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Financial Data and Trend Prediction

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Encyclopedia of Big Data

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Synonyms

Financial econometrics; Machine learning; Pattern recognition; Risk analysis; Time series; Financial forecasting

Introduction

The prediction of financial time series is the primary object of study in the area of financial econometrics. The first step from this perspective is to separate any systematic variation of these series from their random movements. Systematic changes can be caused by trends and seasonal and cyclical variations. Econometric models include different levels of complexity to simulate the existence of these diverse patterns. However, machine-learning algorithms can be used to forecast nonlinear time series as they can learn and evolve jointly with the financial markets.

The most standard econometric approach to forecast trends of financial time series is the Box and Jenkins (1970) methodology. This approach has three major steps: (1) identify the relevant systematic variations of the time series (trend, seasonal or cyclical effects), the input variables,...

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Further Readings

  • Allen, F., & Karjalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51(2), 245–271.

    Article  Google Scholar 

  • Box, G. Y., Jenkins, G. (1970). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.

    Google Scholar 

  • Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–215.

    Article  Google Scholar 

  • Creamer, G. (2012). Model calibration and automated trading agent for euro futures. Quantitative Finance, 12(4), 531–545.

    Article  Google Scholar 

  • Freund, Y., & Schapire, R. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119–139.

    Article  Google Scholar 

  • Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer-Verlag.

    Book  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Business, Stevens Institute of Technology, Hoboken, NJ, USA

    Germán G. Creamer

Authors
  1. Germán G. Creamer
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Corresponding author

Correspondence to Germán G. Creamer .

Editor information

Editors and Affiliations

  1. School of Policy, Government and International Affairs, George Mason University, Fairfax, Virginia, USA

    Laurie A. Schintler

  2. School of Policy, Government and International Affairs, George Mason University, Fairfax, Virginia, USA

    Connie L. McNeely

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Creamer, G.G. (2018). Financial Data and Trend Prediction. In: Schintler, L., McNeely, C. (eds) Encyclopedia of Big Data. Springer, Cham. https://doi.org/10.1007/978-3-319-32001-4_95-1

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  • DOI: https://doi.org/10.1007/978-3-319-32001-4_95-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32001-4

  • Online ISBN: 978-3-319-32001-4

  • eBook Packages: Springer Reference Business and ManagementReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

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