Modal-adaptive Knowledge-enhanced Graph-based Financial Prediction from Monetary Policy Conference Calls with LLM
arxiv(2024)
摘要
Financial prediction from Monetary Policy Conference (MPC) calls is a new yet
challenging task, which targets at predicting the price movement and volatility
for specific financial assets by analyzing multimodal information including
text, video, and audio. Although the existing work has achieved great success
using cross-modal transformer blocks, it overlooks the potential external
financial knowledge, the varying contributions of different modalities to
financial prediction, as well as the innate relations among different financial
assets. To tackle these limitations, we propose a novel Modal-Adaptive
kNowledge-enhAnced Graph-basEd financial pRediction scheme, named MANAGER.
Specifically, MANAGER resorts to FinDKG to obtain the external related
knowledge for the input text. Meanwhile, MANAGER adopts BEiT-3 and Hidden-unit
BERT (HuBERT) to extract the video and audio features, respectively.
Thereafter, MANAGER introduces a novel knowledge-enhanced cross-modal graph
that fully characterizes the semantic relations among text, external knowledge,
video and audio, to adaptively utilize the information in different modalities,
with ChatGLM2 as the backbone. Extensive experiments on a publicly available
dataset Monopoly verify the superiority of our model over cutting-edge methods.
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