INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent

• URL: http://arxiv.org/abs/2412.18174v1
• Date: Tue, 24 Dec 2024 05:22:33 GMT
• Title: INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent
• Authors: Haohang Li, Yupeng Cao, Yangyang Yu, Shashidhar Reddy Javaji, Zhiyang Deng, Yueru He, Yuechen Jiang, Zining Zhu, Koduvayur Subbalakshmi, Guojun Xiong, Jimin Huang, Lingfei Qian, Xueqing Peng, Qianqian Xie, Jordan W. Suchow,
• Abstract summary: InvestorBench is a benchmark for evaluating large language model (LLM)-based agents in financial decision-making contexts. It provides a comprehensive suite of tasks applicable to different financial products, including single equities like stocks, cryptocurrencies and exchange-traded funds (ETFs) We also assess the reasoning and decision-making capabilities of our agent framework using thirteen different LLMs as backbone models.
• Score: 15.562784986263654
• License:
• Abstract: Recent advancements have underscored the potential of large language model (LLM)-based agents in financial decision-making. Despite this progress, the field currently encounters two main challenges: (1) the lack of a comprehensive LLM agent framework adaptable to a variety of financial tasks, and (2) the absence of standardized benchmarks and consistent datasets for assessing agent performance. To tackle these issues, we introduce \textsc{InvestorBench}, the first benchmark specifically designed for evaluating LLM-based agents in diverse financial decision-making contexts. InvestorBench enhances the versatility of LLM-enabled agents by providing a comprehensive suite of tasks applicable to different financial products, including single equities like stocks, cryptocurrencies and exchange-traded funds (ETFs). Additionally, we assess the reasoning and decision-making capabilities of our agent framework using thirteen different LLMs as backbone models, across various market environments and tasks. Furthermore, we have curated a diverse collection of open-source, multi-modal datasets and developed a comprehensive suite of environments for financial decision-making. This establishes a highly accessible platform for evaluating financial agents' performance across various scenarios.

Related papers

• EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents [63.43699771428243]
  EmbodiedBench is an extensive benchmark designed to evaluate vision-driven embodied agents. We evaluated 13 leading proprietary and open-source MLLMs within EmbodiedBench. MLLMs excel at high-level tasks but struggle with low-level manipulation.
  arXiv  Detail & Related papers  (2025-02-13T18:11:34Z)
• Demystifying Domain-adaptive Post-training for Financial LLMs [79.581577578952]
  FINDAP is a systematic and fine-grained investigation into domain adaptive post-training of large language models (LLMs) Our approach consists of four key components: FinCap, FinRec, FinTrain and FinEval. The resulting model, Llama-Fin, achieves state-of-the-art performance across a wide range of financial tasks.
  arXiv  Detail & Related papers  (2025-01-09T04:26:15Z)
• FinVision: A Multi-Agent Framework for Stock Market Prediction [0.0]
  This research introduces a multi-modal multi-agent system designed specifically for financial trading tasks. A key feature of our approach is the integration of a reflection module, which conducts analyses of historical trading signals and their outcomes.
  arXiv  Detail & Related papers  (2024-10-29T06:02:28Z)
• Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making [85.24399869971236]
  We aim to evaluate Large Language Models (LLMs) for embodied decision making. Existing evaluations tend to rely solely on a final success rate. We propose a generalized interface (Embodied Agent Interface) that supports the formalization of various types of tasks.
  arXiv  Detail & Related papers  (2024-10-09T17:59:00Z)
• Automate Strategy Finding with LLM in Quant investment [4.46212317245124]
  We propose a novel framework for quantitative stock investment in portfolio management and alpha mining. This paper proposes a framework where large language models (LLMs) mine alpha factors from multimodal financial data. Experiments on the Chinese stock markets demonstrate that this framework significantly outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-09-10T07:42:28Z)
• CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications [10.225210627594894]
  This paper presents our solution to IJCAI-2024 FinLLM challenge, investigating the capabilities of LLMs within three critical areas of financial tasks. Financial classification, financial text summarization, and single stock trading are investigated. Our approach aims to tackle these diverse tasks in a comprehensive and integrated manner, showcasing LLMs' capacity to address diverse and complex financial tasks with improved accuracy and decision-making capabilities.
  arXiv  Detail & Related papers  (2024-07-02T05:04:13Z)
• A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist [33.82344864007857]
  FinAgent is a multimodal foundation agent for financial trading tasks. It processes a diverse range of data-numerical, textual, and visual-to accurately analyze the financial market. It integrates established trading strategies and expert insights, ensuring that its trading approaches are both data-driven and rooted in sound financial principles.
  arXiv  Detail & Related papers  (2024-02-28T17:06:54Z)
• FinBen: A Holistic Financial Benchmark for Large Language Models [75.09474986283394]
  FinBen is the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks. FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading.
  arXiv  Detail & Related papers  (2024-02-20T02:16:16Z)
• Large Language Models in Finance: A Survey [12.243277149505364]
  Large language models (LLMs) have opened new possibilities for artificial intelligence applications in finance. Recent advances in large language models (LLMs) have opened new possibilities for artificial intelligence applications in finance.
  arXiv  Detail & Related papers  (2023-09-28T06:04:04Z)
• AgentBench: Evaluating LLMs as Agents [88.45506148281379]
  Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks. We present AgentBench, a benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities.
  arXiv  Detail & Related papers  (2023-08-07T16:08:11Z)
• PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance [63.51545277822702]
  PIXIU is a comprehensive framework including the first financial large language model (LLMs) based on fine-tuning LLaMA with instruction data. We propose FinMA by fine-tuning LLaMA with the constructed dataset to be able to follow instructions for various financial tasks. We conduct a detailed analysis of FinMA and several existing LLMs, uncovering their strengths and weaknesses in handling critical financial tasks.
  arXiv  Detail & Related papers  (2023-06-08T14:20:29Z)

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.