Data-Driven Portfolio Management for Motion Pictures Industry: A New
  Data-Driven Optimization Methodology Using a Large Language Model as the
  Expert
Mohammad Alipour-Vaezi Kwok-Leung Tsui
Portfolio management is one of the unresponded problems of the Motion
Pictures Industry (MPI). To design an optimal portfolio for an MPI distributor,
it is essential to predict the box office of each project. Moreover, for an
accurate box office prediction, it is critical to consider the effect of the
celebrities involved in each MPI project, which was impossible with any
precedent expert-based method. Additionally, the asymmetric characteristic of
MPI data decreases the performance of any predictive algorithm. In this paper,
firstly, the fame score of the celebrities is determined using a large language
model. Then, to tackle the asymmetric character of MPI's data, projects are
classified. Furthermore, the box office prediction takes place for each class
of projects. Finally, using a hybrid multi-attribute decision-making technique,
the preferability of each project for the distributor is calculated, and
benefiting from a bi-objective optimization model, the optimal portfolio is
designed.