Integrating technical indicators, chip factors and stock news for enhanced stock price predictions: A multi-kernel approach

IF 5.5 Q1 MANAGEMENT

Asia Pacific Management Review Pub Date : 2024-09-01 DOI:10.1016/j.apmrv.2023.10.001

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引用次数: 0

Abstract

In the field of stock price forecasting, we are actively seeking to integrate various information to more accurately grasp market dynamics. Although historical stock prices and financial news have been widely used in previous studies, it is relatively rare to find research considering news-based, technical, and chip factors simultaneously and evaluating their combined effect. In this study, we innovatively propose a multi-kernel model that not only combines news-based, technical, and chip factor analysis but also utilizes market data provided by the Taiwan Stock Exchange, including institutional trading situations and stock price technical indicators. The aim is to further enhance the prediction accuracy of stock price dynamics. Based on the frequency of word occurrences, we design a new discriminant index to extract features highly correlated with stock prices from financial news. The empirical results show that our multi-kernel model significantly surpasses the single-kernel model in prediction accuracy. However, we also find that although financial news is somewhat correlated with stock price dynamics, information such as chip factors and stock price technical indicators contribute more significantly in our model. This further validates that our multi-kernel learning algorithm can effectively handle multifaceted data sources and give appropriate weights according to the importance of each data point, thereby enhancing the comprehensiveness of prediction. Through this research, we hope to bring new perspectives and inspirations to the field of stock price forecasting.

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整合技术指标、筹码因素和股票新闻，提高股价预测能力：多核方法

在股价预测领域，我们正积极寻求整合各种信息，以更准确地把握市场动态。虽然历史股价和财经新闻已被广泛应用于以往的研究中，但同时考虑新闻因素、技术因素和筹码因素并评估其综合效应的研究却相对较少。在本研究中，我们创新性地提出了一个多核模型，该模型不仅结合了新闻因素、技术因素和筹码因素分析，还利用了台湾证券交易所提供的市场数据，包括机构交易情况和股价技术指标。目的是进一步提高股价动态预测的准确性。基于词的出现频率，我们设计了一种新的判别指标，从财经新闻中提取与股价高度相关的特征。实证结果表明，我们的多核模型在预测准确性上明显优于单核模型。不过，我们也发现，虽然财经新闻与股价动态有一定的相关性，但筹码因素和股价技术指标等信息对我们的模型贡献更大。这进一步验证了我们的多核学习算法可以有效地处理多方面的数据源，并根据每个数据点的重要性给予适当的权重，从而提高预测的全面性。通过这项研究，我们希望能为股价预测领域带来新的视角和启发。

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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来源期刊

Asia Pacific Management Review MANAGEMENT-

CiteScore

8.00

自引率

4.50%

发文量

期刊介绍： Asia Pacific Management Review (APMR), peer-reviewed and published quarterly, pursues to publish original and high quality research articles and notes that contribute to build empirical and theoretical understanding for concerning strategy and management aspects in business and activities. Meanwhile, we also seek to publish short communications and opinions addressing issues of current concern to managers in regards to within and between the Asia-Pacific region. The covered domains but not limited to, such as accounting, finance, marketing, decision analysis and operation management, human resource management, information management, international business management, logistic and supply chain management, quantitative and research methods, strategic and business management, and tourism management, are suitable for publication in the APMR.