We analyze the joint out-of-sample predictive ability of a comprehensive set of 299 firm characteristics for cross-sectional stock returns. We develop a cross-sectional out-of-sample R2 statistic that provides an informative measure of the accuracy of cross-sectional return forecasts in terms of...

We develop new structural nonparametric methods for estimating conditional asset pricing models using deep neural networks. Our method is guided by economic theory and employs time-varying conditional information on alphas and betas carried by firm-specific characteristics. Contrary to many...

The profitability of a trading system based on the momentum-like effects of price jumps was tested on the time series of 7 assets (EUR/USD, GBP/USD, USD/CHF and USD/JPY exchange rates and Light Crude Oil, E-Mini S&P 500 and VIX Futures), in each case for 7 different frequencies (ranging from...

Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing.I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set...

Models based on factors such as size, value, or momentum are ubiquitous in asset pricing. Therefore, portfolio allocation and risk management require estimates of the volatility of these factors. While realized volatility has become a standard tool for liquid individual assets, this measure is...

We investigate the dynamics of prices, information and expectations in a competitive, noisy, dynamic asset pricing equilibrium model. We show that prices are farther away from (closer to) fundamentals compared with average expectations if and only if traders over- (under-) rely on public...

We combine general equilibrium theory and théorie générale of stochastic processes to derive structural results about equilibrium state prices. -- General equilibrium ; Continuous-time finance ; Théorie générale of stochastic processes ; Asset pricing ; State prices

We explore the term structures of claims to a variety of cash flows: U.S. government bonds (claims to dollars), foreign government bonds (claims to foreign currency), inflation-adjusted bonds (claims to the price index), and equity (claims to future equity indexes or dividends). Average term...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

Forecasting the stock returns in the emerging markets is challenging due to their peculiar characteristics. These markets exhibit linear as well as nonlinear features and Conventional forecasting methods partially succeed in dealing with the nonlinear nature of stock returns. Contrarily,...