Allocate3M

Machine learning and data analytics are increasingly being used for resource allocation, increasing growth rates or reducing volatility by allocating limited financial, human, technological, time, or natural resources to the tasks and projects that need them most. The “Mean-Variance” Model is the most classic resource allocation model and is widely used in various industries. Common application scenarios: 1. Seeking the allocation weights with the highest growth rate under a specific level of volatility. For example, you can allocate resources based on historical sales data from 500 European retail stores. The “Mean-Variance” model uses the Monte Carlo method to find a set of allocation weights that have the optimal sales growth rate. 2. Weights are assigned based on volatility in order to seek stability. For example, you can allocate your procurement budget based on the historical raw material prices of 300 suppliers in the Asia-Pacific region. The “Risk-Parity” model uses Newton's method to find a set of allocation weights with equal volatility to reduce the risk of raw material price fluctuations. Allocate3M combines three models: “Mean-Variance”, “Black-Litterman”, and “Risk-Parity”, to help you optimize resource allocation results from different perspectives.