Really interesting approach. How does your dynamic allocation engine weigh conflicting goals when users have multiple time horizons? Would love to understand how you solve that optimization problem.
That's a great question, as complex goals are often conflicting.
Right now, each goal uses a qualitative scale to establish an initial risk budget.
In the future, we will ask users to rank all goals (like a weighted priority list). This ranking allows our dynamic allocation engine to solve the optimization problem:
Prioritization: The ranking determines the importance of each goal in the final outcome calculation.
Continuous Recalibration: The engine doesn't use a fixed risk cap. Instead, it continuously adjusts the risk allocated to each goal based on its performance and time horizon.
Risk to Maximize Probability: Goals that are far from being achieved may temporarily take more risk to increase the chance of success, while goals that are ahead of schedule will de-risk immediately to protect gains.
This ensures the total portfolio risk stays optimal while maximizing the probability of achieving your goals in priority order.
Right now, each goal uses a qualitative scale to establish an initial risk budget.
In the future, we will ask users to rank all goals (like a weighted priority list). This ranking allows our dynamic allocation engine to solve the optimization problem:
Prioritization: The ranking determines the importance of each goal in the final outcome calculation.
Continuous Recalibration: The engine doesn't use a fixed risk cap. Instead, it continuously adjusts the risk allocated to each goal based on its performance and time horizon.
Risk to Maximize Probability: Goals that are far from being achieved may temporarily take more risk to increase the chance of success, while goals that are ahead of schedule will de-risk immediately to protect gains.
This ensures the total portfolio risk stays optimal while maximizing the probability of achieving your goals in priority order.