研究紹介TOPへ戻る

Information Pooling Game 
in Multi-portfolio Optimization

分野
ゲーム理論
キーワード
information pooling, multi-portfolio optimization, perceived fairness 
情報工学部 システムマネジメント学科

助 教 傅 靖

研究概要

 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 

利点・特徴
  • The clients are allowed to decide whether and to what extent their  private trading information is shared with others in the same information pool.
  • Both horizontal and vertical fairness are incorporated in this novel   mechanism,   
      which guarantees that no client is systematically advantaged or disadvantaged.
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.