英文标题：
《Investment Ranking Challenge: Identifying the best performing stocks
  based on their semi-annual returns》
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作者：
Shanka Subhra Mondal, Sharada Prasanna Mohanty, Benjamin Harlander,
  Mehmet Koseoglu, Lance Rane, Kirill Romanov, Wei-Kai Liu, Pranoot Hatwar,
  Marcel Salathe, Joe Byrum
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最新提交年份：
2019
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英文摘要：
  In the IEEE Investment ranking challenge 2018, participants were asked to build a model which would identify the best performing stocks based on their returns over a forward six months window. Anonymized financial predictors and semi-annual returns were provided for a group of anonymized stocks from 1996 to 2017, which were divided into 42 non-overlapping six months period. The second half of 2017 was used as an out-of-sample test of the model\'s performance. Metrics used were Spearman\'s Rank Correlation Coefficient and Normalized Discounted Cumulative Gain (NDCG) of the top 20% of a model\'s predicted rankings. The top six participants were invited to describe their approach. The solutions used were varied and were based on selecting a subset of data to train, combination of deep and shallow neural networks, different boosting algorithms, different models with different sets of features, linear support vector machine, combination of convoltional neural network (CNN) and Long short term memory (LSTM).
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中文摘要：
在2018年IEEE投资排名挑战赛中，参与者被要求构建一个模型，该模型将根据其在未来六个月内的回报确定表现最佳的股票。从1996年到2017年，为一组匿名股票提供了匿名财务预测和半年回报，这些股票被分为42个不重叠的六个月期。2017年下半年被用作模型性能的抽样测试。使用的指标是斯皮尔曼排名相关系数和模型预测排名前20%的归一化贴现累积收益（NDCG）。前六名参与者被邀请描述他们的方法。使用的解决方案多种多样，基于选择要训练的数据子集、深度和浅层神经网络的组合、不同的boosting算法、具有不同特征集的不同模型、线性支持向量机、卷积神经网络（CNN）和长-短期记忆（LSTM）的组合。
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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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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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