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交叉提交 (展示 1 之 1 条目 )

[1] arXiv:2507.07296 (交叉列表自 q-fin.GN) [中文pdf, pdf, html, 其他]

标题： 时间序列基础模型用于多变量金融时间序列预测 显示英文标题

标题： Time Series Foundation Models for Multivariate Financial Time Series Forecasting

Ben A. Marconi

评论： 66页

主题： 一般金融 (q-fin.GN) ; 机器学习 (cs.LG)

金融时间序列预测由于复杂的非线性关系、时间依赖性、变量相互依赖性和数据可用性有限而面临重大挑战，特别是在涉及低频数据、新上市工具或新兴市场资产的任务中。时间序列基础模型（TSFMs）通过在多样化的时间序列语料库上进行预训练，随后进行特定任务的适应，提供了一个有希望的解决方案。本研究评估了两种TSFMs（Tiny Time Mixers（TTM）和Chronos）在三个金融预测任务中的表现：美国10年期国债收益率变化、EUR/USD波动率和股票价差预测。结果表明，TTM表现出强大的可迁移性。当对TTM的预训练版本和具有相同架构的未训练模型进行微调时，在有限数据上微调的预训练版本性能提高了25-50%，即使在更长的数据集上微调也实现了15-30%的提升。值得注意的是，TTM的零样本性能在波动率预测和股票价差预测中超过了简单的基准，在后者中表明TSFMs可以在不进行微调的情况下超越传统基准模型。预训练模型在达到与未训练模型相当的性能水平时，所需的数据年数减少了3-10年，展示了显著的样本效率提升。然而，尽管TTM优于简单的基线，传统专业模型在三项任务中的两项中达到了或超过了其性能，这表明TSFMs更注重广度而非任务特定优化。这些发现表明，尽管TSFMs仍处于初级阶段，但在噪声大、数据受限的任务中，它们为金融预测提供了巨大的潜力，但要实现具有竞争力的性能，可能需要针对金融时间序列特征进行领域特定的预训练和架构改进。 显示英文摘要

Financial time series forecasting presents significant challenges due to complex nonlinear relationships, temporal dependencies, variable interdependencies and limited data availability, particularly for tasks involving low-frequency data, newly listed instruments, or emerging market assets. Time Series Foundation Models (TSFMs) offer a promising solution through pretraining on diverse time series corpora followed by task-specific adaptation. This study evaluates two TSFMs (Tiny Time Mixers (TTM) and Chronos) across three financial forecasting tasks: US 10-year Treasury yield changes, EUR/USD volatility, and equity spread prediction. Results demonstrate that TTM exhibits strong transferability. When fine-tuning both the pretrained version of TTM and an untrained model with the same architecture, the pretrained version achieved 25-50% better performance when fine-tuned on limited data and 15-30% improvements even when fine-tuned on lengthier datasets. Notably, TTM's zero-shot performance outperformed naive benchmarks in volatility forecasting and equity spread prediction, with the latter demonstrating that TSFMs can surpass traditional benchmark models without fine-tuning. The pretrained model consistently required 3-10 fewer years of data to achieve comparable performance levels compared to the untrained model, demonstrating significant sample-efficiency gains. However, while TTM outperformed naive baselines, traditional specialised models matched or exceeded its performance in two of three tasks, suggesting TSFMs prioritise breadth over task-specific optimisation. These findings indicate that TSFMs, though still nascent, offer substantial promise for financial forecasting-particularly in noisy, data-constrained tasks-but achieving competitive performance likely requires domain-specific pretraining and architectural refinements tailored to financial time series characteristics.

替换提交 (展示 1 之 1 条目 )

[2] arXiv:2506.21775 (替换) [中文pdf, pdf, html, 其他]

标题： 被动投资的隐性成本 显示英文标题

标题： On the hidden costs of passive investing

Iro Tasitsiomi

评论： v1 修正了一些拼写错误，添加了更多解释，并且摘要更加详尽。没有对结果或结论进行修改。

主题： 交易与市场微观结构 (q-fin.TR) ; 理论经济学 (econ.TH) ; 一般金融 (q-fin.GN) ; 数学金融 (q-fin.MF)

被动投资由于其低费用以及专注于零跟踪误差的感知简单性，而非证券选择，已经获得了极大的流行度。 然而，我们的分析显示，被动（零跟踪误差）方法是在指数再构成当天市场收盘时购买股票（该股票几天前已被宣布为即将添加的股票），与那些提前逐步购入少量所需股份并产生最小额外跟踪误差的策略相比，会导致数百个基点的成本。 此外，我们表明，在所有分析的情景下，一个在公告后建立少量库存并在再构成事件中提供流动性的交易者可以持续获得数百个基点的利润，通常更多，假设风险极小。 显示英文摘要

Passive investing has gained immense popularity due to its low fees and the perceived simplicity of focusing on zero tracking error, rather than security selection. However, our analysis shows that the passive (zero tracking error) approach of waiting until the market close on the day of index reconstitution to purchase a stock (that was announced days earlier as an upcoming addition) results in costs amounting to hundreds of basis points compared to strategies that involve gradually acquiring a small portion of the required shares in advance with minimal additional tracking errors. In addition, we show that under all scenarios analyzed, a trader who builds a small inventory post-announcement and provides liquidity at the reconstitution event can consistently earn several hundreds of basis points in profit and often much more, assuming minimal risk.