Analyzing Optimal Portfolios Under Uncertainty

This document describes current steps and results in the updated workflow.

1. Creating States of the World
2. Identifying Optimal Portfolios
3. Identifying Factors Driving Uncertainty

Step 1: Generating States of the World and organizations

We first create a series of states of the worlds (SOW) and organization characteristics (OC). Together, these variables are used to simulate the expected value of each dollar invested in an organization in a given state of the world.

Step 2: Identifying optimal portfolios

For each combination of SOW and OC, we derive an Optimal Portfolio (OP) using linear programming. This process is repeated across numerous simulations to capture variability due to uncertainty.

The solver is also used to identify the optimal portfolio for the combined state of the worlds.

graph LR

%% Outer Subgraph Surrounding All Simulations
subgraph Simulations
    direction LR
    %% Simulation n
    subgraph Simulation n
        direction LR
        SOW1(SOW)
        SOO1(SOO)
        OP1(Optimal Portfolio)
    end
    SOW1 --> SOO1 --> OP1

    %% Ellipsis to represent continuation
    subgraph  
        ellipsis[•••]
        style ellipsis fill-opacity:0,stroke:none,font-size:30px
    end

    %% Simulation 3
    subgraph Simulation 3
        direction LR
        SOW2(SOW)
        SOO2(SOO)
        OP2(Optimal Portfolio)
    end
    SOW2 --> SOO2 --> OP2

    %% Simulation 2
    subgraph Simulation 2
        direction LR
        SOW3(SOW)
        SOO3(SOO)
        OP3(Optimal Portfolio)
    end
    SOW3 --> SOO3 --> OP3



    %% Last Simulation (Simulation n)
    subgraph Simulation 1
        direction LR
        SOWn(SOW)
        SOOn(SOO)
        OPn(Optimal Portfolio)
    end
    SOWn --> SOOn --> OPn
end

%% Combined Optimal Portfolio for all Simulations
COP(Combined Optimal Portfolio)
Simulations --> COP