摘要翻译：
对于行人观察者来说，金融市场看起来完全随机，行为不稳定且不可控制。在很大程度上，这是正确的。在一次近似下，实际价格变化与随机游走模型之间的差异太小，无法用传统的时间序列分析来检测。然而，我们在下文中表明，真实金融时间序列和随机游动之间的这种差异，尽管很小，但可以用现代统计多元分析来检测，交易系统中编码了几个触发器。这类分析基于核物理中广泛使用的方法，数据样本大，统计推断先进。考虑到欧元期货合约的高频波动，我们发现可以推断出该系列的一部分非随机内容，即依赖于波动幅度的趋势跟随内容。
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英文标题：
《Statistical Methods for Estimating the non-random Content of Financial
  Markets》
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作者：
Laurent Schoeffel (CEA Saclay)
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最新提交年份：
2011
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
  For the pedestrian observer, financial markets look completely random with erratic and uncontrollable behavior. To a large extend, this is correct. At first approximation the difference between real price changes and the random walk model is too small to be detected using traditional time series analysis. However, we show in the following that this difference between real financial time series and random walks, as small as it is, is detectable using modern statistical multivariate analysis, with several triggers encoded in trading systems. This kind of analysis are based on methods widely used in nuclear physics, with large samples of data and advanced statistical inference. Considering the movements of the Euro future contract at high frequency, we show that a part of the non-random content of this series can be inferred, namely the trend-following content depending on volatility ranges.
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PDF链接：
https://arxiv.org/pdf/1108.2937