BloombergGPT: A Large Language Model for Finance

Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, Gideon Mann

Key Topics: Domain-Specific Language Models, Financial Text Analysis, Model Training and Evaluation, Dataset Annotation, Financial Industry Applications

Link: here | AI Score: 🚀 🚀 🚀 | Interest Score: 🧲 🧲 | Reading Time: ⏰ ⏰

Result: This paper presents BloombergGPT, a large language model designed specifically for the finance industry. The model was trained on a diverse range of financial texts, including news articles, transcripts, and social media posts. The authors found that BloombergGPT outperformed existing models on financial tasks while maintaining strong performance on general language tasks. The paper also describes the annotation process used to create labeled datasets for evaluating the model’s performance on financial tasks. The authors provide detailed statistics on the datasets used for evaluation and describe the various financial tasks that the model was tested on. Overall, the paper demonstrates the effectiveness of domain-specific language models and highlights the potential benefits of using such models in the finance industry.

FinGPT: Open-Source Financial Large Language Models

Hongyang Yang, Xiao-Yang Liu, Christina Dan Wang

Key Topics: Financial Sector Language Models, Open-Source AI, Robo-Advising, Algorithmic Trading, Low-Rank Adaptation

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Result: The paper discusses the challenges of utilizing language models in the financial sector and introduces an open-source large language model, FinGPT, designed for the finance sector. The paper emphasizes the importance of accessible and transparent resources for developing FinLLMs and highlights potential applications such as robo-advising, algorithmic trading, and low-code development. The paper also discusses the use of relative stock price change percentage as output labels and the implementation of Low-Rank Adaptation (LoRA) to reduce the number of trainable parameters in the model.

ChatGPT-based Investment Portfolio Selection

Oleksandr Romanko, Akhilesh Narayan, Roy H. Kwon

Key Topics: Generative AI Models, Investment Portfolio Selection, Trading Universe Generation, Portfolio Optimization Strategies, Financial Performance Analysis

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Result: The paper explores the use of generative AI models, specifically ChatGPT, in the context of investment portfolio selection. The authors demonstrate how ChatGPT can be employed to generate a trading universe of stocks and analyze the performance of portfolios optimized using this AI-generated universe. The study shows that portfolios constructed with the help of ChatGPT can outperform the S&P500 index, providing a promising outlook on the integration of AI in financial decision-making processes. The paper details the methodologies and presents the results through evaluation metrics and the cumulative returns of the constructed portfolios.

Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams

Ethan Callanan, Amarachi Mbakwe, Antony Papadimitriou, Yulong Pei, Mathieu Sibue, Xiaodan Zhu, Zhiqiang Ma, Xiaomo Liu, Sameena Shah

Key Topics: Large Language Models, Financial Reasoning, CFA Exams, Natural Language Processing, AI in Finance

Link: here | AI Score: 🚀 🚀 | Interest Score: 🧲 🧲 | Reading Time: ⏰ ⏰

Result: This paper evaluates the financial reasoning abilities of Large Language Models (LLMs) such as ChatGPT and GPT-4. The study assesses their performance on mock Chartered Financial Analyst (CFA) exams and provides insights into potential strategies and improvements to enhance their applicability in finance. The authors highlight the challenges of benchmarking LLMs on the CFA exam, including the lack of publicly available past exams and the need for human experts to grade plain text responses. To overcome these difficulties, the authors rely on mock CFA exams and focus solely on levels I and II. The study finds that while LLMs show promise in solving industry use cases involving NLP tasks, they do not deliver satisfactory performance in complex scientific reasoning yet to be reliably leveraged in practice. The paper concludes by suggesting future research directions to improve the financial reasoning abilities of LLMs

Narratives from GPT-derived Networks of News, and a Link to Financial Markets Dislocations

Deborah Miori, Constantin Petrov

Key Topics: News Data Analysis, Financial Markets Impact, Advanced Analytical Tools, Investor Sentiment, Crisis Prediction

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Result: The paper discusses the challenges of processing a large volume of news data and its impact on financial markets. It highlights the importance of advanced tools and technologies to analyze news data and extract quantitative signals for understanding its impact on asset prices, trading strategies, and investor sentiment. The authors mention various research studies related to the interdependent relationships between the stock market and economic news, the influence of news on trading behavior, and the correlation between company returns and news. The paper also evaluates the limitations of topic modeling techniques such as Latent Dirichlet Allocation (LDA) and compares different state-of-the-art topic models. Additionally, it presents a methodology for analyzing news narratives using word-clouds and GPT-generated summaries to map major events and early signs of crises.

Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment

Saizhuo Wang, Hang Yuan, Leon Zhou, Lionel M. Ni, Heung-Yeung Shum, Jian Guo

Key Topics: Quantitative Investment, Alpha Mining, Human-AI Interaction, Large Language Models, System Framework

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Result: The paper proposes a new paradigm for alpha mining in quantitative investment research, which enhances human-AI interaction to improve the effectiveness and efficiency of alpha research. The proposed system, Alpha-GPT, incorporates large language models (LLM) as a mediator between quantitative researchers and alpha search. Alpha-GPT has three key advantages: it can interpret users’ trading ideas and translate them into fitting expressions, quickly summarize top-performing alphas in natural language for ease of understanding, and allow the user to suggest modifications to the alpha search which the model will automatically make to future rounds of alpha mining. The paper highlights the limitations of traditional alpha mining methods and how human-AI interaction can overcome them. The authors also provide a detailed description of the Alpha-GPT system framework, which provides a heuristic way to generate creative and effective alphas.

Benchmarking Large Language Model Volatility

Boyang Yu

Key Topics: Large Language Models, Financial Text Analysis, Sentiment Analysis, Model Volatility, Investment Performance

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Result: In this paper, B the impact of non-deterministic outputs from Large Language Models (LLMs) on financial text understanding tasks is explored. The paper highlights the importance of understanding and managing volatility in financial decision-making, and how LLMs can be used to extract valuable insights from textual data, such as news headlines. The study uses two LLMs, Chat Generative Pre-trained Transformers (ChatGPT) and Large Language Model Meta AI (LLaMA), and focuses on sentiment analysis in news headlines related to S&P 500 companies. The paper presents a methodology for measuring lexical and semantic volatility in LLM outputs, and evaluates the impact of increased training data on sentiment analysis and investment performance. The findings suggest that LLMs can provide valuable insights for financial analysis, but their non-deterministic outputs can introduce volatility and uncertainty in decision-making. The paper concludes by highlighting the need for practical methods to assess uncertainty in LLM outputs to enhance the reliability and trustworthiness of model outputs

Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection

Georgios Fatouros, Konstantinos Metaxas, John Soldatos, Dimosthenis Kyriazis

Key Topics: AI in Finance, GPT-4, Investment Recommendations, MarketSenseAI, Corporate Disclosures

Link: here | AI Score: 🚀 🚀 | Interest Score: 🧲 🧲 | Reading Time: ⏰ ⏰

Result: The paper discusses the use of GPT-4 model in financial applications, particularly in summarizing corporate disclosures and providing investment recommendations. The paper outlines the architectural framework of MarketSenseAI, which integrates news, fundamentals, price dynamics, and macroeconomic analysis to generate investment signals. It also emphasizes the value of AI-generated explanations for investment decisions and considers transaction costs and number of trades in real-world settings. The paper highlights the use of GPT-4 in distilling complex financial and news data into actionable insights, and it presents examples of the output generated by the Progressive News Summarizer and Fundamentals Summarizer components.

Multimodal Gen-AI for Fundamental Investment Research

Lezhi Li, Ting-Yu Chang, Hai Wang

Key Topics: Automated Investment Summarization, Fine-tuning Language Models, AI in Finance, Information Summarization, Strategic Investment AI

Link: here | AI Score: 🚀 🚀 | Interest Score: 🧲 🧲 | Reading Time: ⏰ ⏰

Result: The paper outlines an initiative to transform the financial investment industry by automating information summarization and investment idea generation using language models. The study evaluates the effectiveness of fine-tuning methods on a base model to achieve specific application-level goals. The ultimate objective is to develop an AI agent prototype that can liberate human investors from repetitive tasks and allow them to focus on high-level strategic thinking. The paper discusses the diverse corpus dataset used for the experiments and presents the results of statistical and human evaluations, demonstrating the effectiveness of the fine-tuned versions in solving text modeling, summarization, reasoning, and finance domain questions. The code implementation for the project is available on GitHub.