Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems

• URL: http://arxiv.org/abs/2309.16888v3
• Date: Fri, 14 Jun 2024 11:30:25 GMT
• Title: Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems
• Authors: Lele Cao, Gustaf Halvardsson, Andrew McCornack, Vilhelm von Ehrenheim, Pawel Herman,
• Abstract summary: This paper addresses the growing application of data-driven approaches within the Private Equity (PE) industry. We present a comprehensive review of the relevant approaches and propose a novel approach for predicting the success likelihood of any candidate company. Our experiments on two real-world investment tasks, benchmarked towards three popular baselines, demonstrate the effectiveness of our approach.
• Score: 1.7343080574639578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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). We present a comprehensive review of the relevant approaches and propose a novel approach leveraging a Transformer-based Multivariate Time Series Classifier (TMTSC) for predicting the success likelihood of any candidate company. The objective of our research is to optimize sourcing performance for VC and GC investments by formally defining the sourcing problem as a multivariate time series classification task. We consecutively introduce the key components of our implementation which collectively contribute to the successful application of TMTSC in VC/GC sourcing: input features, model architecture, optimization target, and investor-centric data processing. Our extensive experiments on two real-world investment tasks, benchmarked towards three popular baselines, demonstrate the effectiveness of our approach in improving decision making within the VC and GC industry.

Related papers

• Trustworthiness in Retrieval-Augmented Generation Systems: A Survey [59.26328612791924]
  Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs) We propose a unified framework that assesses the trustworthiness of RAG systems across six key dimensions: factuality, robustness, fairness, transparency, accountability, and privacy.
  arXiv  Detail & Related papers  (2024-09-16T09:06:44Z)
• Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization [49.396692286192206]
  We study the use of deep reinforcement learning for responsible portfolio optimization by incorporating ESG states and objectives. Our results show that deep reinforcement learning policies can provide competitive performance against mean-variance approaches for responsible portfolio allocation.
  arXiv  Detail & Related papers  (2024-03-25T12:04:03Z)
• Multimodal Gen-AI for Fundamental Investment Research [2.559302299676632]
  This report outlines a transformative initiative in the financial investment industry, where the conventional decision-making process is being reimagined. We seek to evaluate the effectiveness of fine-tuning methods on a base model (Llama2) to achieve specific application-level goals. The project encompasses a diverse corpus dataset, including research reports, investment memos, market news, and extensive time-series market data.
  arXiv  Detail & Related papers  (2023-12-24T03:35:13Z)
• 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)
• Portfolio Selection via Topological Data Analysis [2.3901301169141056]
  We present a two-stage method for constructing an investment portfolio of common stocks. The method involves the generation of time series representations followed by their subsequent clustering. Experimental results show that our proposed system outperforms other methods.
  arXiv  Detail & Related papers  (2023-08-15T09:36:43Z)
• ChatGPT-based Investment Portfolio Selection [21.24186888129542]
  We explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. We use ChatGPT to obtain a universe of stocks from S&P500 market index that are potentially attractive for investing. Our findings indicate that ChatGPT is effective in stock selection but may not perform as well in assigning optimal weights to stocks within the portfolio.
  arXiv  Detail & Related papers  (2023-08-11T17:48:17Z)
• E2EAI: End-to-End Deep Learning Framework for Active Investing [123.52358449455231]
  We propose an E2E that covers almost the entire process of factor investing through factor selection, factor combination, stock selection, and portfolio construction. Experiments on real stock market data demonstrate the effectiveness of our end-to-end deep leaning framework in active investing.
  arXiv  Detail & Related papers  (2023-05-25T10:27:07Z)
• Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning [64.75603723249837]
  This work proposes a novel framework to dynamically manage and allocate different types of resources for Metaverse applications. We first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework.
  arXiv  Detail & Related papers  (2023-02-27T00:30:01Z)
• Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval [152.3504607706575]
  This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks. We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
  arXiv  Detail & Related papers  (2022-06-17T15:40:45Z)
• A Data-Driven Framework for Identifying Investment Opportunities in Private Equity [0.0]
  This paper proposes a framework for automated data-driven screening of investment opportunities. The framework draws on data from several sources to assess the financial and managerial position of a company. It then uses an explainable artificial intelligence (XAI) engine to suggest investment recommendations.
  arXiv  Detail & Related papers  (2022-04-04T21:28:34Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

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.