This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders, aiming to improve investment strategy performance. It specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns, using the Sharpe and Information Ratios. The study focuses on the S&P 500 index, EUR/USD, and BTC/USD as the traded assets from January 1, 2010, 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, highlighting the importance of precise parameter tuning. This paper also presents a derivation of a novel optimization metric that can be used with triple barrier labeling. The results of this study have substantial policy implications, suggesting that financial institutions and regulators could leverage techniques presented to enhance market stability and investor protection, while also encouraging more informed and strategic investment approaches in various financial sectors.

翻译：本文研究通过监督式自编码器增强神经网络在金融时间序列预测中的表现，旨在提升投资策略绩效。研究重点考察噪声增强与三重障碍标记对风险调整后收益的影响，并以夏普比率和信息比率作为评估指标。研究对象为2010年1月1日至2022年4月30日期间的标普500指数、欧元/美元及比特币/美元交易资产。研究结果表明，采用均衡噪声增强与适当瓶颈尺寸的监督式自编码器能显著提升策略有效性。然而，过度噪声与过大的瓶颈尺寸会损害模型性能，这凸显了参数精确调优的重要性。本文还推导出一种可与三重障碍标记结合使用的新型优化度量方法。本研究结果具有重要的政策启示，表明金融机构与监管机构可运用相关技术以增强市场稳定性与投资者保护，同时推动各金融领域采用更具前瞻性与战略性的投资方法。