We use a large cross-section of equity returns to estimate a rich affine model of equity prices, dividends, returns and their dynamics. Using the model, we price dividend strips of the aggregate market index, as well as any other well-diversified equity portfolio. We do not use any dividend...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

On paper, momentum is one of the most compelling factors: simulated portfolios based on momentum add remarkable value, in most time periods and in most asset classes, all over the world. So, our title may seem unduly provocative. However, live results for mutual funds that take on a momentum...

Value stocks outperform growth stocks. The academic literature provides two competing interpretations on what drives the value premium: exposure to risk factors or mispricing of securities. Existing empirical studies, which are largely based on U.S. data, have not conclusively rejected one...

We challenge the common view that smart beta strategies and factor tilts are equivalent. Initially, the term “smart beta” referred to strategies that broke the link between the price of a stock and its weight in the portfolio or index. Capitalization weighting does not do that — neither...

In our paper — “How Can ‘Smart Beta' Go Horribly Wrong?” — we show, using U.S. data, that the relative valuation of a strategy (in comparison with its own historical norms) is correlated with the strategy's subsequent return at a five-year horizon. The high past performance of many of...

Stock momentum, long-term reversal, and other past return characteristics that predict future returns also predict future realized betas, suggesting these characteristics capture time-varying risk compensation. We formalize this argument with a conditional factor pricing model. Using...