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

Bibliografiset tiedot
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.
Yhteisötekijä: National Research Tomsk Polytechnic University (570)
Muut tekijät: 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
Yhteenveto: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
Kieli:englanti
Julkaistu: 2024
Aiheet:
Forecasting firm growth
Random forest
LightGBM
CatBoost
Logistic regression
электронный ресурс
труды учёных ТПУ
Linkit:https://doi.org/10.1016/j.joitmc.2024.100406
Aineistotyyppi: Elektroninen Kirjan osa
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=677816

Samankaltaisia teoksia

Samankaltaisia teoksia