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首页> 外文期刊>Applied Soft Computing >Predicting next day direction of stock price movement using machine learning methods with persistent homology: Evidence from Kuala Lumpur Stock Exchange
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Predicting next day direction of stock price movement using machine learning methods with persistent homology: Evidence from Kuala Lumpur Stock Exchange

机译：使用持续同源性的机器学习方法预测股票价格运动的第二天方向：从吉隆坡证券交易所的证据

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摘要

Predicting direction of stock price movement is notably important to provide a better guidance to assist market participants in making their investment decisions. This study presents a hybrid method combining machine learning methods with persistent homology to improve the prediction performance. Three stock prices namely Kuala Lumpur Composite Index, Kuala Lumpur Stock Exchange Industrial and Kuala Lumpur Stock Exchange Technology sampled from Kuala Lumpur Stock Exchange are selected for experimental evaluation. In particular, persistent homology was applied to obtain a new and useful input vectors of invariant topological features from returns of these stock prices for further classification task using machine learning methods such as logistic regression, artificial neural network, support vector machine and random forest to predict the next day movement direction of Kuala Lumpur Composite Index. For comparative analysis, we compare the proposed method with others, where the machine learning methods are applied independently on stock returns and also on technical indicators respectively. By using the average of prediction performances and pairwise model comparison method, these two evaluation measures revealed that machine learning methods with persistent homology produced better prediction performance. Our results also demonstrated that the combination of support vector machine with persistent homology generates the best outcome. In general, a combination of machine learning methods with persistent homology is an emerging and promising alternative tool for predicting direction of stock price movement. (c) 2020 Elsevier B.V. All rights reserved.
机译：预测股票价格的方向毫不符合重要的是提供更好的指导，以协助市场参与者制定投资决策。本研究提出了一种混合方法，将机器学习方法与持久性同源性结合以提高预测性能。三层股票价格​​吉隆坡综合指数，吉隆坡证券交易所工业和吉隆坡证券交易所从吉隆坡证券交易所取样进行实验评估。特别地，应用持续同源性以获得来自这些股票价格的返回的不变性拓扑特征的新的和有用的输入向量，用于使用逻辑回归，人工神经网络，支持向量机和随机森林等机器学习方法来获得进一步的分类任务，以便预测吉隆坡综合指数的第二天移动方向。对于比较分析，我们将提出的方法与他人进行比较，其中机器学习方法分别独立应用于库存回报和技术指标。通过使用预测性能的平均值和成对型号比较方法，这两个评估措施揭示了具有持续同源性的机器学习方法产生了更好的预测性能。我们的结果还表明，带有持续同源性的支持向量机的组合产生了最佳结果。通常，具有持久性同源性的机器学习方法的组合是一种新兴和有前途的替代工具，用于预测股票价格运动方向。 （c）2020 Elsevier B.V.保留所有权利。

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