Stock prediction is a challenging task due to the uncertainty of stock markets. Despite the success of previous works, most of them rely on the assumption that stock data follow the identically identical distribution while the existence of multiple trading patterns in stock market violates it, ignoring multiple patterns in stock market will inevitably lead to the performance decline, and the lack of pattern prior knowledge further hinders the learning of patterns. In this paper, we propose a novel training process Pattern Adaptive Training based on Optimal Transport (OT) to train a set of predictors specializing in diverse patterns while without any prior pattern knowledge and inconsistent assumption. Based on this process, we further mine the potential fitness rank among specialists and design the Pattern Adaptive Specialist Network (PASN) with proposed ranking based selector to choose appropriate specialist predictor for samples. Extensive experimental results show that our method achieves best IC and other metrics on real-world stock datasets.

Graph Neural Networks (GNNs) have drawn attention due to their excellent performance in fraud detection tasks, which reveal fraudsters by aggregating the features of their neighbors. However, some fraudsters typically tend to alleviate their suspiciousness by connecting with many benign ones. Besides, label-imbalanced neighborhood also deteriorates fraud detection accuracy. Such behaviors violate the homophily assumption and worsen the performance of GNN-based fraud detectors. In this paper, we propose a Dual-Augment Graph Neural Network (DAGNN) for fraud detection tasks. In DAGNN, we design a two-pathway framework including disparity augment (DA) pathway and similarity augment (SA) pathway. Accordingly, we devise two novel information aggregation strategies. One is to augment the disparity between target node and its heterogenous neighbors in original topology. The other is to augment its similarity to homogenous neighbors in a relatively label-balanced neighborhood. The experimental results compared with the state-of-the-art models on two real-world datasets demonstrate the superiority of the proposed DAGNN.

The stock market investors aim at maximizing their investment returns. Stock recommendation task is to recommend stocks with higher return ratios for the investors. Most stock prediction methods study the historical sequence patterns to predict stock trend or price in the near future. In fact, the future price of a stock is correlated not only with its historical price, but also with other stocks. In this article, we take into account the relationships between stocks (corporations) by stock relation graph. Furthermore, we propose a Time-aware Relational Attention Network (TRAN) for graph-based stock recommendation according to return ratio ranking. In TRAN, the time-aware relational attention mechanism is designed to capture time-varying correlation strengths between stocks by the interaction of historical sequences and stock description documents. With the dynamic strengths, the nodes of the stock relation graph aggregate the features of neighbor stock nodes by graph convolution operation. For a given group of stocks, the proposed TRAN model can output the ranking results of stocks according to their return ratios. The experimental results on several real-world datasets demonstrate the effectiveness of our TRAN for stock recommendation.

Recommending stock with the highest return ratio is always a challenging problem in the field of financial technology. In this paper, we propose a time-aware graph relational attention network (TRAN) for stock recommendation based on return ratio ranking. In TRAN, time-aware relational attention mechanism is the key unit to capture time-varying correlation strength between stocks by the interaction of historical sequences and stock description documents. With the dynamic strength, the nodes of the stock relation graph aggregate the features of neighbor stock nodes by graph convolution operation. For a given group of stocks, our model can output the ranking results of stocks according to their return ratios. The experimental results on several real-world datasets demonstrate the effectiveness of our TRAN for stock recommendation.