Machine Learning-based Optimization Study of Diversified Portfolio for Film and Television Projects
Zang, Mingxuan (2024)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024121034288
https://urn.fi/URN:NBN:fi:amk-2024121034288
Tiivistelmä
While machine learning technologies have been embedded in the investment decision-making process and thus revolutionized many aspects of the financial world, their application in optimizing film industry investment portfolios remains a largely uncharted frontier. The paper responsibly investigates the potency of machine learning algorithms in enhancing movie investment portfolios, having two clear objectives in mind: risk diversification and return maximization. The present research is based on an impressive dataset comprising 5,000 films released between 2010 and 2023, with comprehensive features of production budgets, genres, cast metrics, and historical performance indicators.
Adopting a mixed-methods approach, this study harmonizes quantitative analysis through advanced machine learning models with qualitative evaluations of industry-specific dynamics. With confidence, I can report that XGBoost, Random Forest, and Neural Networks – a powerful suite of machine learning algorithms – were effectively utilized to accurately predict financial returns for film projects and optimize portfolio allocation. The dataset was segmented, with 70% utilized for training the models and 30% set aside for validation, to ensure thorough performance assessments via standard metrics including Mean Squared Error (MSE) and R-squared values.
The findings of this study confidently demonstrate that machine learning-driven portfolio optimization can deliver a significant 15%% increase in risk-adjusted returns, surpassing the capabilities of traditional investment methodologies. Notably, the XGBoost model exhibited exceptional performance, elucidating 78% of the variance in film project returns. The research further delineated critical predictive features, identifying production budget, director track record, and genre diversity as the most pivotal factors influencing investment success. Additionally, the portfolios optimized through machine learning demonstrated enhanced resilience during periods of market volatility, achieving a notable 25% reduction in portfolio variance compared to non-optimized strategies.
This investigation underscores the transformative potential of machine learning in the film investment sector, advocating for a paradigm shift in how investment decisions are formulated. By leveraging sophisticated algorithms and comprehensive datasets, investors can significantly enhance their decision-making processes, ultimately leading to more sustainable and profitable outcomes in an inherently unpredictable market. These findings contribute substantially to the academic discourse surrounding machine learning applications in niche investment sectors, particularly in the realm of film. Through a comprehensive analysis of machine learning's capabilities and limitations in this context, the research not only deepens theoretical knowledge but also offers a robust blueprint for data-driven decision-making in the film industry.
This research represents a significant and indispensable advancement in the academic literature and the practical application of machine learning in film investment decisions. By addressing the dual aspects of predictive power and market adaptability, it lays the groundwork for future innovations in the intersection of technology and the film industry.
Adopting a mixed-methods approach, this study harmonizes quantitative analysis through advanced machine learning models with qualitative evaluations of industry-specific dynamics. With confidence, I can report that XGBoost, Random Forest, and Neural Networks – a powerful suite of machine learning algorithms – were effectively utilized to accurately predict financial returns for film projects and optimize portfolio allocation. The dataset was segmented, with 70% utilized for training the models and 30% set aside for validation, to ensure thorough performance assessments via standard metrics including Mean Squared Error (MSE) and R-squared values.
The findings of this study confidently demonstrate that machine learning-driven portfolio optimization can deliver a significant 15%% increase in risk-adjusted returns, surpassing the capabilities of traditional investment methodologies. Notably, the XGBoost model exhibited exceptional performance, elucidating 78% of the variance in film project returns. The research further delineated critical predictive features, identifying production budget, director track record, and genre diversity as the most pivotal factors influencing investment success. Additionally, the portfolios optimized through machine learning demonstrated enhanced resilience during periods of market volatility, achieving a notable 25% reduction in portfolio variance compared to non-optimized strategies.
This investigation underscores the transformative potential of machine learning in the film investment sector, advocating for a paradigm shift in how investment decisions are formulated. By leveraging sophisticated algorithms and comprehensive datasets, investors can significantly enhance their decision-making processes, ultimately leading to more sustainable and profitable outcomes in an inherently unpredictable market. These findings contribute substantially to the academic discourse surrounding machine learning applications in niche investment sectors, particularly in the realm of film. Through a comprehensive analysis of machine learning's capabilities and limitations in this context, the research not only deepens theoretical knowledge but also offers a robust blueprint for data-driven decision-making in the film industry.
This research represents a significant and indispensable advancement in the academic literature and the practical application of machine learning in film investment decisions. By addressing the dual aspects of predictive power and market adaptability, it lays the groundwork for future innovations in the intersection of technology and the film industry.