摘要翻译：
我们构建了一个金融“图灵测试”，以确定人类受试者是否能够区分实际和随机的金融回报。实验包括一个在线视频游戏(http://arora.ccs.neu.edu)，玩家要区分实际的金融市场回报和这些回报的随机时间排列。我们发现压倒性的统计证据（p值不超过0.5%)，表明受试者可以一致地区分这两种类型的时间序列，从而驳斥了金融市场“看起来随机”的普遍信念。实验的一个关键特点是，受试者会得到关于他们选择的有效性的即时反馈，允许他们学习和适应。我们认为，这种新的界面可以利用人类的能力，以计算机无法做到的方式处理和提取金融数据中的信息。
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英文标题：
《Is It Real, or Is It Randomized?: A Financial Turing Test》
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作者：
Jasmina Hasanhodzic, Andrew W. Lo, Emanuele Viola
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最新提交年份：
2010
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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一级分类：Computer Science        计算机科学
二级分类：Computational Engineering, Finance, and Science        计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Human-Computer Interaction        人机交互
分类描述：Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
包括人为因素、用户界面和协作计算。大致包括ACM学科课程H.1.2和所有H.5中的材料，除了H.5.1，它更有可能以多媒体作为主要学科领域。
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英文摘要：
  We construct a financial "Turing test" to determine whether human subjects can differentiate between actual vs. randomized financial returns. The experiment consists of an online video-game (http://arora.ccs.neu.edu) where players are challenged to distinguish actual financial market returns from random temporal permutations of those returns. We find overwhelming statistical evidence (p-values no greater than 0.5%) that subjects can consistently distinguish between the two types of time series, thereby refuting the widespread belief that financial markets "look random." A key feature of the experiment is that subjects are given immediate feedback regarding the validity of their choices, allowing them to learn and adapt. We suggest that such novel interfaces can harness human capabilities to process and extract information from financial data in ways that computers cannot.
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PDF链接：
https://arxiv.org/pdf/1002.4592