利用机器学习确定俄罗斯电影票房投资成功及其因素

Q3 Economics, Econometrics and Finance
A. Dozhdikov
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引用次数: 0

摘要

俄罗斯电影票房的历史数据是研究对象。研究的目的是确定在电影制作的早期阶段预测电影项目现金费用的可能性,由于外国发行商退出俄罗斯市场,这一点尤为重要。研究分析了 2004 年初至 2022 年 4 月上映的俄罗斯国产电影的全部数据(N = 1400)。这些数据是首次引入科学流通领域。研究采用了基于历史盈利能力的电影项目评估方法,以及按流派、导演、编剧对电影进行分类的方法。7 个机器学习和神经网络模型的实验结果表明,准确率达到 0.96,ROC(AUC)= 0.98。文章对改进预测方法的方向做出了结论,并对所提出方法的局限性做出了结论。考虑到电影项目单个财务结果的高波动性,建议采用 "组合 "投资原则,这为电影制片人和发行人利用市场金融工具、发行债券和股票进行债务和股权融资开辟了前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determination of Investment Success and its Factors for Russian Cinema at the Box Office Using Machine Learning
Historical data of the box office of Russian cinema is the object of research. The purpose  of  the  study  is  to determine the possibility of forecasting the cash fees of  the  film  project  at  an  early stage  in  the  production  of films, which is especially important due to withdrawal of foreign distributors from the Russian market. The analysis was carried out on data for the entire population (N = 1400) of Russian national films that were released from the beginning of 2004 to April 2022. These data are introduced into scientific circulation for the first time. The study used methods of evaluation of film projects based on historical profitability and classification of films by genres, directors, screenwriters. The result of the experiment on 7 machine learning and neural network models achieved an accuracy of 0.96 and ROC (AUC) = 0.98. The article provides conclusions about the directions for improving forecasting methods and conclusions about the limitations of the proposed approach. Taking into account the high volatility of the individual financial result of a film project, recommendations were made by the “portfolio” principle of investment, which opens the prospects of debt and equity financing of cinema using market financial instruments, issuance of bonds and shares by producers and distributors.
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来源期刊
Finance: Theory and Practice
Finance: Theory and Practice Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
CiteScore
1.30
自引率
0.00%
发文量
84
审稿时长
8 weeks
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