How to Create a Custom Application for Stock Portfolio Optimization, Right in Your Spreadsheet

This is an example of the use of @RISK automation applied to stock portfolio optimization. It is a custom application written by Palisade Custom Development using @RISK’s XDK in Excel. 

The steps outlined in the Stock Portfolio Optimization example movie:
  1. Obtain Price & Weight Data
    The user will first define a portolfio of stocks. 
     
  2. Run Analysis & Review
    The next step is to run the analysis for the returns of each security. 
     
  3. Fit Data & Simulate
    The third step is to obtain the distributions that fit the historical return of the portoflio. Distributions of 10 will be used as a base for prediting gains or losses using a simulation. 
     
  4. Optimize & Generate Efficient Frontier
    The last step is to run the optimization process and maximize the portfolio's mean return, given certain constraints. Another feature available is to get the Efficient Frontier of the portfolio.
Automation takes the shape of an Excel add-in, which is shown to the user as a new Excel ribbon. 
 

Custom Development in Excel

Palisade Custom Development has written applications for cost estimation, retirement planning, oil and gas prospecting, portfolio risk management, and more – all utilizing @RISK technology in Excel. In each case, the interface is customized to include only what the users need, hiding unused @RISK functionality and preventing user access to the underlying model logic. You can also automate processes like reporting, generating only the charts and data you want. The result is a tailored application ready to roll out to your workgroup. Because the application is in Excel, the training required for users is minimal. XDKs come with the DecisionTools software PrecisionTree, StatTools, NeuralTools, RISKOptimizer, and Evolver as well as @RISK. This means we can create risk analysis solutions for you using a range of powerful analytics, including Monte Carlo simulation, decision trees, statistics, neural networks, and optimization. 

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