Enhancing stock market prediction with extended coupled hidden Markov model over multi-sourced data

Zhang, Xi; Li, Yixuan; Wang, Senzhang; Fang, Binxing; Yu, Philip S.

doi:10.1007/s10115-018-1315-6

Cited by 39 publications

(16 citation statements)

References 52 publications

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“…ANN and genetic algorithms (GA)) and a fuzzy model to optimize the selection of input and output sequences. Later, maximum a posterior HMM [29] and extended coupled HMM models [30] were developed to predict stock prices in the USA and Chinese stock markets. Table 1 summarizes the abovementioned references.…”

Section: Hidden Markov Modelmentioning

confidence: 99%

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

Shi

Zhuang

2019

Axioms

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Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors. The fast advancement of soft computing techniques provides an alternative approach for estimating and forecasting volatile stock prices. Soft computing approaches exploit tolerance for imprecision, uncertainty, and partial truth to progressively and adaptively solve practical problems. In this study, a comprehensive review of latest soft computing tools is given. Then, examples incorporating a series of machine learning models, including both single and hybrid models, to predict prices of two representative indexes and one stock in Hong Kong’s market are undertaken. The prediction performances of different models are evaluated and compared. The effects of the training sample size and stock patterns (viz. momentum and mean reversion) on model prediction are also investigated. Results indicate that artificial neural network (ANN)-based models yield the highest prediction accuracy. It was also found that the determination of optimal training sample size should take the pattern and volatility of stocks into consideration. Large prediction errors could be incurred when stocks exhibit a transition between mean reversion and momentum trend.

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Section: Hidden Markov Modelmentioning

confidence: 99%

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

Shi

Zhuang

2019

Axioms

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“…Ref. [1,7] combined two data sources to predict stock price movement and reported an accuracy of (52-63) %. Also, in [9], three data sources were joined to predict future stock price and achieved prediction accuracy (70.66-77.12)%.…”

Section: Training and Testing Results Based On The Optimised Featuresmentioning

confidence: 99%

“…Previous studies [1,7,8,25,[43][44][45] have attempted to examine the joint impacted of different stock-related information sources for predicting stock price movement, a high percentage (63%) of these studies employed 2 data sources. In comparison, 37% used 3 data sources (see Table 1).…”

Section: Discussionmentioning

confidence: 99%

“…Also, the authors reported a high association between stock price movement and public sentiments. Also, Zhang et al [7] proposed an extended coupled hidden Markov stock price prediction framework based on Web news and historical stock data. In [8], the authors proposed Multi-source multiple instance learning framework, based on three different data sources.…”

Section: Studies Based On Both Qualitative and Quantitative Datasetsmentioning

confidence: 99%

“…Notwithstanding, stock market information is multi-layered and interconnected [5]; hence, the ability to make intelligence of data, by fusing them into new knowledge would offer distinct advantages in predicting the stock market [6]. Therefore, Multi-source Data-Fusion (MDF) has become a key area of interest in recent studies in this field [7]. i.e., MDF aims to attain a global view of all factors affecting the stock price movement and make the best investment decision.…”

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confidence: 99%

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A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction

2021

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The stock market is very unstable and volatile due to several factors such as public sentiments, economic factors and more. Several Petabytes volumes of data are generated every second from different sources, which affect the stock market. A fair and efficient fusion of these data sources (factors) into intelligence is expected to offer better prediction accuracy on the stock market. However, integrating these factors from different data sources as one dataset for market analysis is seen as challenging because they come in a different format (numerical or text). In this study, we propose a novel multi-source information-fusion stock price prediction framework based on a hybrid deep neural network architecture (Convolution Neural Networks (CNN) and Long Short-Term Memory (LSTM)) named IKN-ConvLSTM. Precisely, we design a predictive framework to integrate stock-related information from six (6) heterogeneous sources. Secondly, we construct a base model using CNN, and random search algorithm as a feature selector to optimise our initial training parameters. Finally, a stacked LSTM network is fine-tuned by using the tuned parameter (features) from the base-model to enhance prediction accuracy. Our approach's emperical evaluation was carried out with stock data (January 3, 2017, to January 31, 2020) from the Ghana Stock Exchange (GSE). The results show a good prediction accuracy of 98.31%, specificity (0.9975), sensitivity (0.8939%) and F-score (0.9672) of the amalgamated dataset compared with the distinct dataset. Based on the study outcome, it can be concluded that efficient information fusion of different stock price indicators as a single data source for market prediction offer high prediction accuracy than individual data sources.

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