Machine Learning and the Stock Market
研究机器学习算法能否从历史价格中找出盈利的技术交易规则，发现样本外盈利性随时间下降，表明市场效率提高；进化遗传算法因不回避错误预测而优于严格最小化损失的算法。
Abstract Practitioners allocate substantial resources to technical analysis whereas academic theories of market efficiency rule out technical trading profitability. We study this long-standing puzzle by applying a diverse set of machine learning algorithms. The results show that an investor can find profitable technical trading rules using past prices, and that this out-of-sample profitability decreases through time, showing that markets have become more efficient over time. In addition, we find that the evolutionary genetic algorithm’s attitude in not shying away from erroneous predictions gives it an edge in building profitable strategies compared to the strict loss-minimization-focused machine learning algorithms.