论文标题
资产气泡检测深度学习
Deep Learning for Asset Bubbles Detection
论文作者
论文摘要
我们开发了一种使用神经网络检测资产气泡的方法。我们在连续时间中依靠本地群众的理论，并使用深层网络比当前的估计器更准确地估计价格过程的扩散系数，从而改善了气泡的检测。我们显示了使用模拟数据创建的实验室中现有统计方法的算法的表现。然后，我们将网络分类应用于真实数据并建立零净敞口交易策略，从2006年至2008年，从美国股票市场中的气泡出现的风险套利构成。该策略的盈利能力估计了气泡的经济幅度以及对理论假设的支持。
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.
