DIVINE: Simple Cross-Market Stock Pretraining via Diverse Indicator Reconstruction

2026-10-02

AI Summary

This study addresses the challenges of noisy supervision signals and misalignment with return prediction in financial time series pre-training by proposing a cross-market pre-training framework. Methodologically, technical indicators are leveraged as stable supervision sources to reconstruct OHLCV data, while joint multi-market pre-training is employed to learn generalizable representations. Subsequently, only a lightweight Transformer encoder is transferred to downstream stock ranking tasks. The findings reveal that supervision signal design and market diversity are more critical than model scale. Remarkably, with merely 0.05M parameters, the proposed framework achieves superior average portfolio performance across six major markets, rivaling large-scale foundation models while maintaining both high efficiency and robustness.

Abstract

Financial time-series pretraining typically learns from masked observations, contrastive relations, or future outcomes---yet existing objectives struggle to simultaneously avoid future-supervision uncertainty and maintain return-prediction alignment. We propose DIVINE (DIVerse INdicator rEconstruction), a simple cross-market pretraining framework that reconstructs technical indicators from raw OHLCV history. Computed from observed price-volume history, technical indicators provide consistently defined supervision across markets while summarizing diverse market dynamics with established relevance to return prediction. Pretrained jointly on six-equity market datasets, DIVINE reconstructs 77 targets derived from 16 standard indicators and transfers only the learned encoder to downstream stock ranking. Across all six markets, DIVINE achieves the strongest average portfolio performance with a lightweight 0.05M-parameter encoder, outperforming pretraining baselines and matching or exceeding substantially larger financial foundation models, while remaining robust and data-efficient. Systematic analyses show that indicator diversity and market diversity provide complementary gains in transfer. Together, these results suggest that supervision design and cross-market diversity---rather than model scale---are the key drivers of strong, transferable financial representations.

Problem

• financial time-series pretraining
• cross-market stock prediction
• technical indicators
• representation learning

Innovation

• Cross-Market Pretraining
• Indicator Reconstruction
• Financial Time-Series
• Self-Supervised Learning
• Stock Ranking