HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information

TL;DR

HIST is a graph-based framework that dynamically mines shared information from both predefined and hidden stock concepts to enhance trend forecasting accuracy and investment returns.

Contribution

It introduces a novel dynamic mining approach for concept-oriented shared information, including hidden concepts, improving stock trend prediction.

Findings

Outperforms baseline methods in forecasting accuracy

Achieves higher investment returns in simulations

Demonstrates efficiency on real-world stock data

Abstract

Stock trend forecasting, which forecasts stock prices' future trends, plays an essential role in investment. The stocks in a market can share information so that their stock prices are highly correlated. Several methods were recently proposed to mine the shared information through stock concepts (e.g., technology, Internet Retail) extracted from the Web to improve the forecasting results. However, previous work assumes the connections between stocks and concepts are stationary, and neglects the dynamic relevance between stocks and concepts, limiting the forecasting results. Moreover, existing methods overlook the invaluable shared information carried by hidden concepts, which measure stocks' commonness beyond the manually defined stock concepts. To overcome the shortcomings of previous work, we proposed a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts. The proposed framework simultaneously utilize the stock's shared information and individual information to improve the stock trend forecasting performance. Experimental results on the real-world tasks demonstrate the efficiency of our framework on stock trend forecasting. The investment simulation shows that our framework can achieve a higher investment return than the baselines.