A multi-source heterogeneous data analytic method for future price fluctuation prediction

Chai, Lei; Xu, Hongfeng; Luo, Zhiming; Li, Shaozi

doi:10.1016/j.neucom.2020.07.073

Cited by 17 publications

(15 citation statements)

References 34 publications

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“…In [35], a prediction method of stock market price trend based on high order HMM was presented. In [36], a multisource heterogeneous data analysis method was constructed to integrate multisource information, namely transaction data, news event data, and investor comments to predict future stock price. In [37], an adaptive hidden Markov abnormal state model (AHMMAS) was proposed to model and detect price manipulation activities.…”

Section: Related Workmentioning

confidence: 99%

Research on HMM-Based Efficient Stock Price Prediction

2022

Mobile Information Systems

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Stock market is one of the most important parts of the investment market. Compared with other industries, the stock market not only has a higher rate of return on investment but also has a higher risk, and stock price prediction has always been a close concern of investors. Therefore, the research on stock price prediction methods and how to reduce the error of stock price prediction has become a hot topic for many scholars at home and abroad. In recent years, the development of computer technology such as machine learning and econometric method makes the stock price prediction more reliable. Due to the hidden Markov nature of stock price, this paper proposes a stock price prediction method based on hidden Markov model (HMM). To be specific, since the data of stock price have continuity in time series, it is necessary to extend the discrete HMM to the continuous HMM, and then put forward the up and down trend prediction model based on the continuous HMM. The first-order continuous HMM is extended to the second-order continuous HMM, and the stock price is predicted by combining the prediction method of fluctuation range. As a result, the proposed second-order continuous HMM-based stock price prediction model is simulated on Hang Seng Index (HSI), one of the earliest stock market indexes in Hong Kong. The evaluation results on six months HSI show that the predicted value of the proposed model is very close to the actual value and outperforms three benchmarks in terms of RMSE, MAE, and R2.

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Section: Related Workmentioning

confidence: 99%

Research on HMM-Based Efficient Stock Price Prediction

2022

Mobile Information Systems

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“…The main objective of the proposed approach was to improve the efficacy of time series prediction and provide more reliable performance. The forecasting of the palm oil price fluctuation was conducted using MHDA method by Chai et al [117] in which investor comments and multiple-source information were combined. The accuracy, however, is only 64.15% which is not strong enough for reliable prediction.…”

Section: G Miscellaneous Crops Yield Predictionmentioning

confidence: 99%

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

et al. 2021

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“…The authors also include emojis in their data to explore their effect on investor sentiment analysis and show that it significantly improves sentiment classification in traditional algorithms. Chai et al [19] build a multi-source heterogeneous data analysis (MHDA) price prediction model by combining stock data, news event data and investor comments from financial discussion boards. The model is tested on the data of palm oil features.…”

Section: Textual Datamentioning

confidence: 99%

Stock Market Analysis with Text Data: A Review

Fataliyev,

Chivukula,

Prasad

et al. 2021

Preprint

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Stock market movements are influenced by public and private information shared through news articles, company reports, and social media discussions. Analyzing these vast sources of data can give market participants an edge to make profit. However, the majority of the studies in the literature are based on traditional approaches that come short in analyzing unstructured, vast textual data. In this study, we provide a review on the immense amount of existing literature of text-based stock market analysis. We present input data types and cover main textual data sources and variations. Feature representation techniques are then presented. Then, we cover the analysis techniques and create a taxonomy of the main stock market forecast models. Importantly, we discuss representative work in each category of the taxonomy, analyzing their respective contributions. Finally, this paper shows the findings on unaddressed open problems and gives suggestions for future work. The aim of this study is to survey the main stock market analysis models, text representation techniques for financial market prediction, shortcomings of existing techniques, and propose promising directions for future research.

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A multi-source heterogeneous data analytic method for future price fluctuation prediction

Cited by 17 publications

References 34 publications

Research on HMM-Based Efficient Stock Price Prediction

Research on HMM-Based Efficient Stock Price Prediction

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

Stock Market Analysis with Text Data: A Review

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