Text became a mainstream data source for quant research in three waves: dictionary-based sentiment scoring of news and filings, supervised NLP on earnings calls and social media, and — since 2023 — large language models used for everything from extracting signals to simulating analysts. This hub tracks all three, with the LLM wave now dominating new submissions.
The evaluation bar shifts with each wave, and it’s worth knowing where it currently stands: for LLM-based trading papers, the critical flaw to check is temporal leakage — a model whose training data includes the test period’s news has seen the future, and many published backtests fail exactly this test. The papers ranked highest below either use point-in-time text data with pre-cutoff models or study tasks (extraction, classification, summarization quality) where leakage doesn’t invalidate the result. Sentiment-signal decay is the other recurring theme: documented text signals fade fast once published.
Related hubs: Machine Learning for Forecasting, Crypto Markets & DeFi, Factor Investing.
Large Language Models (LLMs) research in the financial domain is particularly complex due to the sheer number of approaches proposed in literature. Retrieval-Augmented Generation (RAG) has emerged as one of the leading methods in the sector due to its inherent groundedness and data source variabilit
Domain-specific quantitative reasoning remains a major challenge for large language models (LLMs), especially in fields requiring expert knowledge and complex question answering (QA). In this work, we propose Expert Question Decomposition (EQD), an approach designed to balance the use of domain know
The success of OpenAI’s ChatGPT in 2023 has spurred financial enterprises into exploring Generative AI applications to reduce costs or drive revenue within different lines of businesses in the Financial Industry. While these applications offer strong potential for efficiencies, they introduce new mo
Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market. In recent years, knowledge-enhanced stock price prediction methods have shown groundbreaking results by utilizing external knowledge to understand the stock marke
Relative to quantitative methods traditionally used in accounting and finance, textual analysis is substantially less precise. Thus, understanding the art is of
This paper explores the application of Natural Language Processing (NLP) in financial risk detection. By constructing an NLP-based financial risk detection model, this study aims to identify and predict potential risks in financial documents and communications. First, the fundamental concepts of NLP
Quantitative investment (quant) is an emerging, technology-driven approach in asset management, increasingy shaped by advancements in artificial intelligence. Recent advances in deep learning and large language models (LLMs) for quant finance have improved predictive modeling and enabled agent-based
This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading. By filtering a dataset of 136 million research papers, we identified 14,342 relevant articles published between 1956 and Q1 2020. We compare traditional practices-such as keyword-ba
Interestingly the numbers of young traders in Jakarta Stock Exchange had been increasing in recent years. Even in the middle of the global crisis caused by covid19 pandemic, in December 2021 according to KSEI, Individual investors were dominated by young investors. Data presented by KSEI showed that
Explainability in AI and ML models is critical for fostering trust, ensuring accountability, and enabling informed decision making in high stakes domains. Yet this objective is often unmet in practice. This paper proposes a general purpose framework that bridges state of the art explainability techn
The outlook for the future of artificial intelligence (AI) in the financial sector, especially in financial forecasting, the challenges and implications. The dynamics of AI technology, including deep learning, reinforcement learning, and integration with blockchAIn and the Internet of Things, also h
The paper focuses on the conceptual process of using LLMs to generate academic papers, rather than presenting complex mathematical models or empirical backtesting results.
Big data revolutionizes accounting and auditing, offering deep insights but also introducing challenges like data privacy and security. With data from IoT, social media, and transactions, traditional practices are evolving. Professionals must adapt to these changes, utilizing AI and machine learning
With the investment landscape becoming more competitive, efficiently scaling deal sourcing and improving deal insights have become a dominant strategy for funds. While funds are already spending significant efforts on these two tasks, they cannot be scaled with traditional approaches; hence, there i
The financial sector is entering a new era of rapidly advancing data analytics as deep learning models are adopted into its technology stack. A subset of Artifi
Artificial intelligence (AI), from time to time called machine intelligence is simulation of human intelligence in machines. It is the intellect exhibited by ma
Relative to quantitative methods traditionally used in accounting and finance, textual analysis is substantially less precise. Thus, understanding the art is of
Recent advances in data science, machine learning, and artificial intelligence, such as the emergence of large language models, are leading to an increasing demand for data that can be processed by such models. While data sources are application-specific, and it is impossible to produce an exhaustiv
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
The paper is a conceptual case study discussing potential applications of AI in academic research, lacking any mathematical formulas, derivations, or formal models. It also contains no backtesting, datasets, statistical metrics, or implementation details, focusing instead on high-level benefits and
📬 The Quant Space Weekly
One email a week: the most interesting quant finance papers, scored and summarized. No spam, unsubscribe anytime.