Forecasting firm growth resumption post-stagnation; Journal of Open Innovation: Technology, Market, and Complexity; Vol. 10, iss. 4

Forecasting firm growth resumption post-stagnation; Journal of Open Innovation: Technology, Market, and Complexity; Vol. 10, iss. 4

Détails bibliographiques
Parent link:Journal of Open Innovation: Technology, Market, and Complexity.— .— Amsterdam: Elsevier Science Publishing Company Inc.
Vol. 10, iss. 4.— 2024.— Article number 100406, 17 p.
Collectivité auteur: Томский политехнический университет (570)
Autres auteurs: Vukovic D. B. Darko, Spitsin V. V. Vladislav Vladimirovich, Bragin A. D. Aleksandr Dmitrievich, Leonova V. A. Victoria Aleksandrovna, Spitsina (Spitsyna) L. Yu. Lubov Yurievna
Résumé:Title screen
Our study forecasts the likelihood of firms resuming growth after periods of stagnation or declining sales. We employ machine learning methods, including Random Forest, LightGBM, and CatBoost, alongside logistic regression models. To address class imbalance, we incorporate oversampling techniques such as SMOTE, ADASYN, and SMOTEENN. We focus on two key indicators—Precision (predictive accuracy) and Recall (completeness of prediction)—to meet the needs of different investor groups. The performance of our models is evaluated using metrics such as accuracy, precision, recall, F-score, and RocAUC, with Venkatraman's test applied for model comparison. Our key findings reveal that CatBoost achieves a predictive accuracy of 65–67 %, significantly outperforming random firm selection, which yields only 13–17 % accuracy. The combination of the CatBoost method with the SMOTEENN technique notably enhances Recall values, reaching 58–63 %, a critical metric for large investors and policymakers. Our study offers a methodological approach to better understand and forecast the trajectories of firms engaged in open innovation
Текстовый файл
AM_Agreement
Langue:anglais
Publié: 2024
Sujets:
Forecasting firm growth
Random forest
LightGBM
CatBoost
Logistic regression
электронный ресурс
труды учёных ТПУ
Accès en ligne:https://doi.org/10.1016/j.joitmc.2024.100406
Format: Électronique Chapitre de livre
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=677816

Documents similaires

Documents similaires