摘要翻译：
由于许多原因，计算智能和机器学习方法在很大程度上被专业团体所忽视。造成这种情况的原因是多种多样的，但不可避免的是，所设计的系统往往不能达到设计者的预期。这种缺乏性能的原因是市场预测系统中常见的错误的直接结果。本文考察了一些比较常见的错误，即数据集不充分；缩放不当；时间序列跟踪；不适当的目标量化和不适当的业绩计量。分析了导致每一个错误的基本原理，以及它们引入分析/设计的错误的性质。为了避免这些错误的延续，还推荐了执行每项任务的替代方法，并希望有助于为在工业中使用这些强大的技术扫清道路。
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英文标题：
《Common Mistakes when Applying Computational Intelligence and Machine
  Learning to Stock Market modelling》
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作者：
E. Hurwitz and T. Marwala
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最新提交年份：
2012
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Computer Science        计算机科学
二级分类：Computers and Society        计算机与社会
分类描述：Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
涵盖计算机对社会的影响、计算机伦理、信息技术和公共政策、计算机的法律方面、计算机和教育。大致包括ACM学科类K.0、K.2、K.3、K.4、K.5和K.7中的材料。
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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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英文摘要：
  For a number of reasons, computational intelligence and machine learning methods have been largely dismissed by the professional community. The reasons for this are numerous and varied, but inevitably amongst the reasons given is that the systems designed often do not perform as expected by their designers. The reasons for this lack of performance is a direct result of mistakes that are commonly seen in market-prediction systems. This paper examines some of the more common mistakes, namely dataset insufficiency; inappropriate scaling; time-series tracking; inappropriate target quantification and inappropriate measures of performance. The rationale that leads to each of these mistakes is examined, as well as the nature of the errors they introduce to the analysis / design. Alternative ways of performing each task are also recommended in order to avoid perpetuating these mistakes, and hopefully to aid in clearing the way for the use of these powerful techniques in industry.
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PDF链接：
https://arxiv.org/pdf/1208.4429