Pairwise acquisition prediction with SHAP value interpretation

Futagami, Katsuya; Fukazawa, Yusuke; Kapoor, Nakul; Kito, Tomomi

doi:10.1016/j.jfds.2021.02.001

Cited by 81 publications

(37 citation statements)

References 32 publications

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“…To evaluate the goodness of our predictions, we compare s and computing the Best-F1. Finally, to show more easily interpretable results, we compute a normalized version of the Best-F1 metric, based on the method used in [ 54 ]. For each prediction, we divide the actual Best-F1 value with the one computed shuffling the array.…”

Section: Resultsmentioning

confidence: 99%

“…Pair Prediction : Given a set of companies, we want to predict which pairs of firms will undergo a M&A process. This is the prediction task investigated in [ 54 ]; here the point of view is of an external observer who compares all the possible pairs.…”

Section: Resultsmentioning

confidence: 99%

“…However, as pointed out in [ 25 ], we believe that a forecast constitutes an important test to compare the goodness of relatedness assessments. Notable forecast exercise includes [ 53 ], in which an ensemble learning algorithm is trained on a set of relative features between companies, built using patent data, to predict future acquisitions, and the attempt to M&A prediction in [ 54 ]. In this latter work, a very large set of M&A features is built employing financial, geographical, industrial, and patent data of firms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction and visualization of Mergers and Acquisitions using Economic Complexity

2023

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Mergers and Acquisitions represent important forms of business deals, both because of the volumes involved in the transactions and because of the role of the innovation activity of companies. Nevertheless, Economic Complexity methods have not been applied to the study of this field. By considering the patent activity of about one thousand companies, we develop a method to predict future acquisitions by assuming that companies deal more frequently with technologically related ones. We address both the problem of predicting a pair of companies for a future deal and that of finding a target company given an acquirer. We compare different forecasting methodologies, including machine learning and network-based algorithms, showing that a simple angular distance with the addition of the industry sector information outperforms the other approaches. Finally, we present the Continuous Company Space, a two-dimensional representation of firms to visualize their technological proximity and possible deals. Companies and policymakers can use this approach to identify companies most likely to pursue deals or explore possible innovation strategies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction and visualization of Mergers and Acquisitions using Economic Complexity

2023

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“…The contribution of a particular feature in a multidimensional dataset can then be evaluated based on process expertise 22 . At present, these interpretation techniques, which are model agnostic in nature, are mostly used in medicine [23][24][25][26] , finance 27,28 , etc. However, the application of these techniques in the field of chemical modelling is currently under-explored 22 .…”

Section: Logis C Regressionmentioning

confidence: 99%

Machine learning based interpretation of microkinetic data: a Fischer–Tropsch synthesis case study

et al. 2022

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“…PFI is a new global model-agnostic explanation technique that was recently used to identify the most relevant features in many fields, such as medicine [45], agriculture [46], and engineering [47]. Similarly, the SHAP method has been applied successfully to interpret local and global ML predictions in several studies in order to predict the risk of water erosion [48], estimate pairwise acquisition [49], investigate the factors that contribute to freight truck-related crashes [50], estimate the occurrence of benthic macroinvertebrate species [51], and predict the fuel properties of the chars [52].…”

Section: Locationmentioning

confidence: 99%

An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction

et al. 2021

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Machine learning (ML) models are commonly used in solar modeling due to their high predictive accuracy. However, the predictions of these models are difficult to explain and trust. This paper aims to demonstrate the utility of two interpretation techniques to explain and improve the predictions of ML models. We compared first the predictive performance of Light Gradient Boosting (LightGBM) with three benchmark models, including multilayer perceptron (MLP), multiple linear regression (MLR), and support-vector regression (SVR), for estimating the global solar radiation (H) in the city of Fez, Morocco. Then, the predictions of the most accurate model were explained by two model-agnostic explanation techniques: permutation feature importance (PFI) and Shapley additive explanations (SHAP). The results indicated that LightGBM (R2 = 0.9377, RMSE = 0.4827 kWh/m2, MAE = 0.3614 kWh/m2) provides similar predictive accuracy as SVR, and outperformed MLP and MLR in the testing stage. Both PFI and SHAP methods showed that extraterrestrial solar radiation (H0) and sunshine duration fraction (SF) are the two most important parameters that affect H estimation. Moreover, the SHAP method established how each feature influences the LightGBM estimations. The predictive accuracy of the LightGBM model was further improved slightly after re-examination of features, where the model combining H0, SF, and RH was better than the model with all features.

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Pairwise acquisition prediction with SHAP value interpretation

Cited by 81 publications

References 32 publications

Prediction and visualization of Mergers and Acquisitions using Economic Complexity

Prediction and visualization of Mergers and Acquisitions using Economic Complexity

Machine learning based interpretation of microkinetic data: a Fischer–Tropsch synthesis case study

An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction

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