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Stock investment recommendation is crucial for guiding investment decisions and managing portfolios. Recent studies have demonstrated the potential of temporal-relational models (TRM) to yield excess investment returns. However, in the complicated finance ecosystem, the current TRM suffer from both the intrinsic temporal bias from the low signal-to-noise ratio (SNR) and the relational bias caused by utilizing inappropriate relational topologies and propagation mechanisms. Moreover, the distribution shifts behind macro-market scenarios invalidate the underlying i.i.d. assumption and limit the generalization ability of TRM. In this paper, we pioneer the impact of the above issues on the effective learning of temporal-relational patterns and propose an Automatic De-Biased Temporal-Relational Model (ADB-TRM) for stock recommendation. Specifically, ADB-TRM consists of three main components, i.e., (i) a meta-learned architecture forms a dual-stage training process, with the inner part ameliorating temporal-relational bias and the outer meta-learner counteracting distribution shifts, (ii) automatic adversarial sample generation guides the model adaptively to alleviate bias and enhance its profiling ability through adversarial training, and (iii) global-local interaction helps seek relative invariant stock embeddings from local and global distribution perspectives to mitigate distribution shifts. Experiments on three datasets from distinct stock markets show that ADB-TRM excels state-of-the-arts over 28.41% and 9.53% in terms of cumulative and risk-adjusted returns.
Chen et al. (Fri,) studied this question.