Skip to main content

Predicting Cryptocurrency Price Returns by Using Deep Learning Model of Technical Analysis Indicators

Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

Over the last few years, cryptocurrencies have become a new alternative exchange currency for the global economy. Due to the high volatility in the prices of cryptocurrencies, forecasting the price movements is considered a very complicated challenge in the world of finance. Technical analysis indicators are one of the prediction tools which are widely used by analysts. These indicators, which are explored from the historical prices and volumes, might have useful information on price dynamics in the market. Meanwhile, with the new advances in artificial intelligence techniques, like long short-term memory (LSTM), which is able to keep the track of long-term dependencies; there is the extensive application of deep neural networks for predicting nonstationary and nonlinear time series. This study provides a forecasting method for cryptocurrencies by applying an LSTM multi-input neural network to investigate the prediction power of the lags of technical analysis indicators as the inputs to forecast the price returns of the three cryptocurrencies; Bitcoin(BTC), Ethereum (ETH), and Ripple (XRP) that have the highest market capitalization. The results illustrate that the proposed method helps the investors to make more reliable decisions by significantly improving the prediction accuracy against the random walk over the maximum trading time of BTC, ETH, and XRP datasets.

Keywords

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Brock, W. A., & De Lima, P. J. (1996). 11 nonlinear time series, complexity theory, and finance. Handbook of Statistics, 14, 317–361.

    Article  Google Scholar 

  • Chen, Y. J., Chen, Y. M., Tsao, S. T., & Hsieh, S. F. (2018). A novel technical analysis-based method for stock market forecasting. Soft Computing, 22(4), 1295–1312.

    Article  Google Scholar 

  • Chortareas, G. E., Garza-Garcia, J. G., & Girardone, C. (2011). Banking sector performance in Latin America: Market power versus efficiency. Review of Development Economics, 15(2), 307–325.

    Article  Google Scholar 

  • de Souza, M. J. S., Ramos, D. G. F., Pena, M. G., Sobreiro, V. A., & Kimura, H. (2018). Examination of the profitability of technical analysis based on moving average strategies in BRICS. Financial Innovation, 4(1), 1–18.

    Article  Google Scholar 

  • Derbentsev, V., Matviychuk, A., Soloviev, V. N. (2020). Forecasting of cryptocurrency prices using machine learning. In Advanced studies of financial technologies and cryptocurrency markets (pp. 211–231). Springer, .

    Google Scholar 

  • Draxler, J., & Siebenhofer, M. (2014). Verfahrenstechnik in Beispielen: Problemstellungen, Lösungsansätze. Springer-Verlag.

    Book  Google Scholar 

  • Dwyer, G. P. (2015). The economics of bitcoin and similar private digital currencies. Journal of Financial Stability, 17, 81–91.

    Article  Google Scholar 

  • Fama, E. F. (1965). The behavior of stock-market prices. The Journal of Business, 38(1), 34–105.

    Article  Google Scholar 

  • Ferdiansyah, F., Othman, S. H., Radzi, R. Z. R. M., Stiawan, D., Sazaki, Y., Ependi, U. (2019, October). A lstm-method for bitcoin price prediction: A case study yahoo finance stock market. In 2019 international conference on electrical engineering and computer science (ICECOS) (pp. 206–210). IEEE.

    Google Scholar 

  • Granville, P. S. (1976). A modified law of the wake for turbulent shear layers. ASME Journal of Fluids Engineering, 98, 578–580.

    Article  Google Scholar 

  • Hong, Y. Y., Wan, C., No, S., & Chiu, C. Y. (2007). Multicultural identities.

    Google Scholar 

  • Kirkpatrick, C. D., II, & Dahlquist, J. A. (2010). Technical analysis: The complete resource for financial market technicians. FT press.

    Google Scholar 

  • Kuang, P., Schröder, M., & Wang, Q. (2014). Illusory profitability of technical analysis in emerging foreign exchange markets. International Journal of Forecasting, 30(2), 192–205.

    Article  Google Scholar 

  • Lahmiri, S. (2014). Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. Healthcare technology letters, 1(3), 104–109.

    Article  Google Scholar 

  • Li, L., Arab, A., Liu, J., Liu, J., Han, Z. (2019, July). Bitcoin options pricing using LSTM-based prediction model and blockchain statistics. In 2019 IEEE international conference on Blockchain (Blockchain) (pp. 67–74). IEEE.

    Google Scholar 

  • Livieris, I. E., Pintelas, E., Stavroyiannis, S., & Pintelas, P. (2020). Ensemble deep learning models for forecasting cryptocurrency time-series. Algorithms, 13(5), 121.

    Article  Google Scholar 

  • Ma, Y., Yang, B., & Su, Y. (2020). Technical trading index, return predictability and idiosyncratic volatility. International Review of Economics & Finance, 69, 879–900.

    Article  Google Scholar 

  • Patel, M. M., Tanwar, S., Gupta, R., & Kumar, N. (2020). A deep learning-based cryptocurrency price prediction scheme for financial institutions. Journal of information security and applications, 55, 102583.

    Article  Google Scholar 

  • Pathirawasam, C. (2011). Internal factors which determine financial performance of firms: With special reference to ownership concentration. Innovation and Knowledge Management: A Global Competitive Advantage, 1, 4.

    Google Scholar 

  • Radityo, A., Munajat, Q., Budi, I. (2017, October). Prediction of bitcoin exchange rate to American dollar using artificial neural network methods. In 2017 international conference on advanced computer science and information systems (ICACSIS) (pp. 433–438). IEEE.

    Google Scholar 

  • Tanwar, S., Patel, N. P., Patel, S. N., Patel, J. R., Sharma, G., & Davidson, I. E. (2021). Deep learning-based cryptocurrency price prediction scheme with inter-dependent relations. IEEE Access, 9, 138633–138646.

    Article  Google Scholar 

  • Wilder, D. A. (1978). Reduction of intergroup discrimination through individuation of the out-group. Journal of Personality and Social Psychology, 36(12), 1361.

    Article  Google Scholar 

  • Wu, C. H., Lu, C. C., Ma, Y. F., Lu, R. S. (2018, November). A new forecasting framework for bitcoin price with LSTM. In 2018 IEEE international conference on data mining workshops (ICDMW) (pp. 168–175). IEEE.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Negar Fazlollahi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fazlollahi, N., Ebrahimijam, S. (2023). Predicting Cryptocurrency Price Returns by Using Deep Learning Model of Technical Analysis Indicators. In: Özataç, N., Gökmenoğlu, K.K., Balsalobre Lorente, D., Taşpınar, N., Rustamov, B. (eds) Global Economic Challenges. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-23416-3_13

Download citation

Publish with us

Policies and ethics