Article
Version 1
This version is not peer-reviewed
Systematic Review of Machine Learning Return-on-Investment Forecasting
Version 1
: Received: 14 July 2024 / Approved: 15 July 2024 / Online: 16 July 2024 (05:34:01 CEST)
How to cite: Chen, N. Systematic Review of Machine Learning Return-on-Investment Forecasting. Preprints 2024, 2024071191. https://doi.org/10.20944/preprints202407.1191.v1 Chen, N. Systematic Review of Machine Learning Return-on-Investment Forecasting. Preprints 2024, 2024071191. https://doi.org/10.20944/preprints202407.1191.v1
Abstract
In this paper, we investigate the proftability of investing in movies for retail investors, focusing on the return on investment (ROI) metric, which is calculated as revenue divided by budget. Our analysis encompasses 5000 movies from the movie database TMDB, examining factors such as production scale, genres, key actors, directors, and more. Additionally, we employ textual analysis techniques like Latent Semantic Analysis (LSA) on movie overviews and tags to incorporate movie themes. We then evaluate various supervised classifcation models including Logistic Regression, Random Forest, and Light Gradient Boosting (LightGBM), comparing their performance. Our fndings highlight the signifcance of production scale, team structure, and movie themes in identifying potential high-return opportunities for investors.
Keywords
Machine Learning
Latent Semantic Analysis
Gradient Boosting
Random Forest
Logistic Regression
Latent Semantic Analysis
Gradient Boosting
Random Forest
Logistic Regression
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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