The topic is : Cryptography for financial markets.
The purpose of this coursework is to understand the features of a set of managerial, economic or financial variables and be able to find how they are linearly related by using regression methods. Use Excel or Eviews to perform calculations.
Find a relevant research question associated to the dataset. The statistical analysis will help you to find answers to that question. Submit a written report intended for a professional audience. Your report should contain :
• Abstract – summarise objective, contents of the study and conclusions
• Introduction (300 words) – present a relevant financial question and its motivation/justification based on relevant references.
• Litterature review (300 words) – summarise the main sources related to the research question
• Methodology (500 words)- describe the methods and procedures used for this project
• Data description (600 words)- describe statistically the variables
• Analysis and Findings (600 words) – analyse the covariance/correlation structure of the data set taking into account the previous data description.
• Conclusions and Recommendations (200 words)
• References
Supplementary calculations and intermediate results should be supplied in appendices at the end of your report in the form of tables or graphs. Relevant and important graphs and tables should be part of the report.
There is maximum word limit of 2500 words. This is an INDIVIDUAL assignment. The assignment will be submitted as word document (.doc) on blackboard by 12th November 2021 (week 6) at 23.58. The Excel data file and excel output files related to the material presented on the assignment need to be uploaded to a specific shared folder by deadline. All the data should be organized in one Excel file named FIN5B1-StudentName-StudentNumber.
The following tasks will guide you through the econometric analysis:
1. Find a data set: Choose a data set with 3 variables (One different dataset per student). And organise the data in a Excel file. Describe the sample in terms of number of observations and frequency. Present the dataset using a relevant graph and explain the main features of each variable. Explain your intuition about dependent and independent variables.
2. Data description:
a. Discuss and describe your data as well as the reason(s) for your choice of data and using “Descriptive Statistics” for each variable (Variables X1,X2 and X3)
b. Plot all variables and comment on graph(s).
c. Transform your prices/level variables into returns. (Rename the variables: x1, x2, x3)
d. Describe statistically the transformed data set (plot it) and comment about your results.
e. Plot each Xi (xi) variable against the Xj (xj) variable in a scatter plot and comment about the relation between each pair of variables based on a covariances and correlation analysis.
3. Regression analysis:
a. Then regress the dependent variable against the set of independent variables with a multiple regression model or a set of simple regressions. Explain and interpret the results.
b. Analyse the results of the regression in terms of coefficients and associated t- statistics, R squared (adjusted) and F-statistic.
c. Perform a residual analysis – explain and interpret the results
d. Perform a robustness analysis – explain and interpret the results
e. Choose one of the estimators to build a relevant forecast – explain and interpret the results
4. Conclusions: Using the main results of your analysis, conclude about all these results trying to find ideas or models that could be used to improve the analysis.
