Deep Learning for Asset Bubbles Detection

Oksana Bashchenko and Alexis Marchal
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Oksana Bashchenko: HEC Lausanne; Swiss Finance Institute
Alexis Marchal: EPFL; SFI

No 20-08, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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.

Keywords: Bubbles; Strict local martingales; High-frequency data; Deep learning; LSTM (search for similar items in EconPapers)
JEL-codes: C22 C45 C58 G12 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2020-03
New Economics Papers: this item is included in nep-big and nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2008

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