Financial markets provide a natural quantitative lab for understanding some of the most advanced human behaviours. Among them is the invention and use of mathematical tools known as financial instruments. Besides money, the two most fundamental financial instruments are bonds and equities. More...

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...

These days it's become convention (reinforced by the media's treatment of wealth) to assess our net worth by tallying up the market value of our financial assets, even though it's more natural and useful to think of our wealth as a stream of dollars over time given the nature of our income and...

The longest bull market in US stock market history is over. Uncertainty over the public health and economic impact of the coronavirus pandemic will keep markets extremely volatile, making it likely we'll touch a wide range of price levels in the months ahead. Amidst such uncertainty, it's a...

Long-run risk models, a cornerstone in the macro-finance literature for their ability to capture key asset price phenomena, are known to entail implausibly high levels of timing and risk premia. Our paper resolves this puzzle by considering consumption of durable goods in addition to that of...

We propose a new method to improve density forecasts of the equity premium using information from options markets. We obtain predictive densities from stochastic volatility (SV) and GARCH models, which we then tilt using the second moment of the risk-neutral distribution implied by options...

We examine 24 global factor premiums across the main asset classes via replication and new-sample evidence spanning 217 years of data. Replication yields ambiguous evidence within a unified testing framework with methods that account for p-hacking. The new-sample evidence reveals that the large...

This paper investigates bond risk premia in the framework of predictive systems. Different from the traditional linear predictive models, predictive systems allow predictors to be imperfectly correlated with conditional expected returns, and could incorporate prior beliefs on the negative...

We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and...

This study investigates if changes in risk-neutral systematic volatility, skewness, and kurtosis, are priced, either symmetrically or asymmetrically, as systematic risk factors in the cross-section of stock returns. The moments are constructed using options on the S&P 500, and represent...