Deriving successful venture capital deal profile through decision tree analysis in Indian context

2020 ◽  
Vol 16 (2) ◽  
pp. 97-108
Author(s):  
Sarita Mishra ◽  
Dinabandhu Bag
Keyword(s):  
Decision Tree ◽  
Venture Capital ◽  
Performance Model ◽  
Decision Tree Analysis ◽  
Tree Analysis ◽  
Content Type ◽  
Success Model ◽  
Control Right ◽  
And Control ◽  
Quantitative Indicators

PurposeThis study is based on the development of predictive classification for the success of a venture capital (VC) deal derived from both qualitative and quantitative indicators.Design/methodology/approachDecision tree analysis has used for devising the success model of VC deal. Various deal characteristics are considered in this study as the observable component of success.FindingsThe finding of this analysis indicates that the success of the deal does not only depend on the final outcome like post company valuation (POST_COMP), realised revenue (RREV) but also depends on various observable contractual characteristics like syndication, use of convertible security and ownership percentage with some noticeable deal features.Practical implicationsThis study increases the further scope of study on a contractual mechanisms such as allocation of cash flow right and control right in the deal contract between venture investor and entrepreneur firm. This could give a better understanding of success path of a venture deal.Originality/valueThis study has attempted to derive a performance model based on observable attributes of a VC deal.

Optimal design of venture capital financing contracts: the case of Portuguese, Spanish and German markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maria do Rosario Correia ◽  
Raquel F. Ch Meneses
Keyword(s):  
Venture Capital ◽  
Qualitative Comparative Analysis ◽  
Contract Design ◽  
Control Rights ◽  
Content Type ◽  
Convertible Securities ◽  
Mixed Support ◽  
Capital Financing ◽  
Control Right ◽  
And Control

Purpose This study aims to investigate the use of convertible securities and control rights covenants for a sample of 53 Portuguese, Spanish and German venture capital (VC) firms. Design/methodology/approach A relatively new methodology in business sciences – a fuzzy set qualitative comparative analysis – that considers both quantitative and qualitative factors is used for obtaining a solution that best fits the empirical data. Findings The results show that the use of convertible securities is affected by agency predictions, namely, the anticipated severity of double-sided moral hazard problems. On the other hand, a mixed support is provided to the agency predictions regarding the use of control right covenants. The results seem to suggest that control right covenants tend to play a different role from convertible securities in the optimization of contract design for VC-backed investments. Originality/value Existing literature on VC contract design is extended by providing a cross-border analysis to VC financing decision.

Predicting recovery in patients suffering from traumatic brain injury by using admission variables and physiological data: a comparison between decision tree analysis and logistic regression

2002 ◽  
Vol 97 (2) ◽  
pp. 326-336 ◽  
Author(s):  
Peter J. D. Andrews ◽  
Derek H. Sleeman ◽  
Patrick F. X. Statham ◽  
Andrew McQuatt ◽  
Vincent Corruble ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Pupillary Response ◽  
Decision Tree Analysis ◽  
Patient Age ◽  
Tree Analysis ◽  
Content Type ◽  
Patient Âgé ◽  
Wide Range ◽  
Fine Print

Object. Decision tree analysis highlights patient subgroups and critical values in variables assessed. Importantly, the results are visually informative and often present clear clinical interpretation about risk factors faced by patients in these subgroups. The aim of this prospective study was to compare results of logistic regression with those of decision tree analysis of an observational, head-injury data set, including a wide range of secondary insults and 12-month outcomes. Methods. One hundred twenty-four adult head-injured patients were studied during their stay in an intensive care unit by using a computerized data collection system. Verified values falling outside threshold limits were analyzed according to insult grade and duration with the aid of logistic regression. A decision tree was automatically produced from root node to target classes (Glasgow Outcome Scale [GOS] score). Among 69 patients, in whom eight insult categories could be assessed, outcome at 12 months was analyzed using logistic regression to determine the relative influence of patient age, admission Glasgow Coma Scale score, Injury Severity Score (ISS), pupillary response on admission, and insult duration. The most significant predictors of mortality in this patient set were duration of hypotensive, pyrexic, and hypoxemic insults. When good and poor outcomes were compared, hypotensive insults and pupillary response on admission were significant. Using decision tree analysis, the authors found that hypotension and low cerebral perfusion pressure (CPP) are the best predictors of death, with a 9.2% improvement in predictive accuracy (PA) over that obtained by simply predicting the largest outcome category as the outcome for each patient. Hypotension was a significant predictor of poor outcome (GOS Score 1–3). Low CPP, patient age, hypocarbia, and pupillary response were also good predictors of outcome (good/poor), with a 5.1% improvement in PA. In certain subgroups of patients pyrexia was a predictor of good outcome. Conclusions. Decision tree analysis confirmed some of the results of logistic regression and challenged others. This investigation shows that there is knowledge to be gained from analyzing observational data with the aid of decision tree analysis.

Modeling hedonic motive–based segments of wine festival visitors using decision tree approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Valsaraj Payini ◽  
Kartikeya Bolar ◽  
Jyothi Mallya ◽  
Vasanth Kamath
Keyword(s):  
Cluster Analysis ◽  
Decision Tree ◽  
Social Status ◽  
Perceived Value ◽  
Primary Data ◽  
Decision Tree Analysis ◽  
Tree Analysis ◽  
Content Type ◽  
Informal Group ◽  
Elite Group

Purpose This study aims to identify and validate the different clusters of wine festival visitors based on their hedonic motivation. Further, this study also sought how identified clusters were different in terms of perceived value, satisfaction and loyalty to the wine festival. Design/methodology/approach A survey was conducted during the International Beach Wine Festival held in Karnataka, India, to collect primary data from 400 visitors. Data were subjected to a two-step cluster analysis. Further, cluster segmentation based on visitors’ demographics, perceived value, satisfaction and loyalty was conducted. Decision tree analysis based on recursive partitioning algorithm was used to validate the clusters. Findings A two-step cluster analysis identified two distinct segments and named those as elite and informal visitors based on hedonic motivation. The cluster scores show that the elite group had the best ratings on social status, socialization and family harmony. On the other hand, the informal group had top scores for wine tasting, enjoyment, change from routine and the festival atmosphere. Decision tree analysis results indicate that social status enjoyment and taste motives differentiate an informal group from the elite group. Research limitations/implications This study was conducted in a wine festival held in a single location. To assess the strength of the results, case studies in other regions will be of importance. Originality/value This study extended the knowledge of the wine festival by adapting hedonic motivation as a basis for wine festival segmentation. Besides, this study’s empirical findings would greatly benefit wine festival organizers to formulate an appropriate marketing strategy to target each wine festival visitors’ cluster based on the differentiating factors obtained from the decision tree modelling.

scholarly journals Risk of Pre-Malignancy or Malignancy in Postmenopausal Endometrial Polyps: A CHAID Decision Tree Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1094
Author(s):  
Michael Wong ◽  
Nikolaos Thanatsis ◽  
Federica Nardelli ◽  
Tejal Amin ◽  
Davor Jurkovic
Keyword(s):  
Decision Tree ◽  
Expectant Management ◽  
Decision Tree Model ◽  
Decision Tree Analysis ◽  
Focal Lesions ◽  
Tree Model ◽  
Normal Endometrium ◽  
Endometrial Polyps ◽  
Tree Analysis ◽  
Interaction Detection

Background and aims: Postmenopausal endometrial polyps are commonly managed by surgical resection; however, expectant management may be considered for some women due to the presence of medical co-morbidities, failed hysteroscopies or patient’s preference. This study aimed to identify patient characteristics and ultrasound morphological features of polyps that could aid in the prediction of underlying pre-malignancy or malignancy in postmenopausal polyps. Methods: Women with consecutive postmenopausal polyps diagnosed on ultrasound and removed surgically were recruited between October 2015 to October 2018 prospectively. Polyps were defined on ultrasound as focal lesions with a regular outline, surrounded by normal endometrium. On Doppler examination, there was either a single feeder vessel or no detectable vascularity. Polyps were classified histologically as benign (including hyperplasia without atypia), pre-malignant (atypical hyperplasia), or malignant. A Chi-squared automatic interaction detection (CHAID) decision tree analysis was performed with a range of demographic, clinical, and ultrasound variables as independent, and the presence of pre-malignancy or malignancy in polyps as dependent variables. A 10-fold cross-validation method was used to estimate the model’s misclassification risk. Results: There were 240 women included, 181 of whom presented with postmenopausal bleeding. Their median age was 60 (range of 45–94); 18/240 (7.5%) women were diagnosed with pre-malignant or malignant polyps. In our decision tree model, the polyp mean diameter (≤13 mm or >13 mm) on ultrasound was the most important predictor of pre-malignancy or malignancy. If the tree was allowed to grow, the patient’s body mass index (BMI) and cystic/solid appearance of the polyp classified women further into low-risk (≤5%), intermediate-risk (>5%–≤20%), or high-risk (>20%) groups. Conclusions: Our decision tree model may serve as a guide to counsel women on the benefits and risks of surgery for postmenopausal endometrial polyps. It may also assist clinicians in prioritizing women for surgery according to their risk of malignancy.

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