论文标题
用于返回方向的机器学习方法使用分类和回归模型预测交易所交易基金
Machine learning method for return direction forecasting of Exchange Traded Funds using classification and regression models
论文作者
论文摘要
本文旨在使用机器学习方法提出并采用机器学习方法，以使用其组件的历史回报数据来分析交易所交易基金（ETF）的回报方向，从而通过交易算法有助于制定投资策略决策。从方法论方面，除了算法误差指标外，还使用来自巴西和美国市场的标准数据集应用了回归和分类模型。在研究结果方面，它们进行了分析并将其与幼稚的预测和在同一时期购买和持有技术获得的回报进行了比较。在风险和回报方面，模型的性能大多要比控制指标更好，重点是线性回归模型和分类模型，通过逻辑回归，支持向量机（使用线性模型），高斯天真的贝叶斯和k-neart邻居，在某些数据集中，在某些数据集中超过了两次和夏普的模型，这些模型超过了这些模型。
This article aims to propose and apply a machine learning method to analyze the direction of returns from Exchange Traded Funds (ETFs) using the historical return data of its components, helping to make investment strategy decisions through a trading algorithm. In methodological terms, regression and classification models were applied, using standard datasets from Brazilian and American markets, in addition to algorithmic error metrics. In terms of research results, they were analyzed and compared to those of the Naïve forecast and the returns obtained by the buy & hold technique in the same period of time. In terms of risk and return, the models mostly performed better than the control metrics, with emphasis on the linear regression model and the classification models by logistic regression, support vector machine (using the LinearSVC model), Gaussian Naive Bayes and K-Nearest Neighbors, where in certain datasets the returns exceeded by two times and the Sharpe ratio by up to four times those of the buy & hold control model.
