跨市场训练可提升金融指数长期预测准确率
"It Looks All the Same to Me": Cross-index Training for Long-term Financial Series Prediction
- 用一个市场的指数数据训练模型，再用于预测另一市场指数
- 跨市场训练在多数情况下表现优于单市场训练
- 支持有效市场假说，适合量化金融研究者参考
本文研究多种人工神经网络架构（包括经典与较新颖的）在预测全球不同市场指数的长期金融时间序列中的应用。重点考察机器学习算法跨市场训练的可行性：在一个全球市场指数上训练的模型，能否在另一不同市场的指数预测中达到相似甚至更优的准确率？实证结果普遍呈现积极结论，进一步为尤金·法玛提出的长期争议性有效市场假说提供了支持。
原文摘要 · Abstract (English)
We investigate a number of Artificial Neural Network architectures (well-known and more ``exotic'') in application to the long-term financial time-series forecasts of indexes on different global markets. The particular area of interest of this research is to examine the correlation of these indexes' behaviour in terms of Machine Learning algorithms cross-training. Would training an algorithm on an index from one global market produce similar or even better accuracy when such a model is applied for predicting another index from a different market? The demonstrated predominately positive answer to this question is another argument in favour of the long-debated Efficient Market Hypothesis of Eugene Fama.
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