英文标题：
《Neural Network Models for Stock Selection Based on Fundamental Analysis》
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作者：
Yuxuan Huang, Luiz Fernando Capretz, Danny Ho
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最新提交年份：
2019
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英文摘要：
  Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial ratios. The study is designed to evaluate the performance of each architecture based on the relative return of the selected portfolios with respect to the benchmark stock index. The results show that both architectures possess the ability to separate winners and losers from a sample universe of stocks, and the selected portfolios outperform the benchmark. Our study argues that FNN shows superior performance over ANFIS.
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中文摘要：
近年来，人们积极研究神经网络结构在财务预测中的应用。本文对前馈神经网络（FNN）和自适应神经模糊推理系统（ANFIS）在利用基本财务比率进行股票预测方面进行了比较研究。该研究旨在根据所选投资组合相对于基准股票指数的相对回报来评估每个架构的绩效。结果表明，这两种架构都具有从样本股票中区分赢家和输家的能力，所选投资组合的表现优于基准。我们的研究认为，FNN显示出优于ANFIS的性能。
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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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