Forecasting firm growth resumption post-stagnation; Journal of Open Innovation: Technology, Market, and Complexity; Vol. 10, iss. 4
| 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. |
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| מחבר תאגידי: | |
| מחברים אחרים: | , , , , |
| סיכום: | 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 |
| שפה: | אנגלית |
| יצא לאור: |
2024
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| נושאים: | |
| גישה מקוונת: | https://doi.org/10.1016/j.joitmc.2024.100406 |
| פורמט: | אלקטרוני Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=677816 |
MARC
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| 200 | 1 | |a Forecasting firm growth resumption post-stagnation |f Darko B. Vukovic, Vladislav Spitsin, Aleksander Bragin [et al.] | |
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| 330 | |a 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 | ||
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| 461 | 1 | |t Journal of Open Innovation: Technology, Market, and Complexity |c Amsterdam |n Elsevier Science Publishing Company Inc. | |
| 463 | 1 | |t Vol. 10, iss. 4 |v Article number 100406, 17 p. |d 2024 | |
| 610 | 1 | |a Forecasting firm growth | |
| 610 | 1 | |a Random forest | |
| 610 | 1 | |a LightGBM | |
| 610 | 1 | |a CatBoost | |
| 610 | 1 | |a Logistic regression | |
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| 701 | 1 | |a Vukovic |b D. B. |g Darko | |
| 701 | 1 | |a Spitsin |b V. V. |c economist |c Associate Professor of Tomsk Polytechnic University, Candidate of economic sciences |f 1976- |g Vladislav Vladimirovich |9 15195 | |
| 701 | 1 | |a Bragin |b A. D. |c specialist in the field of electrical engineering |c Senior Lecturer of Tomsk Polytechnic University |f 1991- |g Aleksandr Dmitrievich |y Tomsk |9 21943 | |
| 701 | 1 | |a Leonova |b V. A. |c specialist in the field of economics |c assistant of Tomsk Polytechnic University |f 1998- |g Victoria Aleksandrovna |9 88586 | |
| 701 | 1 | |a Spitsina (Spitsyna) |b L. Yu. |c Economist |c Associate Professor of Tomsk Polytechnic University, Candidate of economic sciences |f 1976- |g Lubov Yurievna |9 18510 | |
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