发表机构
SinoPac Holdings（永丰金控）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
DIVINE通过从原始OHLCV数据重建技术指标进行跨市场预训练，以轻量0.05M参数编码器在六个股票市场取得最优平均投资组合表现，证明监督设计与市场多样性比模型规模更重要。
AI 中文摘要
金融时间序列预训练通常从掩码观测、对比关系或未来结果中学习——然而现有目标难以同时避免未来监督的不确定性并保持收益预测的一致性。我们提出DIVINE（DIVERse INdicator rEconstruction，多样指标重建），一个简单的跨市场预训练框架，从原始OHLCV历史中重建技术指标。技术指标由观测到的价格-成交量历史计算得出，提供跨市场一致定义的监督，同时总结多样的市场动态，并具有与收益预测相关的既定有效性。DIVINE在六个股票市场数据集上联合预训练，重建源自16个标准指标的77个目标，仅将学习到的编码器迁移到下游股票排序。在所有六个市场中，DIVINE以轻量级0.05M参数编码器实现了最强的平均投资组合表现，超越了预训练基线，并匹配或超过规模大得多的金融基础模型，同时保持稳健性和数据效率。系统分析表明，指标多样性和市场多样性在迁移中提供互补增益。综合来看，这些结果表明监督设计和跨市场多样性——而非模型规模——是强大且可迁移的金融表征的关键驱动因素。
英文摘要
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.
