@RISK, RISKOptimizer Help Clients Hedge Their Bets for Maximum Profit

Maximum profit, minimal risk; this is the goal for any investment, and particularly true for hedge funds—investment vehicles named for the fact that investors ‘hedge,’ or attempt to protect their funds against volatile swings in the markets. Seth Berlin, Principal Strategist at the financial consulting firm Performance Thinking & Technologies, was on the quest to find the sweet spot between maximizing profit and minimizing risk for his clients. With the help of @RISK and RISKOptimizer, Berlin was able to create a quantitative model to do just that.

Berlin’s client dealt with a hedge fund portfolio comprising both long and short positions. Long positions are assets that are owned by the fund, such as stocks, which investors hope will increase in value. Short positions are ‘borrowed assets’ sold short, which increase in value if the price of the underlying assets goes down. Hedge funds use combinations of long and short positions to protect against equity market movements, currency rate changes, interest rate risk, and credit worthiness. “I’ve worked with many hedge fund managers on how they manage hedges. Anytime you can move to more quantitative than qualitative management based on simulation and optimization, it’s a win for both managers and investors,” says Berlin.

Berlin used Palisade’s risk analysis software, including @RISK and RISKOptimizer, to prove to his clients that quantitative measures of simulation could help identify an optimal portfolio, rather than the typical qualitative approach. He used a step-wise process to develop his quantitative model:

  • Determine hedge ratios and constraints
  • Determine decision variables to be modeled
  • Determine uncertain inputs
  • Model uncertain inputs using @RISK
  • Output dependent on decision variables using RISKOptimizer

After going through this five-step process, Berlin had tips for how the hedge fund managers could change their profitability by altering the mix of long and short positions in the portfolio. “RISKOptimizer gave me an optimal mix of my short positions for the next week. Since this is theoretical, you then compare your results with what really happened in the next week,” he says.

These results are helpful, but of course, they are not the only answer. “I’m not an alchemist. I’m not coming up with a secret sauce that is the model for managers to make money,” says Berlin. “What I was trying to do overall is to move from a qualitative to a quantitative way to judge a problem.” He explains that managing a hedge fund will always require qualitative decisions, but that well done simulations provide a method to test one’s intuition and judgment.

Read the full case study here.

See also: Portfolio Optimization II: An Overview of Markowitz-related Approaches

 

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