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Found 7 papers, 4 papers with code
Sourcing Investment Targets for Venture and Growth Capital Using Multivariate Time Series Transformer
This paper addresses the growing application of data-driven approaches within the Private Equity (PE) industry, particularly in sourcing investment targets (i. e., companies) for Venture Capital (VC) and Growth Capital (GC).
Prompt Tuned Embedding Classification for Multi-Label Industry Sector Allocation
All limitations (a), (b), and (c) are addressed by replacing the PLM's language head with a classification head, which is referred to as Prompt Tuned Embedding Classification (PTEC).
CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification
To the best of our knowledge, CompanyKG is the first large-scale heterogeneous graph dataset originating from a real-world investment platform, tailored for quantifying inter-company similarity.
A Scalable and Adaptive System to Infer the Industry Sectors of Companies: Prompt + Model Tuning of Generative Language Models
The Private Equity (PE) firms operate investment funds by acquiring and managing companies to achieve a high return upon selling.
Using Deep Learning to Find the Next Unicorn: A Practical Synthesis
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.
Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data
Investment professionals rely on extrapolating company revenue into the future (i. e. revenue forecast) to approximate the valuation of scaleups (private companies in a high-growth stage) and inform their investment decision.
PAUSE: Positive and Annealed Unlabeled Sentence Embedding
Sentence embedding refers to a set of effective and versatile techniques for converting raw text into numerical vector representations that can be used in a wide range of natural language processing (NLP) applications.
