Data Driven Analysis for Startup Investments for Venture Capitalists

Singhal, Jaiesh; Rane, Chinmayi; Wadalkar, Yash; Joshi, Mohit; Deshpande, Amol

doi:10.1109/iconat53423.2022.9725892

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…al. [1] has developed a comprehensive solution that simplifies startup analysis for the investors. As in today's world, investing in startups is becoming increasingly popular.…”

Section: Literature Reviewmentioning

confidence: 99%

“…III. EXISTING WORK [1] This project aims to simplify startup analysis for investors who lack the knowledge and resources needed to make data-driven investment decisions. It offers a unique 3-in-1 analysis approach, including founder analysis, investment trend analysis, and a success predictor, which are crucial for investing in new-age startups, especially in times of heightened risk.…”

Section: Literature Reviewmentioning

confidence: 99%

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Start-up Acquisition Status Prediction Using Machine Learning

Mane¹

2023

IJRASET

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Startup acquisition is the process where companies interested in investing in startups buy and own some or all of the startup's shares. However, investing in startups requires extensive research as many startups fail within a short period of time, and other factors such as IPO and acquisition by other companies also need to be considered. The recent advances in machine learning have made it possible to predict a startup's acquisition status based on financial statistics, historic data and market trends. The model uses supervised learning to find patterns and correlations between the input features (startup characteristics) and the related acquisition status by learning from labelled data. The prediction model is constructed using machine learning algorithms like KNN and random forests which can save time and costs for investors and speed up their investment process. Also, it aims to classify a startup's acquisition status as Operating, Acquired, IPO, or Closed based on input data. When using the Random Forest approach, the suggested system for predicting acquisition status achieves an accuracy score of 93%, whereas using the KNN (K-Nearest Neighbours) strategy, it earns a score of 91%.

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“…al. [1] has developed a comprehensive solution that simplifies startup analysis for the investors. As in today's world, investing in startups is becoming increasingly popular.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Start-up Acquisition Status Prediction Using Machine Learning

Mane¹

2023

IJRASET

View full text Add to dashboard Cite

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“…Predicting which start-ups will ultimately succeed is critical for venture capitalists and governments worldwide, significantly when the benefits can multiply exponentially (Singhal et al, 2022). For this purpose, analyzing Twitter using the Social Network Analysis (SAA) approach can determine how fast the startup can grow and how well it can be recognized by the public or the market (Arifianto and Veritia, 2022).…”

Section: Social Mediamentioning

confidence: 99%

Social Media Interaction of Foreign Users in Getir of Turkey's Second Unicorn: Twitter Sentiment Analysis

Korkmaz

2022

Yönetim Ve Ekonomi Araştırmaları Dergisi

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Social media interactions, the digital form of classical word-of-mouth marketing (AAP), used to strengthen the business-brand image, have become one of the most critical evaluation criteria today. It is known how effective social media is in maximizing brand awareness and sales. For this purpose, the social media contents of the users abroad of Getir, Turkey's largest unicorn company, were analyzed. In this direction, the contents of the tweets posted in English from July 1, 2021, when Getir was launched to the European market in general, until July 1, 2022, were analyzed. Python programming language was used for data collection, and R language was used for data analysis. Social network analysis (SAA) of the most used words in the context of positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise, and trust of the emotional states of the tweets posted for Getir was performed. Social network analysis (SAA) was conducted in the context of positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise, and trust of the most used words in the emotional states of tweets for Getir. As a result of the analysis, it was determined that the positive emotions of the users towards Getir were higher than the negative emotions. It has been determined that the company's development performance and social media analysis results are in parallel.

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“…The compound annual growth rate (CAGR) of this market is estimated to be 28.5% (16). The journey of a health start-up in providing health services is fraught with uncertainty, which contributes to its high failure rate (29). Studies on the factors that contribute to the success of these kinds of startups are scarce.…”

Section: Introductionmentioning

confidence: 99%

Developing a Model for the Success of Healthcare Start-up Companies in Iran: A Systematic Review and Interviews with Experts

Majedi,

Meskarpour-Amiri,

Yaghoubi

2024

Preprint

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Background: In the dynamic landscape of healthcare, start-ups drive transformative change. Understanding the pivotal factors influencing their success is paramount to their advancement. This study aims to present a comprehensive framework delineating the drivers of success for emerging businesses in healthcare. Methods: Employing a mixed-method approach, this research integrates expert interviews and a systematic review of pertinent literature. Extensive scientific exploration across databases, augmented by manual searches and expert surveys, identified 57 influential factors from 37 selected articles and interviews. Through expert consultations, additional factors were incorporated, culminating in an 11-dimensional classification. Finally, the position of the factors in 4 stages (before starting the business, during the activity, evaluation, control and feedback) was determined, and the final model was extracted. Results: Based on the inclusion and exclusion criteria, a total of 37 articles were reviewed. Eleven dimensions were utilized to classify the 57 factors identified in this study. The factors "choosing the appropriate marketing and advertising strategy" and "targeting how to finance the business" had the highest number of repetitions in the studies; 49% of these factors were related to the stage "before starting the business", and 33% were related to "during activity". Discussion: To ensure growth and development at various stages of business formation, founders of start-up businesses in the healthcare sector need to carefully consider the factors that impact success, particularly business financing and marketing and advertising strategies.

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Data Driven Analysis for Startup Investments for Venture Capitalists

Cited by 7 publications

References 10 publications

Start-up Acquisition Status Prediction Using Machine Learning

Start-up Acquisition Status Prediction Using Machine Learning

Social Media Interaction of Foreign Users in Getir of Turkey's Second Unicorn: Twitter Sentiment Analysis

Developing a Model for the Success of Healthcare Start-up Companies in Iran: A Systematic Review and Interviews with Experts

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