英文标题：
《A Study on Neural Network Architecture Applied to the Prediction of
Brazilian Stock Returns》
---
作者：
Leonardo Felizardo, Afonso Pinto
---
最新提交年份：
2019
---
英文摘要：
In this paper we present a statistical analysis about the characteristics that we intend to influence in the performance of the neural networks in terms of assertiveness in the prediction of Brazilian stock returns. We created a population of architectures for analysis and extracted the sample that had the best assertive performance. It was verified how the characteristics of this sample stand out and affect the neural networks. In addition, we make inferences about what kind of influence the different architectures have on the performance of neural networks. In the study, the prediction of the return of a Brazilian stock traded on the stock exchange of S\\~ao Paulo to measure the error committed by the different architectures of constructed neural networks. The results are promising and indicate that some aspects of the neural network architecture have a significant impact on the assertiveness of the model.
---
中文摘要：
在本文中，我们对我们打算影响神经网络性能的特征进行了统计分析，这些特征涉及巴西股票收益预测中的自信。我们创建了一组体系结构进行分析，并提取了具有最佳断言性能的样本。验证了该样本的特征是如何突出并影响神经网络的。此外，我们还推断了不同结构对神经网络性能的影响。在这项研究中，对圣保罗证券交易所交易的巴西股票的回报率进行预测，以衡量所构建的神经网络的不同结构所带来的误差。结果是有希望的，并表明神经网络结构的某些方面对模型的自信有重大影响。
---
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
--

---
PDF下载：
-->