Predicting Financial Risk Associated to Bitcoin Investment by Deep Learning

  • Nahla Aljojo
    College of Computer Science and Engineering, Information system and Technology Department, University of Jeddah, Jeddah nmaljojo[at]


The financial risk of investing in Bitcoin is increasing, and everyone partic-ipating in the transaction is aware of it. The rise and fall of bitcoin’s value is difficult to predict, and the system is fraught with uncertainty. As a result, this study proposed to use the «Deep learning» technique for predicting fi-nancial risk associated with bitcoin investment, that is linked to its «weighted price» on the bitcoin market’s volatility. The dataset used included Bitcoin historical data, which was acquired «at one-minute intervals» from selected exchanges of January 2012 through December 2020. The deep learning lin-ear-SVM-based technique was used to obtain an advantage in handling the high-dimensional challenges related with bitcoin-based transaction transac-tions large data volume. Four variables («High», «Low», «Close», and «Volume (BTC)».) are conceptualized to predict weighted price, in order to indi-cate if there is a propensity of financial risk over the effect of their interaction. The results of the experimental investigation show that the fi-nancial risk associated with bitcoin investing is accurately predicted. This has helped to discover engagements and disengagements with doubts linked with bitcoin investment transactions, resulting in increased confidence and trust in the system as well as the elimination of financial risk. Our model had a significantly greater prediction accuracy, demonstrating the utility of deep learning systems in detecting financial problems related to digital currency.
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Abubakar, A.I., Chiroma, H. and Abdulkareem, S., 2015. Comparing performances of neural network models built through transformed and original data. In 2015 International Conference on Computer, Communications, and Control Technology (I4CT) (pp. 364–369).

Balvers, R.J. and McDonald, B., 2021. Designing a global digital currency. Journal of International Money and Finance, 111, p.102317.

Bengio, Yoshua, Aaron Courville, and Pascal Vincent. «Representation learning: A review and new perspectives». IEEE transactions on pattern analysis and machine intelligence 35, no. 8 (2013): 1798–1828.

Dixit, P. and Silakari, S., 2021. Deep learning algorithms for cybersecurity applications: A technological and status review. Computer Science Review, 39, p.100317.

Dutta, A., Kumar, S. and Basu, M., 2020. A gated recurrent unit approach to bitcoin price prediction. Journal of Risk and Financial Management, 13(2), p.23.

Embrechts, P., Puccetti, G., Rüschendorf, L., Wang, R. and Beleraj, A., 2014. An academic response to Basel 3.5. Risks, 2(1), pp.25–48.

Felizardo, L., Oliveira, R., Del-Moral-Hernandez, E. and Cozman, F., 2019, October. Comparative study of Bitcoin price prediction using WaveNets, Recurrent Neural Networks and other Machine Learning Methods. In 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC) (pp. 1–6).

Fischer, M., Moser, T. and Pfeuffer, M., 2018. A discussion on recent risk measures with application to credit risk: Calculating risk contributions and identifying risk concentrations. Risks, 6(4), p.142.

Foley, S., Karlsen, J.R. and Putni?š, T.J., 2019. Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies?. The Review of Financial Studies, 32(5), pp.1798–1853.

Gil-Alana, L.A., Abakah, E.J.A. and Rojo, M.F.R., 2020. Cryptocurrencies and stock market indices. Are they related?. Research in International Business and Finance, 51, p.101063.

Gloor, P., Colladon, A.F., de Oliveira, J.M. and Rovelli, P., 2020. Put your money where your mouth is: Using deep learning to identify consumer tribes from word usage. International Journal of Information Management, 51, p.101924.

Gómez, J.A., Arévalo, J., Paredes, R. and Nin, J., 2018. End-to-end neural network architecture for fraud scoring in card payments. Pattern Recognition Letters, 105, pp.175–181.

Ji, S., Kim, J. and Im, H., 2019. A comparative study of bitcoin price prediction using deep learning. Mathematics, 7(10), p.898.

Jiang, Z. and Liang, J., 2017. Cryptocurrency portfolio management with deep reinforcement learning. In 2017 Intelligent Systems Conference (IntelliSys) (pp. 905–913).

Jiang, Z., Xu, D. and Liang, J., 2017. A deep reinforcement learning framework for the financial portfolio management problem. arXiv preprint arXiv:1706.10059.

Jiménez, I., Mora-Valencia, A. and Perote, J., 2020. Risk quantification and validation for Bitcoin. Operations Research Letters, 48(4), pp.534–541.

Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.E., He-Guelton, L. and Caelen, O., 2018. Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, pp.234–245.

Kowalski, M., Lee, Z.W. and Chan, T.K., 2021. Blockchain technology and trust relationships in trade finance. Technological Forecasting and Social Change, 166, p.120641.

Lamothe-Fernández, P., Alaminos, D., Lamothe-López, P. and Fernández-Gámez, M.A., 2020. Deep Learning Methods for Modeling Bitcoin Price. Mathematics, 8(8), p.1245.

LeCun, Y., 2019. 1.1 deep learning hardware: Past, present, and future. In 2019 IEEE International Solid-State Circuits Conference-(ISSCC) (pp. 12–19).

LeCun, Y., Bengio, Y. and Hinton, G., 2015. Deep learning. nature 521 (7553), 436–444.

Linardatos, p. and Kotsiantis, S., 2020. Bitcoin Price Prediction Combining Data and Text Mining. In Advances in Integrations of Intelligent Methods (pp. 49–63). Springer, Singapore.

Lopes, R.G., Fenu, S. and Starner, T., 2017. Data-free knowledge distillation for deep neural networks. arXiv preprint arXiv:1710.07535.

McNally, S., Roche, J. and Caton, S., 2018. Predicting the price of bitcoin using machine learning. In 2018 26th euromicro international conference on parallel, distributed and network-based processing (PDP) (pp. 339–343).

Rizwan, M., Narejo, S., and Javed, M., 2019. Bitcoin price prediction using deep learning algorithm. In 2019 13th IEEE. International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) (pp. 1-7) .

Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams, S. and Beling, P., 2018. Deep learning detecting fraud in credit card transactions. In 2018 Systems and Information Engineering Design Symposium (SIEDS) (pp. 129–134).

Sohony, I., Pratap, R. and Nambiar, U., 2018, January. Ensemble learning for credit card fraud detection. In Proceedings of the ACM India Joint International Conference on Data Science and Management of Data (pp. 289–294).

Spilak, B., 2018. Deep neural networks for cryptocurrencies price prediction (Master's thesis, Humboldt-Universität zu Berlin).

Yaga, D., Mell, p., Roby, N. and Scarfone, K., 2019. Blockchain technology overview.arXiv preprint arXiv:1906.11078.

Young, C., 2017. South Korea Officially Legalizes Bitcoin, Huge Market for Traders. Available online: (accessed on 02 May 2020).

Zhang, C., Tan, K.C., Li, H. and Hong, G.S., 2018. A cost-sensitive deep belief network for imbalanced classification. IEEE transactions on neural networks and learning systems, 30(1), pp.109–122.
Aljojo, N. (2022). Predicting Financial Risk Associated to Bitcoin Investment by Deep Learning . ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(1), 5–18.

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