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Deep Learning for Asset Bubbles Detection

Author

Listed:
  • Oksana Bashchenko

    (HEC Lausanne; Swiss Finance Institute)

  • Alexis Marchal

    (EPFL; SFI)

Abstract

We develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection of bubbles. We show the outperformance of our algorithm over the existing statistical method in a laboratory created with simulated data. We then apply the network classification to real data and build a zero net exposure trading strategy that exploits the risky arbitrage emanating from the presence of bubbles in the US equity market from 2006 to 2008. The profitability of the strategy provides an estimation of the economical magnitude of bubbles as well as support for the theoretical assumptions relied on.

Suggested Citation

  • Oksana Bashchenko & Alexis Marchal, 2020. "Deep Learning for Asset Bubbles Detection," Swiss Finance Institute Research Paper Series 20-08, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2008
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    More about this item

    Keywords

    Bubbles; Strict local martingales; High-frequency data; Deep learning; LSTM;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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