Information Pooling Game
in Multi-portfolio Optimization
Ⅰn this paper, an information pooling game is proposed and studied for multi-portfolio optimization problem. The financial adviser would proceed as follows:
- Determine the trades by solving the collusive optimization problem.
- Invite each client to determine her information pooling strategy, which forms an information pool.
- Authorize each client to access her corresponding information pool. Both the manager and client may estimate her expected net utility by solving the information pooling problem with SLCP (Sequential Linearly Constrained Programming).
- Determine the split ratio of the resulting market impact cost by minimizing the variance of dissatisfaction indicators across all accounts.
This approach produces Pareto optimal utilities while also keeping the satisfaction of all accounts at a similar level, complying with the SEC (Securities and Exchange Commission) best execution rules. It outperforms the pro-rata collusive solution in horizontal fairness, and overcomes the pitfall in Cournot-Nash equilibrium with a more tractable approach by introducing the dissatisfaction indicator.
Fig. 1: Increase of dissatisfaction indicators with partial and complete information pools from that with null information pool
|応用分野||In order to efficiently serve a large number of clients, SEC allows the manager to “bunch orders on behalf of two or more client accounts”. This information pooling approach provides a potential solution to the problematic interaction arising in multi-portfolio optimization because of the inter-dependent market impact cost.|