Skip to main content
SpringerLink
Log in

Neural Networks and Deep Learning

  • Chapter
  • First Online:
Book cover Statistics of Financial Markets

Part of the book series: Universitext ((UTX))

Abstract

Deep learning is a group of optimisation methods for artificial neural networks. The field consists of three major branches.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

References

  • Abberger, K. (1997). Quantile smoothing in financial time series. Statistical Papers, 38, 125–148.

    Article  MathSciNet  Google Scholar 

  • Anders, U. (1997). Statistische neuronale Netze. München: Vahlen.

    Google Scholar 

  • Back, A., & Tsoi, A.C. (1991). Fir and iir synapses, a new neural network architecture for time series modeling. Neural Computation, Massachusetts Institute of Technology, 3, 375–385.

    Article  Google Scholar 

  • Ben Taieb, S., Sorjamaa, A., & Bontempi, G. (2010). Multiple-output modeling for multi-step-ahead time series forecasting. Neurocomputing, 73(10–12), 1950–1957.

    Article  Google Scholar 

  • Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166.

    Article  Google Scholar 

  • Bol, G., Nakhaeizadeh, G., & Vollmer, K.-H. (1996). Finanzmarktanalyse und -prognose mit innovativen quantitativen Verfahren. Heidelberg: Physica-Verlag.

    Book  Google Scholar 

  • Chen, S., Chen, C. Y.-H., Härdle, W. K., Lee, T., & Ong, B. (2017). Crix index: Analysis of a cryptocurrency index for portfolio investment. In D. Lee Kuo Chen, & R. Deng (Eds.), Handbook of digital finance and financial inclusion: Cryptocurrency, FinTech, InsurTech, Regulation, ChinaTech, Mobile Security, and Distributed Ledger. 1st Edition.

    Google Scholar 

  • Eisl, A., Gasser, S. M., & Weinmayer, K. (2015). Caveat emptor: Does Bitcoin improve portfolio diversification? SSRN Scholarly Paper ID 2408997. Rochester, NY: Social Science Research Network.

    Google Scholar 

  • Elendner, H., Trimborn, S., Ong, B., & Lee, T. M. (2018). The cross-section of cryptocurrencies as financial assets: Investing in crypto-currencies beyond bitcoin. In D. Lee Kuo Chen, & R. Deng (Eds.), Handbook of digital finance and financial inclusion: Cryptocurrency, FinTech, InsurTech, Regulation, ChinaTech, Mobile Security, and Distributed Ledger. 1st Edition.

    Google Scholar 

  • Elman, J. (1990). Finding structure in time. Cognitive Science, University of California, San Diego, 14, 179–211.

    Google Scholar 

  • Fan, J., & Yao, Q. (1998). Efficient estimation of conditional variance functions in stochastic regression. Biometrika, 85, 645–660.

    Article  MathSciNet  Google Scholar 

  • Franke, J. (1999). Nonlinear and nonparametric methods for analyzing financial time series. In P. Kall, & H.-J. Luethi (Eds.), Operation research proceedings 98. Heidelberg: Springer-Verlag.

    Google Scholar 

  • Franke, J. (2000). Portfolio management and market risk quantification using neural networks. Statistics and finance: An interface. Imperial College Press: London.

    Google Scholar 

  • Franke, J., & Diagne, M. (2006). Estimating market risk with neural networks. Statistics and Decisions, 24, 233–253.

    MathSciNet  MATH  Google Scholar 

  • Franke, J., Kreiss, J., Mammen, E., & Neumann, M. (2003). Properties of the nonparametric autoregressive bootstrap. Journal of Time Series Analysis, 23, 555–585.

    Article  MathSciNet  Google Scholar 

  • Franke, J., & Neumann, M. (2000). Bootstrapping neural networks. Neural Computation, 12, 1929–1949.

    Article  Google Scholar 

  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

    MATH  Google Scholar 

  • Haykin, S. (1999). Neural networks: a comprehensive foundation. Prentice-Hall.

    MATH  Google Scholar 

  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, Massachusetts Institute of Technology, 9(8), 1735–1780.

    Article  Google Scholar 

  • Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359–366.

    Article  Google Scholar 

  • Jordan, M. (1986). Serial order: A parallel distributed processing approach. Technical Report, Institute for Cognitive Science, University of California, San Diego, 8604.

    Google Scholar 

  • Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46, 33–50.

    Article  MathSciNet  Google Scholar 

  • Müller, T., & Nietzer, H. (1993). Das große Buch der technischen Indikatoren. TM Börsenverlag.

    Google Scholar 

  • Murata, N., Yoskizawa, S., & Amari, S. (1994). Network information criterion - determining the number of hidden units for an artificial neural network model. IEEE Trans. Neural Networks, 5, 865–872.

    Article  Google Scholar 

  • Pascanu, R., Mikolov, T., & Bengio, Y. (2013). On the difficulty of training recurrent neural networks. In S. Dasgupta, & D. McAllester (Eds.), Proceedings of the 30th international conference on machine learning, Vol. 28(3) of Proceedings of machine learning research (pp. 1310–1318). Atlanta, Georgia, USA: PMLR.

    Google Scholar 

  • Refenes, A.-P. (1995a). Neural networks for pattern recognition. Clarendon Press.

    Google Scholar 

  • Refenes, A.-P. (1995b). Neural networks in the capital market. Wiley, New York.

    Google Scholar 

  • Rehkugler, H., & Zimmermann, H. G. (1994). Neuronale Netze in der Ökonomie. München: Vahlen.

    Google Scholar 

  • Ripley, B. (1996). Pattern recognition and neural networks. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Rojas, R. (1996). Neural networks: A systemic introduction. Springer.

    Book  Google Scholar 

  • Rüeger, S., & Ossen, A. (1997). The metric structure of weightspace. Neural Processing Letters, 5, 63–72.

    Google Scholar 

  • Teräsvirta, T., Lin, C.-F., & Granger, C. (1993). Power of the neural network linearity test. Journal of Time Series analysis, 14, 209–220.

    Article  Google Scholar 

  • Trimborn, S., & Härdle, W. K. (2018). CRIX an Index for cryptocurrencies. Journal of Empirical Finance, 49, 107–122.

    Article  Google Scholar 

  • Trimborn, S., Li, M., & Härdle, W. (2019). Investing with cryptocurrencies - a liquidity constrained investment approach. Journal of Financial Econometrics (forthcoming)

    Google Scholar 

  • Waibel, A., Hanazawa, T., G., H., Shikano, K., & Lang, K. (1989). Phoneme recognition using time-delay neural networks. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37(3), 328–339.

    Google Scholar 

  • Welcker, J. (1994). Technische Aktienanalyse. Zürich: Verlag Moderne Industrie.

    Google Scholar 

  • White, H. (1989a). An additional hidden unit test for neglected nonlinearities in multilayer feedforward networks. In Proceedings of the International Joint Conference on Neural Networks, Zürich, Washington DC.

    Google Scholar 

  • White, H. (1989b). Some asymptotic results for learning in single hidden-layer feedforward network models. Journal of the American Statistical Association, 84, 1008–1013.

    Article  MathSciNet  Google Scholar 

  • White, H. (1990). Connectionist nonparametric regression: multilayer feedforward networks can learn arbitrary mappings. Neural Networks, 3, 535–550.

    Article  Google Scholar 

  • Williams, R., & Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural Computation, Massachusetts Institute of Technology, 1, 270–280.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, Technische Universität Kaiserslautern, Kaiserslautern, Germany

    Jürgen Franke

  2. Ladislaus von Bortkiewicz Chair of Statistics, Humboldt-Universität Berlin, Berlin, Germany

    Wolfgang Karl Härdle

  3. Louvain Institute of Data Analysis and Modeling in Economics and Statistics, UCLouvain, Louvain-la-Neuve, Belgium

    Christian Matthias Hafner

Authors
  1. Jürgen Franke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wolfgang Karl Härdle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christian Matthias Hafner
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Franke, J., Härdle, W.K., Hafner, C.M. (2019). Neural Networks and Deep Learning. In: Statistics of Financial Markets. Universitext. Springer, Cham. https://doi.org/10.1007/978-3-030-13751-9_19

Download citation

Publish with us

Policies and ethics

Search

Navigation