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英文标题：
《Deep Learning Stock Volatility with Google Domestic Trends》
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作者：
Ruoxuan Xiong, Eric P. Nichols and Yuan Shen
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最新提交年份：
2016
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英文摘要：
We have applied a Long Short-Term Memory neural network to model S&P 500 volatility, incorporating Google domestic trends as indicators of the public mood and macroeconomic factors. In a held-out test set, our Long Short-Term Memory model gives a mean absolute percentage error of 24.2%, outperforming linear Ridge/Lasso and autoregressive GARCH benchmarks by at least 31%. This evaluation is based on an optimal observation and normalization scheme which maximizes the mutual information between domestic trends and daily volatility in the training set. Our preliminary investigation shows strong promise for better predicting stock behavior via deep learning and neural network models.
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中文摘要：
我们应用了一个长-短记忆神经网络来模拟标准普尔500指数的波动性，将谷歌国内趋势作为公众情绪和宏观经济因素的指标。在一个长期测试集中，我们的长短期记忆模型给出了24.2%的平均绝对百分比误差，比线性岭/套索和自回归GARCH基准测试至少高出31%。该评估基于最佳观察和规范化方案，该方案最大限度地利用了国内趋势和训练集中每日波动之间的互信息。我们的初步调查显示，通过深度学习和神经网络模型可以更好地预测股票行为。
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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