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...

Which trading strategies differentiate skilled mutual fund managers from their unsuccessful peers? This study provides evidence for a positive association between holdings' implied cost of capital (ICC) and future fund performance. Consistent with large transaction costs of ICC-based investments...

We provide a new monthly cross-sectional measure of stock market tail risk, defined as the average of the daily cross-sectional tail risk, rather than the tail risk of the pooled daily returns within a month. The former better captures monthly tail risk rather than merely the tail risk on...

This paper shows that the equity premium is predictable out of sample when we use a predictive regression that conditions on a large set of economic fundamentals, subject to: (i) economic constraints on the sign of coefficients and return forecasts, and (ii) statistical constraints imposed by...

Asset allocation is critically dependent on the ability to forecast the equity risk premium (ERP) out-of-sample. But, is superior econometric predictability across the business cycle synonymous to predictability at all times? We evaluate recently introduced ERP forecasting models, which have...

I document significant time variation and predictability in a set of risk factors based on technical indicators. As these indicators are primarily designed to detect trends in asset prices, these findings imply substantial time series momentum in factor risk premiums. Specifically, risk premiums...

This paper evaluates the performance of machine learning methods in forecasting stock returns. Compared to a linear benchmark model, interactions and non-linear effects help improve predictive performance. But machine learning models must be adequately trained and tuned to overcome the high...

This paper studies how the stock market perceives and prices cyber risk. To estimate the ex-ante likelihood that a firm will experience a cyber attack, we apply cross-validated logistic LASSO regressions to a set of firm and industry characteristics along with an estimate of a firm's...

A large set of macroeconomic variables have been suggested as equity risk premium predictors in the literature. This paper proposes a forecasting approach for the equity risk premium with two novel features. First, individual month-ahead forecasts are obtained from parsimonious threshold...