Using Deep Learning to Find the Next Unicorn: A Practical Synthesis

• URL: http://arxiv.org/abs/2210.14195v2
• Date: Mon, 10 Jun 2024 07:56:11 GMT
• Title: Using Deep Learning to Find the Next Unicorn: A Practical Synthesis
• Authors: Lele Cao, Vilhelm von Ehrenheim, Sebastian Krakowski, Xiaoxue Li, Alexandra Lutz,
• Abstract summary: Venture Capital (VC) strives to identify and invest in unicorn startups during their early stages, hoping to gain a high return. Over the past two decades, the industry has gone through a paradigm shift moving from conventional statistical approaches towards becoming machine-learning based. In this work, we carry out a literature review and synthesis on DL-based approaches, covering the entire DL life cycle.
• Score: 42.70427723009158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Startups often represent newly established business models associated with disruptive innovation and high scalability. They are commonly regarded as powerful engines for economic and social development. Meanwhile, startups are heavily constrained by many factors such as limited financial funding and human resources. Therefore, the chance for a startup to eventually succeed is as rare as "spotting a unicorn in the wild". Venture Capital (VC) strives to identify and invest in unicorn startups during their early stages, hoping to gain a high return. To avoid entirely relying on human domain expertise and intuition, investors usually employ data-driven approaches to forecast the success probability of startups. Over the past two decades, the industry has gone through a paradigm shift moving from conventional statistical approaches towards becoming machine-learning (ML) based. Notably, the rapid growth of data volume and variety is quickly ushering in deep learning (DL), a subset of ML, as a potentially superior approach in terms of capacity and expressivity. In this work, we carry out a literature review and synthesis on DL-based approaches, covering the entire DL life cycle. The objective is a) to obtain a thorough and in-depth understanding of the methodologies for startup evaluation using DL, and b) to distil valuable and actionable learning for practitioners. To the best of our knowledge, our work is the first of this kind.

Related papers

• A Fused Large Language Model for Predicting Startup Success [21.75303916815358]
  We develop a machine learning approach with the aim of locating successful startups on venture capital platforms. Specifically, we develop, train, and evaluate a tailored, fused large language model to predict startup success. Using 20,172 online profiles from Crunchbase, we find that our fused large language model can predict startup success.
  arXiv  Detail & Related papers  (2024-09-05T16:22:31Z)
• Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques [0.0]
  This study explores the application of large language models (LLMs) in venture capital (VC) decision-making. We utilize LLM prompting techniques, like chain-of-thought, to generate features from limited data, then extract insights through statistics and machine learning. Our results reveal potential relationships between certain founder characteristics and success, as well as demonstrate the effectiveness of these characteristics in prediction.
  arXiv  Detail & Related papers  (2024-07-05T22:54:13Z)
• Exploration with Principles for Diverse AI Supervision [88.61687950039662]
  Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation. We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
  arXiv  Detail & Related papers  (2023-10-13T07:03:39Z)
• Startup success prediction and VC portfolio simulation using CrunchBase data [1.7897779505837144]
  This paper focuses on startups at their Series B and Series C investment stages, aiming to predict key success milestones. We introduce novel deep learning model for predicting startup success, integrating a variety of factors such as funding metrics, founder features, industry category. Our work demonstrates the considerable promise of deep learning models and alternative unstructured data in predicting startup success.
  arXiv  Detail & Related papers  (2023-09-27T10:22:37Z)
• Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction [104.29108668347727]
  This paper proposes an innovative knowledge graph generation approach that leverages the potential of the latest generative large language models. The approach is conveyed in a pipeline that comprises novel iterative zero-shot and external knowledge-agnostic strategies. We claim that our proposal is a suitable solution for scalable and versatile knowledge graph construction and may be applied to different and novel contexts.
  arXiv  Detail & Related papers  (2023-07-03T16:01:45Z)
• Solving the Data Sparsity Problem in Predicting the Success of the Startups with Machine Learning Methods [2.939434965353219]
  We investigate several machine learning algorithms with a large dataset from Crunchbase. The results suggest that LightGBM and XGBoost perform best and achieve 53.03% and 52.96% F1 scores. These findings have substantial implications on how machine learning methods can help startup companies and investors.
  arXiv  Detail & Related papers  (2021-12-15T09:21:32Z)
• INTERN: A New Learning Paradigm Towards General Vision [117.3343347061931]
  We develop a new learning paradigm named INTERN. By learning with supervisory signals from multiple sources in multiple stages, the model being trained will develop strong generalizability. In most cases, our models, adapted with only 10% of the training data in the target domain, outperform the counterparts trained with the full set of data.
  arXiv  Detail & Related papers  (2021-11-16T18:42:50Z)
• WenLan 2.0: Make AI Imagine via a Multimodal Foundation Model [74.4875156387271]
  We develop a novel foundation model pre-trained with huge multimodal (visual and textual) data. We show that state-of-the-art results can be obtained on a wide range of downstream tasks.
  arXiv  Detail & Related papers  (2021-10-27T12:25:21Z)
• Automated Machine Learning, Bounded Rationality, and Rational Metareasoning [62.997667081978825]
  We will look at automated machine learning (AutoML) and related problems from the perspective of bounded rationality. Taking actions under bounded resources requires an agent to reflect on how to use these resources in an optimal way.
  arXiv  Detail & Related papers  (2021-09-10T09:10:20Z)
• Graph Neural Network Based VC Investment Success Prediction [11.527912247719915]
  We design an incremental representation learning mechanism and a sequential learning model, utilizing the network structure together with the rich attributes of the nodes. Our method achieves the state-of-the-art prediction performance on a comprehensive dataset of global venture capital investments. It excels at predicting the outcomes for start-ups in industries such as healthcare and IT.
  arXiv  Detail & Related papers  (2021-05-25T14:29:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.