Deep learning driven company profiling
Creators: name: Yuan, Zixuan
Publisher: name: No Publisher Supplied
Year: 2023

description: Company profiling has become indispensable in the contemporary business landscape as it offers market-concerned insights into various critical facets of a company, including fundamentals, operations, financials, products, market presence, and competitive positioning. Such profiling approach forms a solid foundation that summarizes a holistic understanding of companies, empowering successful decision-making and driving business growth. The advent of digitalization in industries has revolutionized the construction of comprehensive company profiles by leveraging a wealth of market information (e.g., company announcements, social media), and facilitating the decision-making process in various business applications. Due to its significance and usefulness, numerous studies have been conducted via the case-by-case empirical analysis that leverages traditional financial theories, or the deep learning methods that reformulate the problem into various supervised learning tasks.However, both categories of approaches heavily rely on domain knowledge and are labor-intensive. More crucially, the task-specific nature of both approaches restricts the applicability of the resulting company profiles to various downstream applications. Our main contribution is to provide an intelligent deep-learning-driven company profiling solution to address different business problems, including Venture Capital (VC) investment and quantitative trading. Specifically, we first develop a self-supervised representation learning framework (SePaL) for dynamic company profiling. By leveraging the topological information derived from event graphs and employing advanced self-supervised learning techniques, SePaL acquires unified and robust company representations. These representations are highly resistant to the influence of event noises, ensuring their reliability in practical applications. Moreover, we can easily fine-tune SePaL with only a small amount of annotated data, making it a versatile tool for various downstream applications. We then manage to extend the use of company profiling to facilitate two key business applications. For the problem of VC investment, we propose a reinforcement learning-based method (AlphaVC) to facilitate venture capitalists' decision-making. Our policy-based reinforcement learning agents can dynamically identify the best candidates based on their profile representations and sequentially place the optimal investment amounts at proper rounds to maximize financial returns for a given portfolio. For the problem of quantitative trading}, we propose an online adaptive sequence learning (LEVER) framework to simultaneously predict the tick-level trading signals for a variety of securities, which are pre-selected based on their learned company profiles. Moreover, we devise a competitive active-meta learning paradigm to enhance the signal learners' learning efficiency for online implementation. The above approaches are validated for extensive experiments on real-world financial datasets, demonstrating their effectiveness, efficiency, robustness, and generalization ability. We have further conducted the case studies to reveal some interpretable findings which provide actionable insight into the business decision process.