ChatGPT

ChatGPT in Systematic Investing – Enhancing Risk-Adjusted Returns with LLMs ArXiv ID: 2510.26228 “View on arXiv” Authors: Nikolas Anic, Andrea Barbon, Ralf Seiz, Carlo Zarattini Abstract This paper investigates whether large language models (LLMs) can improve cross-sectional momentum strategies by extracting predictive signals from firm-specific news. We combine daily U.S. equity returns for S&P 500 constituents with high-frequency news data and use prompt-engineered queries to ChatGPT that inform the model when a stock is about to enter a momentum portfolio. The LLM evaluates whether recent news supports a continuation of past returns, producing scores that condition both stock selection and portfolio weights. An LLM-enhanced momentum strategy outperforms a standard long-only momentum benchmark, delivering higher Sharpe and Sortino ratios both in-sample and in a truly out-of-sample period after the model’s pre-training cut-off. These gains are robust to transaction costs, prompt design, and portfolio constraints, and are strongest for concentrated, high-conviction portfolios. The results suggest that LLMs can serve as effective real-time interpreters of financial news, adding incremental value to established factor-based investment strategies. ...

Unleashing the power of text for credit default prediction: Comparing human-written and generative AI-refined texts ArXiv ID: 2503.18029 “View on arXiv” Authors: Unknown Abstract This study explores the integration of a representative large language model, ChatGPT, into lending decision-making with a focus on credit default prediction. Specifically, we use ChatGPT to analyse and interpret loan assessments written by loan officers and generate refined versions of these texts. Our comparative analysis reveals significant differences between generative artificial intelligence (AI)-refined and human-written texts in terms of text length, semantic similarity, and linguistic representations. Using deep learning techniques, we show that incorporating unstructured text data, particularly ChatGPT-refined texts, alongside conventional structured data significantly enhances credit default predictions. Furthermore, we demonstrate how the contents of both human-written and ChatGPT-refined assessments contribute to the models’ prediction and show that the effect of essential words is highly context-dependent. Moreover, we find that ChatGPT’s analysis of borrower delinquency contributes the most to improving predictive accuracy. We also evaluate the business impact of the models based on human-written and ChatGPT-refined texts, and find that, in most cases, the latter yields higher profitability than the former. This study provides valuable insights into the transformative potential of generative AI in financial services. ...

Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps? ArXiv ID: 2401.05447 “View on arXiv” Authors: Unknown Abstract We used a dataset of daily Bloomberg Financial Market Summaries from 2010 to 2023, reposted on large financial media, to determine how global news headlines may affect stock market movements using ChatGPT and a two-stage prompt approach. We document a statistically significant positive correlation between the sentiment score and future equity market returns over short to medium term, which reverts to a negative correlation over longer horizons. Validation of this correlation pattern across multiple equity markets indicates its robustness across equity regions and resilience to non-linearity, evidenced by comparison of Pearson and Spearman correlations. Finally, we provide an estimate of the optimal horizon that strikes a balance between reactivity to new information and correlation. ...

ChatGPT: Unlocking the Future of NLP inFinance ArXiv ID: ssrn-4323643 “View on arXiv” Authors: Unknown Abstract This paper reviews the current state of ChatGPT technology in finance and its potential to improve existing NLP-based financial applications. We discuss the eth Keywords: ChatGPT, Natural Language Processing (NLP), Financial Technology (FinTech), Machine Learning, Ethics in AI, General Finance Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: This paper is a literature review discussing the capabilities and applications of ChatGPT in finance, featuring no mathematical derivations, formulas, or empirical backtesting. It focuses on conceptual discussion, ethical considerations, and future research directions, resulting in low scores for both math complexity and empirical rigor. flowchart TD A["Research Goal:<br/>Evaluate ChatGPT in Finance NLP"] --> B["Key Inputs:<br/>Financial Texts, NLP Benchmarks"] B --> C["Methodology:<br/>Review, Compare, Analyze Ethics"] C --> D{"Computational Process"} D --> E["Application:<br/>Sentiment/Forecasting Models"] D --> F["Constraint:<br/>Hallucinations/Data Privacy"] E & F --> G["Outcomes:<br/>Enhanced NLP Capabilities"] G --> H["Outcomes:<br/>Ethical & Bias Considerations"]