  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.
