Leveraging Machine Learning and Multi-Attribute Decision-Making for Optimal Portfolio Management in the Motion Pictures Industry

核心概念

This research proposes a comprehensive data-driven optimization methodology to help Motion Pictures Industry (MPI) distributors design an optimal portfolio by predicting box office performance and considering various criteria to maximize profitability and distributor preferences.

摘要

The research aims to provide a practical framework for effective portfolio management in the MPI supply chain. It addresses key challenges faced by MPI distributors, including predicting project profitability and incorporating distributor preferences.
The key steps of the proposed methodology are:

Box Office Prediction:

Gather a comprehensive dataset on movies released in the US from 1980-2020.
Preprocess the data, including handling missing values, normalizing features, and extracting celebrity fame scores using a Large Language Model (Chat GPT).
Classify the movies into 3 classes based on box office range using various classification algorithms, with the Voting Ensemble model performing the best.
Fit regression models for each class to predict the box office performance.

Determining Distributor Preferences:

Identify a set of 5 criteria (movie rating, genre, director/writer/lead actor fame) to assess project preferability for the distributor.
Use the Bayesian Best-Worst Method (BBWM) to determine the weights of the criteria.
Apply the Weighted Aggregated Sum Product Assessment (WASPAS) method to calculate the preferability rate of each project.

Portfolio Optimization:

Develop a bi-objective optimization model to maximize the distributor's total profit and the preferability of the portfolio.
Solve the model using the Weighted Sum Method to obtain the optimal portfolio.

The proposed methodology is validated using real-world data, demonstrating its effectiveness in predicting box office performance and designing optimal portfolios that consider both profitability and distributor preferences.

統計資料

The dataset contains 6097 movies released in the US from 1980 to 2020, with features including movie rating, genre, franchise/sequel status, release month, IMDb score and vote counts, director/writer/lead actor fame scores, domesticity, runtime, budget, and box office.

引述

"To navigate this dynamic landscape, investment companies operating in this field must adopt effective portfolio management strategies that can help them maximize their returns while mitigating risks."
"One of the factors that can influence the profitability of an MPI project is the celebrities who are taking part in it (e.g., actors, writers, and directors). Celebrities have a considerable influence on consumers' purchase decisions."

從以下內容提煉的關鍵洞見

Data-Driven Portfolio Management for Motion Pictures Industry

by Mohammad Ali... 於 arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07434.pdf

深入探究

How can the proposed methodology be extended to incorporate dynamic changes in the MPI landscape, such as shifts in consumer preferences or the impact of emerging technologies?

The proposed methodology can be extended to incorporate dynamic changes in the MPI landscape by integrating real-time data sources and implementing machine learning algorithms that can adapt to changing trends. One approach could be to continuously gather data on consumer preferences, market trends, and emerging technologies to update the predictive models used in the portfolio optimization process. By leveraging techniques like natural language processing and sentiment analysis, the system can analyze social media, reviews, and other online content to gauge consumer sentiment and preferences. Additionally, incorporating predictive analytics models that can forecast the impact of emerging technologies on the industry can provide valuable insights for decision-making. By creating a feedback loop that continuously updates the models based on new data, the methodology can stay relevant and adaptive to the evolving landscape of the MPI.

What are the potential limitations or drawbacks of using a Large Language Model like Chat GPT to assess celebrity fame scores, and how could these be addressed?

One potential limitation of using a Large Language Model (LLM) like Chat GPT to assess celebrity fame scores is the lack of domain-specific knowledge and context. LLMs are trained on a vast amount of text data from various sources, which may not always capture the nuances and intricacies of the entertainment industry. This could lead to inaccuracies in assessing the fame scores of celebrities, especially if the model lacks specific information about the industry or recent developments.
To address this limitation, one approach could be to fine-tune the LLM on a dataset specifically curated for the entertainment industry. By training the model on relevant data such as movie reviews, box office performance, and industry news, the LLM can better understand the context and factors that contribute to a celebrity's fame score. Additionally, incorporating human experts from the entertainment industry to validate and calibrate the model's predictions can help improve the accuracy and reliability of the fame scores generated by the LLM.

What other factors beyond the ones considered in this study (e.g., marketing strategies, release timing) could be integrated into the portfolio optimization model to further enhance its real-world applicability?

In addition to the factors considered in the study, several other variables could be integrated into the portfolio optimization model to enhance its real-world applicability. Some of these factors include:

Marketing Strategies: Incorporating data on marketing campaigns, social media engagement, and advertising effectiveness can provide insights into the potential success of a movie project. Analyzing the impact of different marketing strategies on box office performance can help distributors make informed decisions.

Release Timing: Considering the timing of a movie release in relation to other blockbuster releases, holidays, and seasonal trends can significantly impact box office performance. By analyzing historical data on release timing and box office success, the model can optimize the portfolio based on the most favorable release dates.

Critical Reception: Including data on critical reviews, audience ratings, and awards can offer valuable insights into the reception of a movie project. By factoring in the critical reception of a movie, distributors can better assess its potential success and adjust their portfolio accordingly.

By incorporating these additional factors into the portfolio optimization model, distributors can make more informed decisions and create a well-rounded portfolio that maximizes profitability and mitigates risks in the dynamic MPI landscape.