Using information in returns, we identify the stochastic process of consumption. We find that aggregate consumption reacts over multiple quarters to innovations spanned by financial markets. This persistent component accounts for over a quarter of consumption variation. These shocks command a large and significant risk premium, driving a large share of stocks' and a small yet significant fraction of bonds' time‐series variation. Nevertheless, we find no support for stochastic volatility of consumption driving time‐varying risk premia. Finally, an otherwise standard recursive utility model based on our estimated process explains equity premium and risk‐free rate puzzles with low‐risk aversion.

We build cross‐sections of asset returns for a given set of characteristics, that is, managed portfolios serving as test assets, as well as building blocks for tradable risk factors. We use decision trees to endogenously group similar stocks together by selecting optimal portfolio splits to span the stochastic discount factor, projected on individual stocks. Our portfolios are interpretable and well diversified, reflecting many characteristics and their interactions. Compared to combinations of dozens (even hundreds) of single/double sorts, as well as machine‐learning prediction‐based portfolios, our cross‐sections are low‐dimensional yet have up to three times higher out‐of‐sample Sharpe ratios and alphas.

We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high‐dimensional problems. For a (potentially misspecified) stand‐alone model, it provides reliable price of risk estimates for both tradable and nontradable factors, and detects those weakly identified. For competing factors and (possibly nonnested) models, the method automatically selects the best specification— if a dominant one exists—or provides a Bayesian model averaging–stochastic discount factor (BMA‐SDF), if there is no clear winner. We analyze 2.25 quadrillion models generated by a large set of factors and find that the BMA‐SDF outperforms existing models in‐ and out‐of‐sample.

We document a rapid increase in retail trading in options in the United States. Facilitated by payment for order flow (PFOF) from wholesalers executing retail orders, retail trading recently reached over 60% of total market volume. Nearly 90% of PFOF comes from three wholesalers. Exploiting new flags in transaction‐level data, we isolate wholesaler trades and build a novel measure of retail options trading. Our measure comoves with equity‐based retail activity proxies and drops significantly during U.S. brokerage platform outages and trading restrictions. Retail investors prefer cheaper, weekly options with average bid‐ask spread of 12.6%, and lose money on average.