LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
- URL: http://arxiv.org/abs/2406.10811v1
- Date: Sun, 16 Jun 2024 06:20:50 GMT
- Title: LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
- Authors: Meiyun Wang, Kiyoshi Izumi, Hiroki Sakaji,
- Abstract summary: We introduce a novel framework called LLMFactor to identify factors that influence stock movements.
Unlike previous methods that relied on keyphrases or sentiment analysis, this approach focuses on extracting factors more directly related to stock market dynamics.
Our framework directs the LLMs to create background knowledge through a fill-in-the-blank strategy and then discerns potential factors affecting stock prices from related news.
- Score: 5.519288891583653
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, Large Language Models (LLMs) have attracted significant attention for their exceptional performance across a broad range of tasks, particularly in text analysis. However, the finance sector presents a distinct challenge due to its dependence on time-series data for complex forecasting tasks. In this study, we introduce a novel framework called LLMFactor, which employs Sequential Knowledge-Guided Prompting (SKGP) to identify factors that influence stock movements using LLMs. Unlike previous methods that relied on keyphrases or sentiment analysis, this approach focuses on extracting factors more directly related to stock market dynamics, providing clear explanations for complex temporal changes. Our framework directs the LLMs to create background knowledge through a fill-in-the-blank strategy and then discerns potential factors affecting stock prices from related news. Guided by background knowledge and identified factors, we leverage historical stock prices in textual format to predict stock movement. An extensive evaluation of the LLMFactor framework across four benchmark datasets from both the U.S. and Chinese stock markets demonstrates its superiority over existing state-of-the-art methods and its effectiveness in financial time-series forecasting.
Related papers
- Evaluating Interventional Reasoning Capabilities of Large Language Models [58.52919374786108]
Large language models (LLMs) can estimate causal effects under interventions on different parts of a system.
We conduct empirical analyses to evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention.
We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types, and enable a study of intervention-based reasoning.
arXiv Detail & Related papers (2024-04-08T14:15:56Z) - AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data.
We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
arXiv Detail & Related papers (2024-03-19T09:45:33Z) - FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications [2.2661367844871854]
Large Language Models (LLMs) can be used in this context, but they are not finance-specific and tend to require significant computational resources.
We introduce a novel approach based on the Llama 2 7B foundational model, in order to benefit from its generative nature and comprehensive language manipulation.
This is achieved by fine-tuning the Llama2 7B model on a small portion of supervised financial sentiment analysis data.
arXiv Detail & Related papers (2024-03-18T22:11:00Z) - Learning to Generate Explainable Stock Predictions using Self-Reflective
Large Language Models [54.21695754082441]
We propose a framework to teach Large Language Models (LLMs) to generate explainable stock predictions.
A reflective agent learns how to explain past stock movements through self-reasoning, while the PPO trainer trains the model to generate the most likely explanations.
Our framework can outperform both traditional deep-learning and LLM methods in prediction accuracy and Matthews correlation coefficient.
arXiv Detail & Related papers (2024-02-06T03:18:58Z) - Chain of History: Learning and Forecasting with LLMs for Temporal
Knowledge Graph Completion [24.545917737620197]
Temporal Knowledge Graph Completion (TKGC) is a complex task involving the prediction of missing event links at future timestamps.
This paper aims to provide a comprehensive perspective on harnessing the advantages of Large Language Models for reasoning in temporal knowledge graphs.
arXiv Detail & Related papers (2024-01-11T17:42:47Z) - Survey on Factuality in Large Language Models: Knowledge, Retrieval and
Domain-Specificity [61.54815512469125]
This survey addresses the crucial issue of factuality in Large Language Models (LLMs)
As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital.
arXiv Detail & Related papers (2023-10-11T14:18:03Z) - Enhancing Financial Sentiment Analysis via Retrieval Augmented Large
Language Models [11.154814189699735]
Large Language Models (LLMs) pre-trained on extensive corpora have demonstrated superior performance across various NLP tasks.
We introduce a retrieval-augmented LLMs framework for financial sentiment analysis.
Our approach achieves 15% to 48% performance gain in accuracy and F1 score.
arXiv Detail & Related papers (2023-10-06T05:40:23Z) - Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements? [13.682396634686159]
This paper introduces a standardized experimental procedure for comprehensive evaluations of Large Language Models.
We detail the methodology using three distinct LLMs, each embodying a unique approach to performance enhancement.
We develop quantitative trading strategies using these sentiment factors and conduct back-tests in realistic scenarios.
arXiv Detail & Related papers (2023-06-25T12:08:44Z) - Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting [7.485041391778341]
We focus on NASDAQ-100 stocks, making use of publicly accessible historical stock price data, company metadata, and historical economic/financial news.
We show that a publicly available LLM such as Open-LLaMA, after fine-tuning, can comprehend the instruction to generate explainable forecasts.
arXiv Detail & Related papers (2023-06-19T15:42:02Z) - Sentiment Analysis in the Era of Large Language Models: A Reality Check [69.97942065617664]
This paper investigates the capabilities of large language models (LLMs) in performing various sentiment analysis tasks.
We evaluate performance across 13 tasks on 26 datasets and compare the results against small language models (SLMs) trained on domain-specific datasets.
arXiv Detail & Related papers (2023-05-24T10:45:25Z) - Factor Investing with a Deep Multi-Factor Model [123.52358449455231]
We develop a novel deep multi-factor model that adopts industry neutralization and market neutralization modules with clear financial insights.
Tests on real-world stock market data demonstrate the effectiveness of our deep multi-factor model.
arXiv Detail & Related papers (2022-10-22T14:47:11Z)
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.