英文标题：
《Stock returns forecast: an examination by means of Artificial Neural
  Networks》
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作者：
Martin Iglesias Caride, Aurelio F. Bariviera, Laura Lanzarini
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最新提交年份：
2018
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英文摘要：
  The validity of the Efficient Market Hypothesis has been under severe scrutiny since several decades. However, the evidence against it is not conclusive. Artificial Neural Networks provide a model-free means to analize the prediction power of past returns on current returns. This chapter analizes the predictability in the intraday Brazilian stock market using a backpropagation Artificial Neural Network. We selected 20 stocks from Bovespa index, according to different market capitalization, as a proxy for stock size. We find that predictability is related to capitalization. In particular, larger stocks are less predictable than smaller ones.
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中文摘要：
几十年来，有效市场假说的有效性一直受到严格审查。然而，反对它的证据并不确凿。人工神经网络提供了一种无模型的方法来分析过去收益对当前收益的预测能力。本章使用反向传播人工神经网络分析巴西股市日内的可预测性。根据不同的市值，我们从Bovespa指数中选择了20只股票作为股票规模的代表。我们发现可预测性与资本化有关。尤其是，规模较大的股票比规模较小的股票更难预测。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Quantitative Finance        数量金融学
二级分类：Pricing of Securities        证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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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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