Publication Library
FinRobot An Open Source AI Agent Platform for Financial Applications using Large Language Models
Description: As financial institutions and professionals increasingly incorporate Large Language Models (LLMs) into their workflows, substantial barriers, including proprietary data and specialized knowledge, persist between the finance sector and the AI community. These challenges impede the AI community’s ability to enhance financial tasks effectively. Acknowledging financial analysis’s critical role, we aim to devise financial-specialized LLMbased toolchains and democratize access to them through open-source initiatives, promoting wider AI adoption in financial decision-making. In this paper, we introduce FinRobot, a novel open-source AI agent platform supporting multiple financially specialized AI agents, each powered by LLM. Specifically, the platform consists of four major layers: 1) the Financial AI Agents layer that formulates Financial Chain-of-Thought (CoT) by breaking sophisticated financial problems down into logical sequences; 2) the Financial LLM Algorithms layer dynamically configures appropriate model application strategies for specific tasks; 3) the LLMOps and DataOps layer produces accurate models by applying training/finetuning techniques and using task-relevant data; 4) the Multi-source LLM Foundation Models layer that integrates various LLMs and enables the above layers to access them directly. Finally, FinRobot provides hands-on for both professionalgrade analysts and laypersons to utilize powerful AI techniques for advanced financial analysis. We open-source FinRobot at https://github. com/AI4Finance-Foundation/FinRobot.
Created At: 14 December 2024
Updated At: 14 December 2024
Instruct FinGPT Financial Sentiment Analysis by Instruction Tuning of General Purpose Large Language Models
Description: Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements. Despite the impressive capabilities of large language models (LLMs) in financial natural language processing (NLP), they still struggle with accurately interpreting numerical values and grasping financial context, limiting their effectiveness in predicting financial sentiment. In this paper, we introduce a simple yet effective instruction tuning approach to address these issues. By transforming a small portion of supervised financial sentiment analysis data into instruction data and finetuning a general-purpose LLM with this method, we achieve remarkable advancements in financial sentiment analysis. In the experiment, our approach outperforms state-of-the-art supervised sentiment analysis models, as well as widely used LLMs like ChatGPT and LLaMAs, particularly in scenarios where numerical understanding and contextual comprehension are vital.
Created At: 14 December 2024
Updated At: 14 December 2024
AI in Investment Analysis LLMs for Equity Stock Ratings
Description: Investment Analysis is acornerstoneoftheFinancial Services industry. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), offers opportunities to enhance the equity stock rating process. This paper explores the application of LLMs to predict stock performance and generate stock ratings by ingesting diverse datasets. Traditional stock rating methods rely heavily on the expertise of financial analysts, and face several challenges such as data overload, inconsistencies in filings, and delayed reactions to market events. Our study addresses these issues by leveraging LLMs to improve the accuracy and consistency of stock ratings. Additionally, we assess the efficacy of using different data modalities with LLMs for the financial domain. We utilize varied datasets comprising fundamental financial, market, and news data from January 2022 to June 2024, along with GPT-4-32k (v0613) (with a training cutoff in Sep. 2021 to prevent information leakage). Our results show that our benchmark method outperforms traditional stock rating methods when assessed by forward returns. Specifically, incorporating financial fundamentals enhances ratings accuracy. While integrating news data improves short-termperformance,substitutingdetailednewssummarieswith sentiment scores reduces token use without loss of performance. In many cases, omitting news data entirely enhances performance by reducing bias. Our research shows that LLMs can be leveraged to effectively utilize large amounts of multimodal financial data, as showcased by their effectiveness at the stock rating prediction task. Our work provides a reproducible framework for generating consistent and accurate stock ratings, offering a cost-effective and efficient alternative to traditional methods. Future work will extend the analysis to longer time horizons, incorporating more diverse data, and utilizing newer models to enhance detailed investment analysis and reports.
Created At: 14 December 2024
Updated At: 14 December 2024
Time-Causal VAE Robust Financial Time Series Generator
Description: We build a time-causal variational autoencoder (TC-VAE) for robust generation of financial time series data. Our approach imposes a causality constraint on the encoder and decoder networks, ensuring a causal transport from the real market time series to the fake generated time series. Specifically, we prove that the TC-VAE loss provides an upper bound on the causal Wasserstein distance between market distributions and generated distributions. Consequently, the TC-VAE loss controls the discrepancy between optimal values of various dynamic stochastic optimization problems under real and generated distributions. To further enhance the model’s ability to approximate the latent representation of the real market distribution, we integrate a RealNVP prior into the TC-VAE framework. Finally, extensive numerical experiments show that TC-VAE achieves promising results on both synthetic and real market data. This is done by comparing real and generated distributions according to various statistical distances, demonstrating the effectiveness of the generated data for downstream financial optimization tasks, as well as showcasing that the generated data reproduces stylized facts of real financial market data.
Created At: 14 December 2024
Updated At: 14 December 2024
GPT-Guided Monte Carlo Tree Search for Symbolic Regression in Financial Fraud Detection
Description: With the increasing number of financial services available online, the rate of financial fraud has also been increasing. The traffic and transaction rates on the internet have increased considerably, leading to a need for fast decision-making. Financial institutions also have stringent regulations that often require transparency and explainability of the decision-making process. However, most stateof-the-art algorithms currently used in the industry are highly parameterized black-box models that rely on complex computations to generate a score. These algorithms are inherently slow and lack the explainability and speedoftraditional rule-based learners. This work introduces SR-MCTS (Symbolic Regression MCTS), which utilizes a foundational GPT modelto guidetheMCTS,significantly enhancing its convergence speed and the quality of the generated expressions which are further extracted to rules. Our experiments show that SR-MCTS can detect fraud more efficiently than widely used methods in the industry while providing substantial insights into the decision-making process.
Created At: 14 December 2024
Updated At: 14 December 2024