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(Answered) Integrating liquidity into the mean variance optimization


The paper in attachment describes 3 approaches to integrate liquidity into mean variance framework (cf attachment) I shouold apply those approaches on the selected stocks provided in the excel file. The excel file contains bid ask prices, volumes, number of shares and market value. Given this, I need: 1. Code to read the data from the sheet and clean and fill the data, pick the required 100 stocks, computing monthly mid prices (as an average of bid and ask, simply), using those to compute monthly log returns and variance covariance matrix, to be used as inputs in the optimization. 2. A Matlab code that implements selected Liquidity Metrics (Turnover, Bid Ask spread, Percentage Volumes) as specified in the Andrew Lo Paper in a monthly period. (in the excel file there are only daily data) 3.a Matlab code to implement the 1st approach described by Lo (the liquidity filtering): it basically select the stocks according to liquidity level..and run the optimization on that selected universe. 4. Quadratic optimization code (Matlab) that uses a liquidity metric as a constraint (so simply an additional constraint to the standard short selling) 5. Quadratic optimization code (Matlab) that uses a liquidity metric as part of the objective function (cf the paper pag. 9-11) Attachments: Updated-data-....xlsx Andrew-J-Lo--....pdf
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Integrating liquidity into the mean variance optimization

 


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