Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach

Othman, Anwar Hasan Abdullah; Kassim, Salina; Rosman, Romzie; Redzuan, Nur Harena

doi:10.1057/s41272-020-00229-3

Cited by 18 publications

(23 citation statements)

References 23 publications

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“…Equally interesting is the development of the theory of quantum computation and quantum information [375][376][377][378] where it would be interesting to see their influence on blockchain and cryptocurrencies. Moreover, the research in the field of artificial intelligence, machine, and deep learning does not remain without attention [51,[379][380][381][382][383][384][385][386][387][388][389][390].…”

Section: Discussionmentioning

confidence: 99%

Econophysics of cryptocurrency crashes: an overview

et al. 2021

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Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.

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Section: Discussionmentioning

confidence: 99%

Econophysics of cryptocurrency crashes: an overview

et al. 2021

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“…Since the NARX neural network is based on exogenous inputs for targeting the output, this paper is following Othman et al (2020), Floros (2009), and Moon and Yu (2010) when they employed the symmetric volatility information of prices as exogenous factors to improve the accuracy of predicting. Thus, this is the first study that uses the NARX neural networks based on symmetric volatility structure in forecasting the daily accuracy improvement for JKII prices.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This paper aims to predict the daily accuracy improvement for the JKII prices by using the volatility pattern of symmetric information of JKII using DL over the ANN which can model any connection amongst the data without statistical distribution assumptions (Othman et al, 2020). In this study, the ANN framework consists of three main elements, including input layer (IL), hidden layer (HL) and output layer (OL), each with multiple neurons.…”

Section: The Ann Framework Of the Current Studymentioning

confidence: 99%

“…Following the study of Indera et al (2017) that predicted the bitcoin prices using the NARX neural network based on the daily open, low, high and close prices, the IL in this study consists of four attributes as exogenous variables that construct basic symmetric volatility information for the JKII. Additionally, although stock volatility is affected by various parameters, the OP, HP, LP and CP which construct the symmetric volatility structure are the best parameters that illustrate the information of supply and demand for optimal informational efficiency (Othman et al, 2020). Therefore, these four attributes of the JKII are used as exogenous variables in the IL.…”

Section: The Ann Framework Of the Current Studymentioning

confidence: 99%

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Deep learning with small and big data of symmetric volatility information for predicting daily accuracy improvement of JKII prices

Ledhem¹

2022

JCMS

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PurposeThe purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information.Design/methodology/approachThis paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG).FindingsThe experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient.Practical implicationsThis research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information.Originality/valueThis research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.

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“…For example, Niu [21] established an artificial neural network model to determine the performance of diesel engines, while Das et al [22] employed a hybrid neural network model to predict stock prices. Othman et al [23] estimated the Bitcoin market price based on an ANN using symmetrical fluctuation information. Similarly, Natarajan et al [20] established an ANN for a particle swarm optimization algorithm and applied it to life predictions.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Working Efficiency of Typical Work in High‐Altitude Alpine Metal Mining Areas Based on a SeqGAN‐GABP Mixed Algorithm

Ning

Huang

Zhang

2021

Advances in Civil Engineering

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Man-machine efficacy evaluations of typical work in the safe mining of high-altitude alpine metal mines are associated with fuzziness, multiple indexes, and large subjective components. This results in difficulties in the prediction of the typical work efficiency in high-altitude alpine metal mining areas. In this study, ergonomic theory was applied to establish the evaluation index system of typical work efficiency in high-altitude alpine metal mining areas by studying the cooperative relationship between operators, working machines, working environment, and design variables. First, we investigated the collaborative relationship between workers, operating machinery, operating environment, and design variables in order to establish the evaluation index system of typical work efficiency in high-altitude alpine metal mining areas. Second, principal component analysis (PCA) was integrated with the fusion entropy weight method to (i) analyze the coupling correlation and overlapping effects between the factors influencing efficiency at different altitudes and (ii) to determine the key influencing factors. Third, a model based on the sequence generative adversarial network genetic algorithm backpropagation (SeqGAN-GABP) hybrid algorithm was established to predict the trends in the operating efficiency of typical work types in high-altitude alpine metal mining areas. Finally, three high-altitude alpine metal mines in Xinjiang were selected as representative examples to verify the proposed framework by comparing it with other state-of the art models (multiple linear regression prediction model, backpropagation (BP) neural network model, and genetic algorithm back propagation (GA-BP) neural network model). Results determine the average relative error of each model as 2.74%, 1.97%, 1.29%, and 1.02%, respectively, indicating the greater accuracy of our proposed method in predicting the efficiency of typical work types in high-altitude alpine mining areas. This study can provide a scientific basis for the establishment of mining safety judgment standards in high-altitude alpine areas.

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Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach

Cited by 18 publications

References 23 publications

Econophysics of cryptocurrency crashes: an overview

Econophysics of cryptocurrency crashes: an overview

Deep learning with small and big data of symmetric volatility information for predicting daily accuracy improvement of JKII prices

Investigating the Working Efficiency of Typical Work in High‐Altitude Alpine Metal Mining Areas Based on a SeqGAN‐GABP Mixed Algorithm

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