Role of Angel Investor in Indian Startup Ecosystem

Rao, S. V. Ramana; Kumar, Lohith

doi:10.1177/2455265820160101

Cited by 10 publications

(4 citation statements)

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“…First, the pre-seed stage start-ups are younger than seed-stage ventures. Second, they have less fund appetite and raise smaller investments, in line with the startups' funding life-cycle (Rao and Kumar, 2016). Third, male angel investors are confident in making perceived high-risk, pre-seed stage investments, which confirms the behavioral finance theory suggesting the overconfidence of men in financial decisionmaking (Barber and Odean, 2001).…”

Section: Categorization Of Variablessupporting

confidence: 53%

What differentiates angel investors in pre-seed versus seed-stage investments? Evidence from India

Annamalaisami

2021

JIBR

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Purpose Angel investments are increasingly getting specialized. In recent years, start-ups are raising pre-seed funding before seed-stage funding. Investors in pre-seed and seed-stage companies commonly are angel investors. The purpose of this paper is to understand the differences between these two groups of angel investors. Design/methodology/approach Data for this study obtained from angel funding deals from the sources such as Venture Intelligence, VCCEdge, Keiretsu Forum, Dealcurry and The Chennai Angels. A total of 732 angel investments made by 405 investors during 2014–18 were used in the analysis. Non-parametric tests and regression estimations were used to identify the differences between angel investors investing in pre-seed and seed-stage ventures. An index was developed to measure the extent of syndication in angel investments and used as an independent variable in the regression. Findings There are significant differences between angel investors investing in pre-seed and seed-stage ventures. The results show that angels with more industry-specific experience make a higher proportion of investment in seed-stage ventures. Seed-stage ventures attract investors from Tier-1 cities, whereas the pre-seed stage has higher investors from smaller cities. Though the investment size is smaller, the extent of syndication is greater in pre-seed stage investments. Originality/value To differentiate the angel investments between pre-seed and seed-stage funding, this study uses data from Indian start-ups. Further, this study develops a composite syndication index to measure the extent of syndication in angel investments and assesses its impact on an angel investor’s choice of pre-seed stage investments.

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Section: Categorization Of Variablessupporting

confidence: 53%

What differentiates angel investors in pre-seed versus seed-stage investments? Evidence from India

Annamalaisami

2021

JIBR

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“…This finding may be due to Indian startups' difficulties in finding funding to craft robust marketing and development plans to acquire funding. As also discussed by Korreck [49] and Rao and Kumar [60], due to the shortage of investors, it is difficult for startups to acquire these investment rounds.…”

Section: Discussionmentioning

confidence: 99%

Identifying Startups Business Opportunities from UGC on Twitter Chatting: An Exploratory Analysis

Saura

Reyes-Menéndez

Matos

et al. 2021

JTAER

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The startup business ecosystem in India has experienced exponential growth. The amount of investment in Indian startups in the last decade demonstrates the strong interest of the technology industry to these business models based on innovation. In this context, the present study aims to identify investment opportunities for investors in Indian startups by identifying key indicators that characterize the startup ecosystem in India. To this end, a three steps data mining method is developed using data mining techniques. First, a sentiment analysis (SA), a machine learning approach that classifies the topics into groups expressing feelings, is applied to a dataset. Next, we develop a Latent Dirichlet Allocation (LDA) model, a topic-modeling technique that divides the sample of n = 14.531 tweets from Twitter into topics, using user-generated content (UGC) as data. Finally, in order to identify the characteristics of each topic we apply textual analysis (TA) to identify key indicators. The originality of the present study lies in the methodological process used for data analysis. Our results also contribute to the literature on startups. The results demonstrate that the Indian startup ecosystem is influenced by areas such as fintech, innovation, crowdfunding, hardware, funds, competition, artificial intelligence, augmented reality and electronic commerce. Of note, in view of the exploratory approach of the present study, the results and implications should be taken as descriptive, rather than determining for future investments in the Indian startup ecosystem.

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“…Angel investors provide funding to startup firms. They are successful individuals who invest their own funds in promising business projects for potential returns (Rao and Kumar, 2016). 15) grant indicates the amount of financial support as a grant from both private or public sectors.…”

Section: Our Purposed Modelmentioning

confidence: 99%

Factor analysis and prediction of startups and ways to exit based on decision tree classification models with adaptive k with SMOTE method for imbalance problem

Songpan,

Kijkasiwat

2023

SEHS

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This paper focuses on factor analysis to combine the information of startups with an synthetic minority over-sampling technique (SMOTE) method via an aspect of the decision tree algorithms that assist investors in project screening for describing important features.However, the investment of a startup company has characteristics of imbalanced data. Improvements in the handling of imbalanced data based on the SMOTE method has been developed by sampling from the minority class. The problem is how to set optimized k-nearest neighbors among the most common feature values. This work purposed a method to fit data in the startup’s information that is designed to handle the data value by adaptive k with SMOTE, which manages the problem with an imbalance class label for robustness of evaluation metrics for balancing the portion of multi-class. The adaptive k experimental results can solve the k parameter setting and produce a high accuracy rate of startup companies’ class as closed, operating, and acquired status of investment at 0.84, 0.87 and 0.97 respectively. The overall accuracy rate is 0.99; that is the best outcome compared with other methods for handling imbalance. In addition, the results and discussion shown that can meet the needs of investment startup are designed and discussed of business views and machine learning views to work co-operation.

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Role of Angel Investor in Indian Startup Ecosystem

Cited by 10 publications

References 0 publications

What differentiates angel investors in pre-seed versus seed-stage investments? Evidence from India

What differentiates angel investors in pre-seed versus seed-stage investments? Evidence from India

Identifying Startups Business Opportunities from UGC on Twitter Chatting: An Exploratory Analysis

Factor analysis and prediction of startups and ways to exit based on decision tree classification models with adaptive k with SMOTE method for imbalance problem

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