TGSLN : Time-aware Graph Structure Learning Network for Multi-variates Stock Sector Ranking Recommendation

Abstract

In the field of financial prediction, most studies focus on individual stocks or stock indices. Stock sectors are collections of stocks with similar characteristics and the indices of sectors have more stable trends and predictability compared to individual stocks. Additionally, stock sectors are subsets of stock indices, which implies that investment portfolios based on stock sectors have a greater potential to achieve excess returns. In this paper, we propose a new method, Time-aware Graph Structure Learning Network (TGSLN), to address the problem of stock sector ranking recommendation. In this model, we use an indicator called Relative Price Strength (RPS) to describe the ranking change trend of the sectors. To construct the inherent connection between sectors, we construct a multi-variable time series that consists of multi-scale RPS sequences and effective indicators filtered through the factor selector. We also build a stock sector relation graph based on authoritative stock sector classifications. Specially, we design a time-aware graph structure learner, which can mine the sector relations from time series, and enhance the initial graph through graph fusion. Our model outperforms state-of-the-art baselines in both A-share and NASDAQ markets.