Stock Price Prediction Based on Data Mining Combination Model

Chang, To-Han; Wang, Nientsu; Chuang, Wen-Bin

doi:10.4018/jgim.296707

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…Over time, models based on artificial intelligence applications, such as artificial neural networks and deep learning, have been highlighted in various scientific studies and applications in different sectors (Chang, Wang, & Chuang, 2022;Du & Shu, 2022;Feng & Chen, 2022;Hossain et al, 2022;Hou et al, 2022;Li, Shang et al, 2022;Paul, Riaz, & Das, 2022;Qiu, 2022;Rashidin et al, 2022;Shrivastav, 2022;Sun et al, 2022;Varsha et al, 2021;Wu, Qiao et al, 2022;Wu, Zhu et al, 2022;Xu, Xiang, & He, 2022;Yang & Wu, 2022;Zhao, 2022). However, the financial sector -FinTech, has shown strong growth in using this method to provide financial solutions.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Intention to Adopt Innovation in Supply Chain Finance

Figueiredo,

Camargo,

Ferreira

et al. 2023

Journal of Organizational and End User Computing

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Based on the mixed model unified technology acceptance and utilization theory (UTAUT) and spinner innovation model (SPINNER), a theoretical model is suggested to explain the determinant of behavioral intention to predict innovation in the context of a financial sector firm. A questionnaire was developed to collect primary data, which was subsequently processed through the artificial intelligence technique (deep learning). The constructs (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, public knowledge, private knowledge, and innovation) supported the model, including mediating hypotheses. It was observed that the mixed methodological approach (SEM and ANN) can help to find the linear and non-linear relationships better, being that the error of the predicted model is 0.104, that is, 10.4% relatively low, which evidences that ANN can be used to predict the dependent variable innovation safely.

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Section: Discussionmentioning

confidence: 99%

Predicting the Intention to Adopt Innovation in Supply Chain Finance

Figueiredo,

Camargo,

Ferreira

et al. 2023

Journal of Organizational and End User Computing

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“…Sun et al, (2023) introduced integrating a multifactor model to build a medium-term investment strategy. Chang et al,(2022) based on data mining combination to predict stock price.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Reinforcement Learning for Adaptive Stock Trading

Zhao,

Deng,

et al. 2024

Journal of Organizational and End User Computing

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In this study, the authors explore how financial institutions make decisions about stock trading strategies in a rapidly changing and complex environment. These decisions are made with limited, often inconsistent information and depend on the current and future strategies of both the institution itself and its competitors. They develop a dynamic game model that factors in this imperfect information and the evolving nature of decision-making. To model reward transitions, they utilize a combination of t-Copula simulation of a non-stationary Markov chain, probabilistic fuzzy regression, and chaos optimization algorithms. They then apply deep q-network, a method from deep reinforcement learning, to ensure the effectiveness of the chosen strategy during ongoing decision-making. The approach is significant for both researchers across fields and practical professionals in the finance industry.

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“…Cheng et al (Cheng et al 2021) present the way they combine decision tree and Apriori algorithm (Agrawal et al 1996;Han, Pei, and Yin 2000) into investment decision models. Chang et al (Chang, Wang, and Chuang 2021) present a data mining pipeline based on neural network, support vector machine, mixed data sampling, etc. for stock price prediction.…”

Section: Related Workmentioning

confidence: 99%

Financial data analysis application via multi-strategy text processing

Zhu¹

2022

Preprint

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Maintaining financial system stability is critical to economic development, and early identification of risks and opportunities is essential. The financial industry contains a wide variety of data, such as financial statements, customer information, stock trading data, news, etc. Massive heterogeneous data calls for intelligent algorithms for machines to process and understand. This paper mainly focuses on the stock trading data and news about China A-share companies. We present a financial data analysis application, Financial Quotient Porter, designed to combine textual and numerical data by using a multi-strategy data mining approach. Additionally, we present our efforts and plans in deep learning financial text processing application scenarios using natural language processing (NLP) and knowledge graph (KG) technologies. Based on KG technology, risks and opportunities can be identified from heterogeneous data. NLP technology can be used to extract entities, relations, and events from unstructured text, and analyze market sentiment. Experimental results show market sentiments towards a company and an industry, as well as news-level associations between companies.

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Stock Price Prediction Based on Data Mining Combination Model

Cited by 9 publications

References 31 publications

Predicting the Intention to Adopt Innovation in Supply Chain Finance

Predicting the Intention to Adopt Innovation in Supply Chain Finance

Deep Reinforcement Learning for Adaptive Stock Trading

Financial data analysis application via multi-strategy text processing

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