"Style Analysis with Particle Filtering and Generalized Simulated Annealing"

Author Name

Fukui, Takaya, Seisho Sato, and Akihiko Takahashi


April 2016

Full Paper   PDF file
Remarks   Revised in June 2016 and March 2017

This paper proposes a new approach to style analysis of mutual funds in a general state space framework with particle filtering and generalized simulated annealing (GSA). Specifucally, we regard the exposure of each style index as a latent state variable in a state space model and employ a Monte Carlo filter as a particle filtering method, where GSA is effectively applied to estimating unknown parameters. An empirical analysis using data of three Japanese equity mu- tual funds with six standard style indexes confirms the validity of our method. Moreover, we create fund-specific style indexes to further improves estimation in the analysis.