A Binary Stock Event Model for stock trends forecasting: Forecasting stock trends via a simple and accurate approach with machine learning

H. J. Jung, J. Aggarwal
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引用次数: 4

Abstract

The volatile and stochastic characteristics of securities make it challenging to predict even tomorrow's stock prices. Better estimation of stock trends can be accomplished using both the significant and well-constructed set of features. Moreover, the prediction capability will gain momentum as we build the right model to capture unobservable attributes of the varying tendencies. In this paper, we propose a Binary Stock Event Model (BSEM) and generate features sets based on it in order to better predict the future trends of the stock market. We apply two learning models such as a Bayesian Naive Classifier and a Support Vector Machine to prove the efficiency of our approach in the aspects of prediction accuracy and computational cost. Our experiments demonstrate that the prediction accuracies are around 70–80% in one day predictions. In addition, our back-testing proves that our trading model outperforms well-known technical indicator based trading strategies with regards to cumulative returns by 30%–100%. As a result, this paper suggests that our BSEM based stock forecasting shows its excellence with regards to prediction accuracy and cumulative returns in a real world dataset.
股票趋势预测的二元股票事件模型:通过简单而准确的机器学习方法预测股票趋势
证券的波动性和随机性使得预测明天的股票价格变得非常困难。对股票趋势的更好的估计可以使用显著的和构造良好的特征集来完成。此外,当我们建立正确的模型来捕捉变化趋势的不可观察属性时,预测能力将获得动力。本文提出了一种二元股票事件模型(BSEM),并在此基础上生成特征集,以便更好地预测股票市场的未来趋势。应用贝叶斯朴素分类器和支持向量机两种学习模型,证明了该方法在预测精度和计算成本方面的有效性。我们的实验表明,在一天的预测中,预测精度在70-80%左右。此外,我们的回测证明,我们的交易模型在累积收益方面优于著名的基于技术指标的交易策略30%-100%。因此,本文表明我们基于BSEM的股票预测在真实世界数据集的预测准确性和累积回报方面表现出卓越的表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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