StockAgent: A Multi-Agent Collaborative Framework for Financial Time Series Prediction

Financial time series prediction is crucial for unraveling the trends of financial indicators and aiding decision making. However, the main challenge is forecasting future trends from noise and non-linear historical data with low generability. Most existing methods involve model optimization for accuracy improvement but ignore the generability among multiple sequences, thereby limiting the forecast of high-dynamic time series. In this paper, we propose StockAgent, a multi-agent collaborative framework, to adaptively learn the multiscale features from time series for stock price prediction. Specifically, StockAgent consists of a set of expert agents to perform parallel analyses of long- and short-term trends, periodic patterns, and risks. A summarizing manager agent is responsible for aggregating the analysis results and generating predictions, while a reflective manager agent performs optimization and adaptive weight adjustment of the expert agents based on experience feedback. We qualitatively and quantitatively evaluated the StockAgent on two datasets. Extensive experiments demonstrate that StockAgent is comparative to deep-learning based solutions in terms of prediction errors and exhibits outstanding zero-shot abilities.