Numerical Matrix CSV -> Covariance Matrix

Question

I have a CSV file containing a numerical matrix with over two thousand New York Stock Exchange listed companies' value over two years.

It seems like it should be really simple - I want to attain a covariance matrix formed from the CSV matrix.

As far as I'm aware I simply need to:

1. Import the data as numerical matrix (just the data no headings etc) using the MATLAB Import Data button.
2. Press save as on the workspace variable and make e.g. NYSE.mat.
3. In my function call cov(NYSE.mat);

This should access the matrix and return a large covariance matrix from my data. The cov() function works when i manually input an example matrix of for example:

[5 0 3 7; 1 -5 7 3; 4 9 8 10];

But for some reason whenever I try to call cov(NYSE.mat); only one number is returned, rather than a covariance matrix.

Can anyone tell me where I'm going wrong? I've been trying to figure this out for a while now and I feel the answer should be really simple.

I'm running on MATLAB R2016a.

Answer 1

Not sure you need to manually save the workspace name in step 2. As part of your import process you once you click the import button the variable should be loaded into workspace with the name of the file (maybe NYSE)

Try Load('NYSE.mat') and see what appears in your workspace.

Once you figure out the name of the variable, calling the function with that.

Answer 2

If your CSV file is without any header lines (not sure if any CSV file needs them) and contains only numbers, you can simply call

x = importdata('<nameofyourcsv>.csv');

and then

c = cov(x);

or even do it in one step:

c = cov(importdata('nameofcsv.csv'));

This function is equivalent of clicking on the button you mention. However, if your file contains header lines, you have to specify its number and call this function with more parameters:

x = importdata('nameofcsv.csv',',', nh); % nh is a number of header lines

x will no longer be a regular MATLAB matrix but a structure with following fields: data, textdata, and colheaders. cov(x.data) will calculate covariance. Not sure why you would need to save anything in a mat file, though. If you wish, you would probably need to assign a value from a data field to a regular variable and then save such variable. Otherwise, you will save the whole x structure.

---

For instance, the following file, test.csv:

95.01,76.21,61.54,40.57,5.79,20.28,1.53
23.11,45.65,79.19,93.55,35.29,19.87,74.68
60.68,1.85,92.18,91.69,81.32,60.38,44.51
48.60,82.14,73.82,41.03,0.99,27.22,93.18
89.13,44.47,17.63,89.36,13.89,19.88,46.60

can be imported by calling importdata:

>> x = importdata('test.csv')
x =
   95.0100   76.2100   61.5400   40.5700    5.7900   20.2800    1.5300
   23.1100   45.6500   79.1900   93.5500   35.2900   19.8700   74.6800
   60.6800    1.8500   92.1800   91.6900   81.3200   60.3800   44.5100
   48.6000   82.1400   73.8200   41.0300    0.9900   27.2200   93.1800
   89.1300   44.4700   17.6300   89.3600   13.8900   19.8800   46.6000

---

EDIT

I downloaded the file and copy pasted this command from your answer:

c = importdata('nyse_data_matrix_no_tags.csv');

Then, I opened the file in a text editor and copied 1st and 20th row of numbers in the file and created a matrix in MATLAB:

x = [46.09,24.69,156.78,5.95,21.14,76.17,55.51,7.04,38.87,19.58,47.57,7.73,119.44,1.61,44.55,24.9,50.89,4.87,26.25,15.95,15.96,39.14,121.27,15.7,25.91,25.8,69.7,16.32,12.86,7.89,247.4,27.11,41.6,5.14,47.77,40.98,13.78,26.058,14.56,41.05,24.27,47.13,40.92,27,85.04,15.06,5.05,29.14,7.51,67.5,67.79,42.68,124.86,24.28,27.82,18.08,100.67,109.07,12.59,50.34,18.64,4.75,6.06,16.63,109.86,14.1,54.48,13.9,59.98,16.24,45.53,4.06,67.99,18.22,4.2,117.23,158.75,5.7,2.46,76.39,77.79,6.17,16.95,5.27,12.74,14.7,19.14,14.4,42.77,26.4,14.17,76.57,12.4,42.07,77.47,41.27,60.53,16.97,65.71,56.13,258.4,28.96,760.07,60.46,34.77,133.1,14.19,29.41,11.78,154.55,44.75,15.18,17.52,24.86,36.18,14.1,30.1,47.65,22.92,47.37,61.84,226.52,10.2,65.14,7.88,16.34,170.23,9.6,34.42,15.0999,20.34,58.7,16.6,14.72,14.32,20.89,14.32,13.2,6.05,422.96,21.43,47.55,13.28,27.98,8.16,45.15,15.1665,41.65,17.45,25.73,63.03,16.07,17.56,11.54,6.54,33.18,15.01,357.96,74.88,15.24,27.24,69.08,36.29,76.55,65.72,50.93,72.73,16.13,23.81,52.14,16.05,12.19,71.77,14.97,11.82,33.04,7.86,15.26,72.46,15.42,24.49,15.75,63.78,32.43,36.57,16.24,7.08,26.08,15.74,14.78,16.22,16.85,23.32,31.14,12.72,6.7,26.65,24,12.95,129.92,20.07,11.28,34.66,12.86,17.5,26.5,0.7038,1.22,128.98,23.33,19.2,15.64,8.34,45.01,38.14,50.31,13.11,17.4,47.15,79.29,8.24,28.92,24.97,1.92,77.16,126.5,8.97,3.97,12.85,30.75,37.82,2.36,10.37,52.28,1.56,53,47.33,41.32,10.65,15.24,39.5,94.42,12,54.25,48.03,3.72,6.34,18.5,23.47,26.74,8.74,6.7,7.08,70.92,27.3,65.43,18.776,7.82,16.8,12.0625,124.24,29.79,22.88,29.4,66.44,28.88,1.505,0.8642,10.37,47.14,99.83,133.87,46.41,4.88,57.63,14.45,11.2,34.99,11.06,129.25,7.71,35.2,2.29,2.53,1.65,13.74,15.6,11.84,59.79,33.85,62.48,72.34,25.15,131.2,61.13,2.17,5.1,53.35,26.9,43.15,10.8,63.61,130.91,81.42,45.29,17.58,415.79,118.64,73.88,9.85,79.22,43.39,4.86,32.18,68.36,60.21,34.15,19.47,23.39,29.96,39.58,14.66,5.98,70.87,25.9,38.64,90.45,165.2,46.57,83.18,16.48,134.15,54.68,62.51,12.25,24.33,17.69,60.73,11.51,15.6,85.99,80.81,59.9,17.48,30.73,104.34,0.94,86.03,82.73,14.41,34.05,17.2,11.98,13,52.5,39.2,19.58,101.59,26.38,44.13,18.72,23.3,32.05,27,26.12,13.13,18.58,6.07,73.74,3.53,42.4,12.21,15.72,16.71,26.27,26.69,4.52,35.99,26.1,17.31,45.61,67.9,11.15,13.08,1.1,9.7001,17.51,59.8,26.27,86.95,18.95,26.75,34.33,66.72,107.98,9.7,18.27,24.16,56.8,46.56,91.69,21.43,78.75,3.37,13.71,31.73,100.39,5.65,6.98,85.21,97.84,13.5,26.19,42.43,26.73,10.11,47.95,102.84,66.95,28.35,14.07,5.25,23.68,127.43,11.41,10.43,4.19,25.48,19.78,72.08,53.59,17.41,36.57,92.37,127.48,23.98,16.46,5.28,24.76,9.1,68.33,64.45,8.15,18.54,8.85,119.72,3.62,20.49,2.57,94.05,16.99,25.93,25.12,25.71,9.68,81.26,16.7,77.21,37.45,80.43,6.95,24.95,87.1,20.74,31.82,25.44,25.48,25.5515,46.96,19.11,43.49,10,8.68,16.99,120.79,3.95,1.91,76.54,7.8,33.71,14.59,13.5,15.99,42.6,38.86,46.76,8.1,23.14,23.04,17.39,15.4399,13.62,14,125.94,13.92,22.98,48.68,4.62,68.17,1.14,9.82,29.75,73.66,6.51,92.28,25.53,25.6,25.63,7.59,72.9,25.23,10.41,27.87,10.81,48.76,11.38,3.76,71.78,13.21,53.59,25.55,42.43,68.21,66.25,37.2,6.1,6.24,84.58,13.17,13.2,22.92,82.95,28.9,6.07,74.57,29.35,74.89,64.46,77.91,30.21,11.09,6.34,21.69,56.88,15.25,41.44,1.77,67.42,209.26,11.24,15.8,13.53,15.09,32.8,9.78,12.99,62.77,21.8,39.11,78.68,14.75,10.67,20.6,10.57,36.39,7.24,6.1,13.61,14.58,51.04,20.36,102.48,35.14,12.31,8.98,3.7,81.22,89.67,27.32,13.26,1.54,37.84,11.29,8.98,24.08,4.43,8.54,15.51,36.26,9.33,6.56,43.38,13.04,10.9,54.68,160,164.11,34.25,15.87,14.65,18.72,10.57,13.25,21.08,62.55,6.08,22.72,17.25,14.1,11.91,16.5,17.36,4.93,31.46,75.14,32.84,55.65,21.39,18.76,53.36,52.27,150,11.51,50.35,4.77,16.2,13.83,43.24,94.4,37.89,13.18,35.9,70.65,14.16,11.8273,12.67,22.93,11.11,13.55,9.55,14.24,26.44,13.49,69.92,9.88,157.12,14.93,6.53,13.5,6.83,28.27,13.07,48.33,59,9.67,2.04,28.2,5.82,31.87,65.9,33.14,36.14,30.08,8.49,64.92,4.76,141.98,8.63,10.11,17.27,19.65,30.24,26.53,40.67,26.87,26.25,39.18,1.54,33.93,16.59,7.33,15.36,13.91,1.1,17.12,4.47,15.67,18.68,1.79,15.54,81.13,21.34,27.59,7.46,10.77,503.01,19.74,18.46,15.26,47.57,30.24,6.55,65.34,4.13,13.2,21.55,21.03,29.36,27.27,47.72,10,26.17,7.45,7.62,3.48,18.15,8.2,20.66,97.23,59.46,13.37,7.8,76.6,19.31,9.21,24.18,77.53,0.9,10.8,153.17,23.32,1.18,25.83,42.06,1.48,11.49,24.78,18.24,21.24,6.59,44.54,33.28,64.61,227.17,30,48.14,29.72,45.5,79.45,26.83,4.96,81.03,30.76,72.15,34.48,129.3,65.48,33.48,67.43,34.31,15.05,60.38,32.59,26.84,1.7,32.48,2.31,113.96,1.13,7.3,31.13,12.27,44.48,163.75,4.55,4.93,49.64,7,0.42,4.66,62.44,41.56,21.6,8.18,26.89,33.78,3.79,47.23,27.86,0.5199,45.15,117.21,9.51,74.52,1.8,67.24,22.28,22.735,28.48,13.84,19.61,26.62,25.78,18.95,33.53,21.54,51.22,13.79,34.9,81.22,6.86,15.59,96.42,18.49,31.21,24.57,21.23,22.93,16.32,10.63,11.18,188.35,16.08,18.39,18.17,43.96,62.54,8.82,1.87,33.38,14.9,10.47,16.69,3.2,8.89,3.95,50.02,153.42,7.65,35.53,0.54,18.36,5.24,262.52,4.07,74.98,12.53,13.66,86.82,129.8,24.78,9.8,7.03,21.68,8.1,17.1,7.5,38.4,118.45,6.47,26.92,17.2,7.6,35.32,1.72,25.83,9.11,26.27,13,12.18,12.29,44.75,26.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I compared matrix x with the 1st and 20th row of c and they are equal:

isequal(x,c([1 20],:))
ans =
     1

importdata seems to be working as expected.