LLMF actor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction (2024)

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.

## introduction artificial intelligence (ai) has become a staple in the financial sector, addressing various challenges such as predicting stock movements @xcite , providing roboadvisory services @xcite , and managing risks @xcite . among these tasks, forecasting stock trends is particularly significant, as it leverages historical data to shape trading strategies and pinpoint opportunities for buying or selling stocks. the efficient market hypothesis (emh), proposed by eugene fama @xcite , suggests that stock prices reflect all available information, making it difficult to predict future price movements. however, subsequent research has identified limitations to market efficiency, highlighting how phenomena such as information asymmetry @xcite and irrational behaviours @xcite can induce departures from perfect efficiency. these observations have paved the way for researchers to seek excess market returns by identifying and leveraging market inefficiencies. ## llmfactor the proposed llmfactor is a comprehensive framework to predict and explain stock market trends as shown in figure 2 . ## experiments by conducting experiments on benchmark datasets, we aim to address the following research question: does llmfactor offer improved prediction accuracy and more insightful explanations for stock market movements? statistics of all datasets are summarized in ## conclusion in this study, we introduce llmfactor, an innovative framework designed to predict and elucidate stock market trends accurately. at the core of llmfactor is the sequential knowledge-guided prompting (skgp) strategy, which integrates background knowledge, stock-related factors, and temporal data to forecast stock movements. through rigorous testing on four benchmark datasets, llm-factor has proven its superiority over sotas that rely on keyphrases, sentiment analysis, and multimodal data inputs. the application of factor analysis highlights llmfactor's novelty and effectiveness, establishing it as a powerful tool for financial analysis. this research represents a significant advancement in leveraging llms for transparent and explainable financial forecasting.