SIT718 - Using Aggregation Functions For Data Analysis And European Stock Market Prediction Data sheet - Statistics Assignment Help

Assignment Task -

European Stockmarket Prediction Dataset :

The given dataset, ”EuStockMarkets2.txt”, can be used to create models of prediction of stockmarkets. The dataset provides the values of European stockmarket indices using 1200 measurements. The dataset includes 4 variables, denoted as X1, X2, X3 and X4, described as follows: 

X1- DAX The blue chip stock market index that comprises 30 major German companies trading on the Frankfurt Stock exchange and is also called DAX30. The index carries similar weight as Dow Jones in US and FT30. 

X2 – SMI The Swissm Market Index (SMI)] is Switzerland’s blue-chip stock market index which makes it the most followed in the country. It is made up of the 20 of the largest and the most liquid Swiss Performance Index (SPI) stock. As a price index, the SMI is not adjusted for dividends. 

X3-CAC The French CAC40 (Cotation Assiste´e en Continu) is a benchmark French stock market index. The index represents a capitalisation-weighted measure of the 40 most significant among the 100 largest market caps on the Euronext Paris, formerly the Paris Burse. 

X4-FTSE The Financial Times Stock Exchange Group (FTSE) is an independent organisa tion. It specialises in creating index offerings for the global financial markets. An index will represent a market segment and its hypothetical portfolio of stock holdings.

Tasks:

1. Understand, clean (if necessary) and prepare the data 

(i) Download the txt file (EuStockMarkets2.txt) from CloudDeakin and save it to your R working directory. 

(ii) Assign the data to a matrix, e.g. using 

the.data <- as.matrix(read.table("EuStockMarkets2.txt")) 

(iii) Your variable of interest is X4, the FTSE index. Generate a subset of 500 data, e.g. using: 

my.data <- the.data[sample(1:1200,500),c(1:4)] 

(iv) Using descriptive statistics, find the main statistical characteristics of all variables. Include assessment of the the outliers using boxplots (4 boxplots). Record your results in a table (Table 1).

(v) Using scatterplots and histograms, report on the general properties of each vari able Xi, i=1,2,3,4 and relationship (if any) between each of the variables X1,X2, X3and your variable of interest X4 (FTSE). Include a scatter plot for each of the variables Xi, i=1,2,3,4, (4 scatterplots) and each pair of variables, e.g. X1 and X4, X1 and X2, etc. (6 scatterplots). Include a histogram for each variable X1,X2,X3 and X4 (4 histograms). Include 1 or 2 sentences about the relationships and distributions. 

2. Transform the data 

(i) Apply transformations to all variables, X1, X2, X3, to model X4. 

Make appropriate transformations to the variables so that the values can be aggregated in order to predict the variable of interest X4 (FTSE). The transformations should reflect the general relationship between each of the four variables and the variable of interest. Assign your transformed data along with your transformed variable of interest to an array (it should be 500 rows and 5 columns). Save it to a txt file titled ”name-transformed.txt” using 

write.table(your.data,"name-transformed.txt",) 

(ii) Briefly explain each transformation for all variables. You may also include mathe matical formulae if appropriate (1- 2 sentences each).

(iii) Include the histograms of all four transformed variables in your report (4 histograms). 

3. Build models using aggregation functions and investigate the importance of each vari able.

(i) Download the AggWaFit.R file (from CloudDeakin) to your working directory and load into the R workspace using, 

source

(ii) Use the fitting functions to learn the parameters for 

• Weighted arithmetic mean (WAM), 

• Weighted power means (PM) with p = 0.5, and p = 2, 

• Ordered weighted averaging function (OWA), and 

• Choquet integral.

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