A Static-Dynamic Hypergraph Neural Network Framework Based on Residual Learning for Stock Recommendation

Table 1

Ranking performance of different methods on the China’s A-share dataset when considering different numbers of N.

Model	CSI 100						CSI 300
	IC (↑)	Rank IC (↑)	Precision@N (↑)				IC (↑)	Rank IC (↑)	Precision@N (↑)
			Top3	Top5	Top10	Top30			Top3	Top5	Top10	Top30
MLP	0.071	0.067	56.53	56.17	55.49	53.55	0.082	0.079	57.21	57.10	56.75	55.56
SFM	0.081	0.074	57.79	56.96	55.92	53.88	0.102	0.096	59.84	58.28	57.89	56.82
GRU	0.103	0.097	59.97	58.99	58.37	55.09	0.113	0.108	59.95	59.28	58.59	57.43
LSTM	0.097	0.091	60.12	59.49	59.04	54.77	0.104	0.098	59.51	59.27	58.40	56.98
ALSTM	0.102	0.097	60.79	59.76	58.13	55.00	0.115	0.109	59.51	59.33	58.92	57.47
Transformer	0.089	0.090	59.62	59.20	57.94	54.80	0.106	0.104	60.76	60.06	59.48	57.71
ALSTM + TRA	0.107	0.102	60.27	59.09	57.66	55.16	0.119	0.112	60.45	59.52	59.16	58.24
GATs	0.096	0.090	59.17	58.71	57.48	54.59	0.111	0.105	60.49	59.96	59.02	57.41
HIST	0.120	0.115	61.87	60.82	59.38	56.04	0.131	0.126	61.60	61.08	60.51	58.79
SD-RL	0.124	0.118	62.86	62.07	60.05	56.28	0.131	0.125	62.39	61.70	60.84	58.83

The best result in terms of each metric is indicated in bold.