Skip to main content
Springer Nature Link
Log in

Bitcoin closing price movement prediction with optimal functional link neural networks

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

Bitcoin prediction is a recent area of research interest and growing fast. Since its inception, in a short period of time Bitcoin got wide popularity and considered as an investment asset. At present it is the most successful cryptocurrency compared to other altcoins dispersed in the world economy. The Bitcoin prices fluctuate like other stock markets due to inherent volatility. The investors’ confidence on Bitcoin rising fast and have been reflected on its prices. Though few computational intelligence methods are available, sophisticated methodologies for accurate prediction of Bitcoin are still lacking and need to be explored. Functional link neural network (FLN) is a flat network, offers lower computational complexity, and achieves enhanced input–output nonlinearity mapping through functional expansion of input signals. Basis functions such as Legendre, Trigonometric, Laguerre, and Chebyshev are commonly used polynomials in FLN for expansion of input signal dimension. In this article along with the weight and bias vector of FLNs, optimal number of polynomial functions for each category of basis function is selected by genetic algorithm during training process rather fixing them earlier. Therefore, an optimal FLN structure is crafted on fly from exploitation of training data. The optimal FLN models are used to predict the daily, weekly, and monthly closing prices of Bitcoin. A comparative study among optimal FLNs is carried out using evaluation metrics such as MAPE, NMSE, ARV, and U of Theil’s statistics. Finally, outcomes from experimental and comparative studies suggested the superiority of optimal FLN models for Bitcoin closing price prediction.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Abbreviations

ANN:

Artificial neural network

ARV:

Average relative variance

ARIMA:

Auto-regressive moving average

BSE:

Bombay stock exchange

CFLN:

Chebysheb functional link neural network

CNN:

Convolutional neural network

DNN:

Deep neural network

DJIA:

Dow Jones industrial average

FLN:

Functional neural network

GA:

Genetic algorithm

LFLN:

Lagurre functional link neural network

LSTM:

Long short term memory

LMS:

Least square estimation

MAPE:

Mean absolute percentage of error

MLP:

Multilayer perceptron

NMSE:

Normalized mean squared error

PSO:

Particle swarm optimization

RNN:

Recurrent neural network

SVM:

Support vector machine

TFLN:

Trigonmetric functional neural network

References

  1. Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system

  2. Grinberg R (2011) Bitcoin: an innovative alternative digital currency. Hast Sci Technol Law J 4:160–207

    Google Scholar 

  3. Mai F et al (2018) How does social media impact bitcoin value? a test of the silent majority hypothesis. J Manage Inf Syst 35(1):19–52

    Article  Google Scholar 

  4. Sapuric S, Kokkinaki A (2014) Bitcoin is volatile! Isn’t that right? Business information systems workshops, lecture notes in business information processing, pp 255–265

  5. Yermack D (2014) Is Bitcoin a real currency? Working Paper, New York University

  6. Presthus W, Nicholas OO (2017) Motivations and barriers for end-user adoption of bitcoin as digital currency. Procedia Comput Sci 121:89–97

    Article  Google Scholar 

  7. Chu J, Nadarajah S, Chan S (2015) Statistical analysis of the exchange rate of bitcoin. PLoS ONE 10(7):e0133678

    Article  Google Scholar 

  8. Núñez JA, Contreras-Valdez MI, Franco-Ruiz CA (2019) Statistical analysis of bitcoin during explosive behavior periods. PLoS ONE 14(3):e02131919

    Article  Google Scholar 

  9. Alessandretti L, ElBahrawy A, Aiello LM, Baronchelli A (2018) Anticipating cryptocurrency prices using machine learning. Complexity 2018:1

    Article  Google Scholar 

  10. Corbet S, Lucey B, Urquhart A, Yarovaya L (2019) Cryptocurrencies as a financial asset: a systematic analysis. Int Rev Financ Anal 62:182–199

    Article  Google Scholar 

  11. McNally S, Roche J, Caton S (2018) Predicting the price of bitcoin using machine learning. In: Proceedings of the 2018 26th Euromicro international conference on parallel, distributed and network-based processing (PDP), Cambridge, UK, 21–23 March 2018, pp 339–343

  12. Nakano M, Takahashi A, Takahashi S (2018) Bitcoin technical trading with artificial neural network. Phys A Stat Mech Appl 510:587–609

    Article  Google Scholar 

  13. Rebane J, Karlsson I, Denic S, Papapetrou P (2018) Seq2Seq RNNs and ARIMA models for cryptocurrency prediction: a comparative study. In: Proceedings of the KDD data science in Fintech Workshop, London, UK, 20 August 2018

  14. Jang H, Lee J, Ko H, Lee W (2018) Predicting bitcoin prices by using rolling window LSTM model. In: Proceedings of the KDD data science in Fintech Workshop, London, UK, 20 August 2018

  15. Shintate T, Pichl L (2019) Trend prediction classification for high frequency bitcoin time series with deep learning. J Risk Financ Manag 12:17

    Article  Google Scholar 

  16. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533–536

    Article  MATH  Google Scholar 

  17. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 26 June–1 July 2016, pp 770–778

  18. A comparative study of bitcoin price prediction using deep learning

  19. Awoke T, Rout M, Mohanty L, Satapathy SC Bitcoin price prediction and analysis using deep learning models. In: Communication software and networks. Springer, Singapore, pp 631–640

  20. Chen W, Xu H, Jia L, Gao Y (2020) Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants. Int J Forecast

  21. Pao YH (1989) Adaptive pattern recognition and neural networks. Addison-Wesley, Reading

    MATH  Google Scholar 

  22. Pao YH, Takefuji Y (1992) Functional-link net computing: thory, system architecture, and functionalities. Computer 25:76–79

    Article  Google Scholar 

  23. Patra JC, Bos AVD (2000) Modeling of an intelligent pressure sensor using functional link artificial neural networks. ISA transactions, vol 39. Elsevier, Oxford, pp 15–27

    Google Scholar 

  24. Majhi R, Panda G, Sahoo G (2009) Development and performance evaluation of FLN based model for forecasting of stock markets. Expert Syst Appl 36:6800–6808

    Article  Google Scholar 

  25. Nayak SC, Misra BB, Behera HS (2018). ACFLN: artificial chemical functional link network for prediction of stock market index. Evolv Syst:1–26

  26. Dehuri S, Roy R, Cho SB, Ghosh A (2012) An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. J Syst Softw 85(6):1333–1345

    Article  Google Scholar 

  27. Mili F, Hamdi M (2012) A hybrid evolutionary functional link artificial neural network for data mining and classification. In: 2012 6th international conference on sciences of electronics, technologies of information and telecommunications (SETIT). IEEE, pp 917–924

  28. Patra JC, Bornand C, Meher PK (2009) Laguerre neural network-based smart sensors for wireless sensor networks. In: 2009 IEEE instrumentation and measurement technology conference. IEEE, pp 832–837

  29. Mishra SK, Panda G, Meher S (2009) Chebyshev functional link artificial neural networks for denoising of image corrupted by salt and pepper noise. Int J Recent Trends Eng 1(1):413

    Google Scholar 

  30. Li M, Liu J, Jiang Y, Feng W (2012) Complex-Chebyshev functional link neural network behavioral model for broadband wireless power amplifiers. IEEE Trans Microw Theory Tech 60(6):1979–1989

    Article  Google Scholar 

  31. Nanda SK, Tripathy DP, Mahapatra SS (2011) Application of Legendre neural network for air quality prediction

  32. Das KK, Satapathy JK (2011) Legendre neural network for nonlinear active noise cancellation with nonlinear secondary path. In: 2011 International conference on multimedia, signal processing and communication technologies. IEEE, pp 40–43

  33. Patra JC, Bornand C (2010) Nonlinear dynamic system identification using Legendre neural network. In: The 2010 international joint conference on neural networks (IJCNN). IEEE, pp 1–7

  34. Colace F, Loia V, Pedrycz W, Tomasiello S (2020) On a granular functional link network for classification. Neurocomputing

  35. Tomasiello S (2011) A functional network to predict fresh and hardened properties of self-compacting concretes. Int J Num Methods Biomed Eng 27(6):840–847

    Article  MATH  Google Scholar 

  36. Tomasiello S (2020) A granular functional network classifier for brain diseases analysis. Comput Methods Biomech Biomed Eng Imaging Vis 8(4):382–388

    Article  Google Scholar 

  37. Briere M, Oosterlinck K, Szafarz A (2015) Virtual currency, tangible return: portfolio diversification with bitcoin. J Asset Manage 16(6):365–373

    Article  Google Scholar 

  38. Corbet S, Meegan A, Larkin C, Lucey B, Yarovaya L (2018) Exploring the dynamic relationships between cryptocurrencies and other financial assets. Econ Lett 165(1):28–34

    Article  Google Scholar 

  39. da Gama Silva PV, Klotzle MC, Pinto AC, Gomes LL (2019) Herding behavior and contagion in the cryptocurrency market. J Behav Exp Finance 22:41–50

    Article  Google Scholar 

  40. Stavroyiannis S, Babalos V (2019) Herding behavior in cryptocurrencies revisited: novel evidence from a TVP model. J Behav Exp Finance 22:57–63

    Article  Google Scholar 

  41. Hasso T, Pelster M, Breitmayer B (2019) Who trades cryptocurrencies, how do they trade it, and how do they perform? Evidence from brokerage accounts. J Behav Exp Finance 23:64–74

    Article  Google Scholar 

  42. Bouri E, Azzi G, Dyhrberg AH (2017a) On the return-volatility relationship in the bitcoin market around the price crash of 2013. Economics 11(2)

  43. Bouri E, Molnar P, Azzi G, Roubaud D, Hagfors LI (2017) On the hedge and safe haven properties of Bitcoin: is it really more than a diversifier? Financ Res Lett 20:192–198

    Article  Google Scholar 

  44. Hanley BP (2013) The false premises and promises of Bitcoin. arXiv preprint arXiv: 1312.2048

  45. Hayes AS (2017) Cryptocurrency value formation: an empirical study leading to a cost of production model for valuing bitcoin. Telemat Inform 34(7):1308–1321

    Article  Google Scholar 

  46. Bariviera AF, Basgall MJ, Hasperue W, Naiouf M (2017) Some stylized facts of the Bitcoin market. Phys A 484(2):82–90

    Article  Google Scholar 

  47. Katsiampa P (2017) Volatility estimation for Bitcoin: a comparison of GARCH models. Econ Lett 158:3–6

    Article  MathSciNet  MATH  Google Scholar 

  48. Kristjanpoller W, Bouri E (2019) Asymmetric multifractal cross-correlations between the main world currencies and the main cryptocurrencies. Phys A 523:1057–1071

    Article  Google Scholar 

  49. Kristoufek L (2013) BitCoin meets Google Trends and Wikipedia: quantifying the relationship between phenomena of the Internet era. Sci Rep 3:3415

    Article  Google Scholar 

  50. Dyhrberg AH (2016) Bitcoin, gold and the dollar—a GARCH volatility analysis. Finance Res Lett 16(2):85–92

    Article  Google Scholar 

  51. Amjad M, Shah D (2017) Trading bitcoin and online time series prediction. In: NIPS 2016 time series workshop, pp 1–15

  52. Giudici P, Abu-Hashish I (2019) What determines bitcoin exchange prices? A network VAR approach. Finance Res Lett 28(1):309–318

    Article  Google Scholar 

  53. Ciaian P, Rajcaniova M, Kancs D (2016) The economics of BitCoin price formation. Appl Econ 48(19):1799–1815

    Article  Google Scholar 

  54. Sun X, Liu M, Sima Z (2018) A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Res Lett

  55. Mallqui DC, Fernandes RA (2019) Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques. Appl Soft Comput 75:596–606

    Article  Google Scholar 

  56. Rout AK, Biswal B, Dash PK (2014) A hybrid FLANN and adaptive differential evolution model for forecasting of stock market indices. Int J Knowl Based Intell Eng Syst 18(1):23–41

    Google Scholar 

  57. Chakravarty S, Dash PK (2012) A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices. Appl Soft Comput 12(2):931–941

    Article  Google Scholar 

  58. Nayak SC, Misra BB, Behera HS (2012) Index prediction with neuro-genetic hybrid network: a comparative analysis of performance. In: 2012 International conference on computing, communication and applications. IEEE, pp 1–6

  59. Chakravarty S, Dash PK, Pandi VR, Panigrahi BK (2013) An evolutionary functional link neural fuzzy model for financial time series forecasting. In: Modeling applications and theoretical innovations in interdisciplinary evolutionary computation. IGI Global, pp 189–205

  60. Rout AK, Bisoi R, Dash PK (2015) A low complexity evolutionary computationally efficient recurrent Functional link Neural Network for time series forecasting. In: 2015 IEEE power, communication and information technology conference (PCITC). IEEE, pp 576–582

  61. Parida AK, Bisoi R, Dash PK, Mishra S (2015) Financial time series prediction using a hybrid functional link fuzzy neural network trained by adaptive unscented Kalman filter. In: 2015 IEEE power, communication and information technology conference (PCITC). IEEE, pp 568–575

  62. Sahu KK, Sahu SR, Nayak SC, Behera HS (2016) Forecasting foreign exchange rates using CRO based different variants of FLANN and performance analysis. Int J Comput Syst Eng 2(4):190–208

    Article  Google Scholar 

  63. Sahu KK, Biswal GR, Sahu PK, Sahu SR, Behera HS (2015) A CRO based FLANN for forecasting foreign exchange rates using FLANN. Computational intelligence in data mining, vol 1. Springer, New Delhi, pp 647–664

    Google Scholar 

  64. Anish CM, Majhi B (2016) Hybrid nonlinear adaptive scheme for stock market prediction using feedback FLANN and factor analysis. J Korean Stat Soc 45(1):64–76

    Article  MathSciNet  MATH  Google Scholar 

  65. Nayak SC, Misra BB, Behera HS (2014) Impact of data normalization on stock index forecasting. Int J Comput Inf Syst Ind Manag Appl 6(2014):257–269

    Google Scholar 

Download references

Funding

Not applicable, No funding available.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, CMR College of Engineering and Technology, Hyderabad, 501401, India

    Sarat Chandra Nayak

Authors
  1. Sarat Chandra Nayak
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Sarat Chandra Nayak.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nayak, S.C. Bitcoin closing price movement prediction with optimal functional link neural networks. Evol. Intel. 15, 1825–1839 (2022). https://doi.org/10.1007/s12065-021-00592-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-021-00592-z

Keywords