financeФинансы: теория и практика/Finance: Theory and PracticeFinance: Theory and Practice2587-56712587-7089Financial University under The Government of Russian Federation10.26794/2587-5671-2025-29-6-243-268finance-4074Research ArticleОЦЕНКА ФИНАНСОВОЙ ЭФФЕКТИВНОСТИASSESSMENT OF FINANCIA PERFORMANCEПрогнозирование финансовой эффективности российского кинематографа с помощью многофакторной ансамблевой модели машинного обучения, тренированной на данных прошлых периодовForecasting the Financial Efficiency of Russian Cinema Using a Multifactor Ensemble Machine Learning Model Trained on Historical Datahttps://orcid.org/0000-0002-1069-1648ДождиковА. В.DozhdikovA. V.

Антон Валентинович Дождиков – кандидат политических наук, старший научный сотрудник кафедры ЮНЕСКО

Москва

Anton V. Dozhdikov – Cand. Sci. (Polit.), Senior Researcher at the UNESCO Chair

Moscow

antondnn@yandex.ruИнститут социально-политических исследований ФСНИЦ РАНРоссияInstitute of Socio-Political Studies of the Russian Academy of SciencesRussian Federation202529122025296243268Copyright © Дождиков А.В., 20252025Дождиков А.В.Dozhdikov A.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://financetp.fa.ru/jour/article/view/4074

Объект исследования — данные проката российского кинематографа c июля 2022 по сентябрь 2023 г., 185 кинокартин.

Инструмент исследования — предобученные на базе прошлых периодов (с 2004 по июль 2022 г., 1500 кинокартин) 26-и 146-факторные модели машинного обучения.

Цель исследования — доказать, что модели машинного обучения, обученные на данных прошлых периодов, могут прогнозировать будущие данные. Это особенно важно для финансирования программ развития национального кинематографа в России и привлечения частных инвестиций в условиях ухода иностранных кинодистрибьюторов с рынка. В исследовании использовались методы оценки кинопроектов на основе исторической доходности по прокатным показателям и характеристикам творческих (съемочных) групп. Акцент сделан на ансамблевых моделях — AdaBoost, Bagging, ExtraTrees, GradientBoosting, RandomForest, Stacking, Voting, XGBoost, CatBoost.

Новизна исследования заключается во введении в научный оборот новых источников и возможности практического применения разработанных подходов для государственного и частного инвесторов при оценке проектов фильмов до начала производственного цикла.

Выводы: поскольку падение метрик качества (accuracy, roc_auc и других) на выборке из 185 новых кинофильмов (по сентябрь 2023 г.) оказалось незначительным, это открывает возможность использования предобученных моделей на данных прошлых периодов для прогнозирования сборов и других результатов кинопроката. Проанализировав прошлые проекты режиссера, сценаристов, оператора, продюсеров, художника, монтажера, композитора и ключевых актеров, а также предполагаемые прокатные данные и объем финансирования, можно с высокой точностью предсказать успех фильма. Это позволит оценить общие сборы, окупаемость, количество просмотров и зрительский рейтинг.

The object of the study is data on the distribution of Russian cinema films from July 2022 to September 2023. Specifically, it analyzes 185 films that were released during this period. The research tool consists of 26 and 146-factor machine learning models that have been pre-trained based on previous periods (from 2004 to July 2022, with 1,500 films).

The purpose of the study is to demonstrate that machine learning models, trained on historical data, can accurately predict future data, which is especially important for funding programs aimed at developing national cinema in the Russian Federation and attracting private investment, in light of the departure of foreign film distributors from the film market. The study used methods to evaluate film projects based on their historical profitability using rental indicators and the characteristics of the creative teams involved in producing them. The emphasis is on ensemble models –AdaBoost, Bagging, ExtraTrees, GradientBoosting, RandomForest, Stacking, Voting, XGBoost, CatBoost.

The novelty of this research lies in introducing of new sources into the scientific community and the potential for practical application of the developed methods for both public and private investors to evaluate film projects prior to the start of the production cycle.

Conclusions: Conclusions: Based on the analysis of the quality metrics (accuracy, ROC AUC, and others) for a sample of 185 newly released films (through September 2023), we found that the drop in these metrics was not significant. This suggests that it is possible to use pre-trained models based on historical data to make predictions about fees and other rental outcomes. By analyzing the past work of the project director, screenwriters, cameramen, producers, artists, editor, composer and key actors of the project, estimated distribution data, and the amount of project funding, it is possible to make an accurate prediction about the success of a film. This will allow you to see the total fees, payback period, number of views, and viewer rating.

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The authors declare that there are no conflicts of interest present.