Deep Learning for Asset Bubbles Detection
This work addresses asset bubble detection for financial markets, offering a novel method that improves accuracy and demonstrates practical profitability, though it is incremental as it builds on existing theory and methods.
The authors tackled the problem of detecting asset bubbles by developing a neural network method that estimates the diffusion coefficient of price processes more accurately than existing statistical methods, leading to improved bubble detection and a profitable trading strategy in US equity data from 2006 to 2008.
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.