用LSTM预测行业ETF涨跌方向，提升投资组合收益
Advanced LSTM Neural Networks for Predicting Directional Changes in Sector-Specific ETFs Using Machine Learning Techniques
- 基于LSTM模型分析9大行业2200多只股票的走势
- 平均R²达0.8651，最高达0.942（VNQ ETF）
- 适合量化交易者与长期投资者参考
对冲基金和散户投资者普遍追求盈利，而分散投资是实现这一目标的关键。本研究评估了长短期记忆网络（LSTM）在九个不同行业中的应用效果，基于先锋集团（Vanguard）的行业型ETF，覆盖超过2200只股票。结果显示，所有行业的平均R²值为0.8651，其中VNQ ETF的R²高达0.942。这些结果表明，LSTM模型能够有效预测各行业板块的方向性变化，为实现多元化投资并优化投资组合回报提供了可行的技术支持。
原文摘要 · Abstract (English)
Trading and investing in stocks for some is their full-time career, while for others, it's simply a supplementary income stream. Universal among all investors is the desire to turn a profit. The key to achieving this goal is diversification. Spreading investments across sectors is critical to profitability and maximizing returns. This study aims to gauge the viability of machine learning methods in practicing the principle of diversification to maximize portfolio returns. To test this, the study evaluates the Long-Short Term Memory (LSTM) model across nine different sectors and over 2,200 stocks using Vanguard's sector-based ETFs. The R-squared value across all sectors showed promising results, with an average of 0.8651 and a high of 0.942 for the VNQ ETF. These findings suggest that the LSTM model is a capable and viable model for accurately predicting directional changes across various industry sectors, helping investors diversify and grow their portfolios.
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