用带噪声增强的自编码器提升加密货币预测，效果显著
Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data
- 用有监督自编码器提取分层特征，结合三重屏障标签
- 适度噪声和适中瓶颈大小使策略收益提升，过度则适得其反
- 适合量化交易、金融时序建模研究者参考
本文研究通过有监督自编码器（SAE）改进神经网络在金融时间序列预测中的表现，以提升投资策略收益。基于夏普比率和信息比率，重点考察了噪声增强与三重屏障标签对风险调整后回报的影响。研究选取比特币、莱特币和以太坊作为交易资产，时间范围为2016年1月1日至2022年4月30日。结果表明，合理平衡噪声增强与瓶颈尺寸的有监督自编码器能显著提升策略有效性；但过度噪声或过大瓶颈尺寸会损害性能。
原文摘要 · Abstract (English)
This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders (SAE), to improve investment strategy performance. Using the Sharpe and Information Ratios, it specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns. The study focuses on Bitcoin, Litecoin, and Ethereum as the traded assets from January 1, 2016, to April 30, 2022. Findings indicate that supervised autoencoders, with balanced noise augmentation and bottleneck size, significantly boost strategy effectiveness. However, excessive noise and large bottleneck sizes can impair performance.
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