Now showing 1 - 10 of 14
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It’s a Peoples Game, Isn’t It?! A Comparison between the Investment Returns of Business Angels and Machine Learning Algorithms

2022-07 , Blohm, Ivo , Antretter, Torben , Siren, Charlotta , Wincent, Joakim , Grichnik, Dietmar

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Should business angels diversify their investment portfolios to achieve higher performance? The role of knowledge access through co-investment networks.

2020-09-05 , Antretter, Torben , Siren, Charlotta , Grichnik, Dietmar , Wincent, Joakim

This paper investigates the performance effects of business angel portfolio industry diversification. Using a unique bi-annual panel dataset of 142 members of a professional angel investment platform and their portfolio returns between 2013 and 2017, we consider the costs and benefits of diversifying investments into various industries. Drawing upon theoretical arguments about distant search, we theorize and find a nonlinear (S-shaped) relationship between portfolio industry diversification and performance. Further, we pay specific attention to a proposed overdiversification effect that takes place at high levels of portfolio industry diversification and show that this effect is moderated by individuals' access to industry knowledge through their co-investment networks. For business angels who have a central position within a diverse network of industry specialists, the overdiversification effect is less pronounced.

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Business Angels and Their Co-Investment Networks: A Longitudinal Analysis of Angel Group Members' Portfolio Returns

2018 , Antretter, Torben , Sirén, Charlotta , Grichnik, Dietmar , Wincent, Joakim

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Startup Navigator: Guiding Your Entrepreneurial Journey

2020 , Grichnik, Dietmar , Hess, Manuel , Probst, Diego , Antretter, Torben , Pukall, Britta

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The role of skill versus luck in new venture survival

2021-04-29 , Soto-Simeone, Aracely , Sirén, Charlotta , Antretter, Torben

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New Venture Survival: A Review and Extension

2020-05-23 , Soto-Simeone, Aracely , Sirén, Charlotta , Antretter, Torben

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Predicting Startup Survival from Digital Traces: Towards a Procedure for Early Stage Investors

2018-12-13 , Antretter, Torben , Blohm, Ivo , Grichnik, Dietmar

We investigate whether digital traces can be used to predict early stage startup survival. Based on common survival factors from the entrepreneurship literature, we mined the digital footprints of 542 entrepreneurs and their ventures. Using a context-specific text mining approach, we performed a bootstrapping simulation in which we predict 5-year survival for different survival rates that range from 50% to 10%. Our results indicate that we can predict 5-year survival with an accuracy of up to 91%. With this study, we will provide an evidence-based taxonomy of digital traces for predicting early stage startup survival, identify the most important digital traces for doing so and benchmark our predictive approach against the actual investments of 339 business angels.

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Do Algorithms Make Better — and Fairer — Investments Than Angel Investors?

2020-11-02 , Antretter, Torben , Blohm, Ivo , Siren, Charlotta , Grichnik, Dietmar , Malmstrom, Malin , Wincent, Joakim

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Predicting new venture survival: A Twitter-based machine learning approach to measuring online legitimacy

2019-01-07 , Antretter, Torben , Blohm, Ivo , Grichnik, Dietmar , Wincent, Joakim

Research indicates that interactions on social media can reveal remarkably valid predictions about future events. In this study, we show that online legitimacy as a measure of social appreciation based on Twitter content can be used to accurately predict new venture survival. Specifically, we analyze more than 187,000 tweets from 253 new ventures' Twitter accounts using context-specific machine learning approaches. Our findings suggest that we can correctly discriminate failed ventures from surviving ventures in up to 76% of cases. With this study, we contribute to the ongoing discussion on the importance of building legitimacy online and provide an account of how to use machine learning methodologies in entrepreneurship research.

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Business Angels and Their Co-Investment Networks: A Longitudinal Analysis of Angel Group Members' Portfolio Returns

2018-06-06 , Antretter, Torben , Sirén, Charlotta , Grichnik, Dietmar , Wincent, Joakim