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Predictive Modelling Of Select Cryptocurrencies And Identifying The Best Suitable Model - With Reference To Arima And Anns

Author

Listed:
  • PROF. REEPU

    (CHANDIGARH UNIVERSITY)

  • PROF.BIJESH DHYANI

    (FACULTY, MANAGEMENT STUDIES, GRAPHIC ERA DEEMED TO BE UNIVERSITY DEHRADUN, INDIA)

  • MS. AYUSHI

    (STUDENT, FIIB, NEW DELHI)

  • DR. SUDHI SHARMA

    (ASSISTANT PROFESSOR, FIIB, NEW DELHI)

  • DR. MANISH KUMAR

    (ASSOCIATE PROFESSOR, MANAGEMENT STUDIES, GRAPHIC ERA DEEMED TO BE UNIVERSITY DEHRADUN, INDIA)

Abstract

In the 4th Industrial revolution, cryptocurrencies emerged as a technology-based financial asset. The digital currency market is the repercussion of the financial crisis of 2008, thus creating disruption in the whole financial market. Investors are fascinated by the crypto market to get the benefit of abnormal returns. Taking into consideration of active trading in digital currency, the paper identifies the best predictable model of select cryptocurrencies i.e. Bitcoin, Ethereum, and Tether by applying ARIMA and ANNs. Finally, the robustness of models has been found by using the criteria i.e. MSE and MASE. It has been found that ANNs are the most suitable model among the two to predict the future prices of cryptocurrencies. The results of the study comprise that the best fit model of ARIMA for Bitcoin is (4,1,1), for Tether (1,1,2), and for Ethereum (1,1,1). Results of ANNs show that for Bitcoin, Tether, and Ethereum, the best suited ANN models are NNAR(1,1); NNAR(16,8), and NNAR(7,4), respectively. The study is of great importance to investors who are looking for investments in the most traded cryptocurrencies. Finally, from the results of various parameters i.e. RMSE, MAE, MPE, and MAPE, for Bitcoin ARIMA is the best-suited model and for Tether and Ethereum, ANNs are the best-suited or robust models for predicting the stock prices.

Suggested Citation

  • Prof. Reepu & Prof.Bijesh Dhyani & Ms. Ayushi & Dr. Sudhi Sharma & Dr. Manish Kumar, 2022. "Predictive Modelling Of Select Cryptocurrencies And Identifying The Best Suitable Model - With Reference To Arima And Anns," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 11-19, December.
    • Handle: RePEc:cbu:jrnlec:y:2022:v:6:p:11-19
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    References listed on IDEAS

    as
    1. Sun, Xiaolei & Liu, Mingxi & Sima, Zeqian, 2020. "A novel cryptocurrency price trend forecasting model based on LightGBM," Finance Research Letters, Elsevier, vol. 32(C).
    2. Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.
    3. Fahad Mostafa & Pritam Saha & Mohammad Rafiqul Islam & Nguyet Nguyen, 2021. "GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies," JRFM, MDPI, vol. 14(9), pages 1-22, September.
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