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Deep Learning for Asset Bubbles Detection

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  • Oksana Bashchenko
  • Alexis Marchal

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," Papers 2002.06405, arXiv.org.
    • Handle: RePEc:arx:papers:2002.06405
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    References listed on IDEAS

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    1. Dumas, Bernard & Luciano, Elisa, 2017. "The Economics of Continuous-Time Finance," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262036541, December.
    2. Robert A. Jarrow, 2015. "Asset Price Bubbles," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 201-218, December.
    3. Andersen, Torben G. & Bollerslev, Tim & Dobrev, Dobrislav, 2007. "No-arbitrage semi-martingale restrictions for continuous-time volatility models subject to leverage effects, jumps and i.i.d. noise: Theory and testable distributional implications," Journal of Econometrics, Elsevier, vol. 138(1), pages 125-180, May.
    4. Jarrow, Robert & Protter, Philip, 2012. "Discrete versus continuous time models: Local martingales and singular processes in asset pricing theory," Finance Research Letters, Elsevier, vol. 9(2), pages 58-62.
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