Mean-variance data collections for multiperiod portfolio optimization problems

Mean-variance data collections for portfolio optimization problems based on time series of daily stock prices for New York stock exchange. These data collections can be used for investment portfolio optimization research.NYSE includes over 2000 stocks. These data includes randomly selected sets of size equal to 200, 300, 400, 500, 600, 700, 800 and 900. Each set include the succesive yearly data from 2014, 2015, 2016, 2017, 2018, and 2019 grouped in a single folder. While each folder includes the data saved in a text file following the format used by J. E. Beasley in OR Library (http://people.brunel.ac.uk/~mastjjb/jeb/orlib/portinfo.html).Example structure of files in the 200 set is presented below:- 2014  + JKMP2_200_2014_1  + JKMP2_200_2014_2  + JKMP2_200_2014_3  ...  + JKMP2_200_2014_12- 2015  + JKMP2_200_2015_1  + JKMP2_200_2015_2  + JKMP2_200_2015_3  ...  + JKMP2_200_2015_12- 2016  + JKMP2_200_2016_1  + JKMP2_200_2016_2  + JKMP2_200_2016_3  ...  + JKMP2_200_2016_12- 2017  + JKMP2_200_2017_1  + JKMP2_200_2017_2  + JKMP2_200_2017_3  ...  + JKMP2_200_2017_12- 2018  + JKMP2_200_2018_1  + JKMP2_200_2018_2  + JKMP2_200_2018_3  ...  + JKMP2_200_2018_12- 2019  + JKMP2_200_2019_1  + JKMP2_200_2019_2  + JKMP2_200_2019_3  ...  + JKMP2_200_2019_12A detailed description of the data collections can be found in the README file of the dataset. For each set of stocks, we estimated the correlation matrix and the vector of mean returns, based on the corresponding time series.