Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques

• URL: http://arxiv.org/abs/2407.04885v1
• Date: Fri, 5 Jul 2024 22:54:13 GMT
• Title: Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques
• Authors: Ekin Ozince, Yiğit Ihlamur,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study explores the application of large language models (LLMs) in venture capital (VC) decision-making, focusing on predicting startup success based on founder characteristics. 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. This framework for integrating ML techniques and LLMs has vast potential for improving startup success prediction, with important implications for VC firms seeking to optimize their investment strategies.

Related papers

• Teaching LLMs According to Their Aptitude: Adaptive Reasoning for Mathematical Problem Solving [55.895917967408586]
  Existing approaches to mathematical reasoning with large language models rely on Chain-of-Thought (CoT) for generalizability or Tool-Integrated Reasoning (TIR) for precise computation. We propose TATA (Teaching LLMs According to Their Aptitude), an adaptive framework that enables LLMs to personalize their reasoning strategy spontaneously.
  arXiv  Detail & Related papers  (2025-02-17T16:56:23Z)
• From Selection to Generation: A Survey of LLM-based Active Learning [153.8110509961261]
  Large Language Models (LLMs) have been employed for generating entirely new data instances and providing more cost-effective annotations. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques.
  arXiv  Detail & Related papers  (2025-02-17T12:58:17Z)
• EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization [62.16747639440893]
  Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering. Our proposed framework incorporates retrieval-augmented generation (RAG) to enhance the system's ability to acquire domain-specific knowledge and generate solutions.
  arXiv  Detail & Related papers  (2024-08-07T08:43:32Z)
• The Economic Implications of Large Language Model Selection on Earnings and Return on Investment: A Decision Theoretic Model [0.0]
  We use a decision-theoretic approach to compare the financial impact of different language models. The study reveals how the superior accuracy of more expensive models can, under certain conditions, justify a greater investment. This article provides a framework for companies looking to optimize their technology choices.
  arXiv  Detail & Related papers  (2024-05-27T20:08:41Z)
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing [56.75702900542643]
  We introduce AlphaLLM for the self-improvements of Large Language Models. It integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop. Our experimental results show that AlphaLLM significantly enhances the performance of LLMs without additional annotations.
  arXiv  Detail & Related papers  (2024-04-18T15:21:34Z)
• Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena [25.865825113847404]
  We introduce AucArena, a novel evaluation suite that simulates auctions. We conduct controlled experiments using state-of-the-art Large Language Models (LLMs) to power bidding agents to benchmark their planning and execution skills.
  arXiv  Detail & Related papers  (2023-10-09T14:22:09Z)
• 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)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Embracing advanced AI/ML to help investors achieve success: Vanguard Reinforcement Learning for Financial Goal Planning [0.0]
  Reinforcement learning is a machine learning approach that can be employed with complex data sets. We will explore the use of machine learning for financial forecasting, predicting economic indicators, and creating a savings strategy.
  arXiv  Detail & Related papers  (2021-10-18T18:46:20Z)
• 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.