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). Specically, we regard the ex-
posure 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 conrms the validity of our
method. Moreover, we create fund-specific style indexes to further
improves estimation in the analysis.