This paper presents a dynamic stochastic general equilibrium model of Ricardian business cycles. Our model is Ricardian because countries (or, equivalently, regions) trade to take advantage of their comparative advantages. Their relative efficiencies, however, change over time stochastically....

We introduce the concept of “negative bubbles” as the mirror image of standard financial bubbles, in which positive feedback mechanisms may lead to transient accelerating price falls. To model these negative bubbles, we adapt the Johansen-Ledoit-Sornette (JLS) model of rational expectation...

Purpose – We use a large and rich data set consisting of over 123,000 single-family houses sold in Switzerland between 2005 and 2017 to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models.Design/methodology/approach – We apply six...

In the data mining and machine learning fields, forecasting the direction of price change can be generally formulated as a supervised classfii cation. This paper attempts to predict the direction of daily changes of the Nasdaq Composite Index (NCI) and of the Standard & Poor's 500 Composite...

Predictive power has always been the main research focus of learning algorithms with the goal of minimizing the test error for supervised classification and regression problems. While the general approach for these algorithms is to consider all possible attributes in a dataset to best predict...

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...

We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short- term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps...

The paper studies liquidity provision by institutional investors using trade-level data. We find that hedge fund trades are a more important predictor of stock-level liquidity than mutual fund trades. However, hedge funds' liquidity provision is more exposed to financial conditions than that of...

This paper provides evidence on how the new international regulation on Global Systemically Important Banks (G-SIBs) impacts the market value of large banks. We analyze the stock price reactions for the 300 largest banks from 52 countries across 12 relevant regulatory announcement and...