principles of corporate finance. New York: McGraw-Hill.
Gitman, L. (2003). Principles of Managerial Finance. Boston: Addison-Wesley Publishing.
Jankowicz, A. (2005). Business Research Projects, London: Thomson Learning.
Khan, M. (1993). Theory & Problems in Financial Management. Boston: McGraw Hill Higher Education.
Kikula, I. and Qorro, M. (2007). Common Mistakes and Problems in Research Proposal Writing. Special Paper 07.24, Dar es Salaam, REPOA.
Maylor, H, & Blackman, K. (2005). Research Business & Management, Basingstoke. UK: Palgrave Macmillan.
McLaney, E. (2009). Business Finance: Theory and Practice. Pearson Education: New Jersey.
Patton, M. (2002). Qualitative research and evaluation methods. Thousand Oaks CA: Sage Publications Inc.
Ross, S., Westerfield, R., and Jordan, B. (2009). Fundamentals Of Corporate Finance Standard Edition. New York, McGraw-Hill.
Saunders, M, Thornhill, A, & Lewis, P. (2009). Research Method for Business Students. London: Financial Times Prentice Hall.
Sekaran, U. (2006). Research Methods for Business. NJ: John Wiley & Sons, Inc.
Wang, J. (2001). Generating daily changes in market variables using a multivariate mixture of normal distributions. In Proceedings of the 33nd conference on Winter simulation (pp. 283-289). IEEE Computer Society.
Wang, J., & Taaffe, M. R. (2000). Modeling and generating daily changes in market variables using a multivariate mixture of normal distributions. Available from http://ww2.valdosta.edu/~jwang/paper/MixNormal.pdf [Accessed 4 June 2013]
