We provide a comprehensive analysis of the timing success for equity risk factors. Our analysis covers over 300 risk factors (factor zoo) and a high dimensional set of predictors. The performance of almost all groups of factors can be improved through timing, with improvements being highest for profitability and value factors. Past factor returns and volatility stand out as the most successful individual predictors of factor returns. However, both are dominated by aggregating many predictors using partial least squares. The median improvement of a timed vs. untimed factor is about 2% p.a. A timed multifactor portfolio leads to a 20% increase in return relative to its untimed counterpart.

We investigate the effect of ETF ownership on stock market anomalies and market efficiency. We find that low ETF ownership stocks exhibit higher returns, greater Sharpe ratios, and highly significant alphas in comparison to high ETF ownership stocks. We show that high ETF ownership stocks demonstrate more pronounced information flows than low ETF ownership stocks which reduces their mispricing as they are more informationally efficient. We find similar results when we match the two groups based on size, volume, book-to-market, and momentum. Our results are robust to different matching methods and to a wide array of controls in Fama-MacBeth regressions. Using Russell index reconstitution, we find causal evidence that ETF ownership attenuates anomaly returns.

We hypothesize that when price correction requires more capital than any one investor can provide, institutions coordinate trading via crowd-sourcing in the media. When the crowd reaches a consensus, synchronized trading occurs, prices are corrected, and anomaly returns result. We use over one million Wall Street Journal articles from 1980 to 2020 to develop a novel textual measure of institutional investors making predictions in the media (InstPred). We show that (i) both value and momentum anomaly returns are 34% to 63% larger when InstPred is higher; (ii) these effects are driven by stocks whose institutional investors are highly cited in WSJ articles; and (iii) institutional investors collectively trade the anomalies more aggressively when InstPred is higher. Our results cannot be explained by existing measures such as document tone.

This paper a develops novel statistical test of whether individual factor risk premia

are identified from return data in multi-factor models. We give a necessary and

sufficient condition for population identification of individual risk premia, which we

call the kernel-orthogonality condition. This condition is weaker than the standard

rank condition commonly assumed for linear factor models. Under misspecification,

our condition ensures point identification of the risk premium with minimal pricing

error. We show how to test this restriction directly in reduced-rank models. Finally,

we apply our test methodology to assess identification of risk premia associated with

consumption growth and intermediary leverage.

We document that the carry of crypto futures, i.e. the difference between futures and spot prices, can become very large (up to 60% p.a.) and varies strongly over time. This behavior is most consistent with the existence of a highly volatile crypto convenience yield that stems from two main forces: (i) trend-chasing and attention by smaller investors seeking leveraged upside exposure to crypto assets in boom periods, and (ii) the relative scarcity of "arbitrage" capital taking the other side through a cash and carry position. Engaging in the latter is risky due to spikes in margins and liquidations amid drawdowns. The interplay between these two forces, and the involved high leverage, may help explain why severe market crashes are a frequent feature of crypto markets.

We show that investors price immediate, short-term stock market outcomes very different from outcomes that occur further into the future. To this end, we introduce the forward pricing kernel to decompose long-term pricing kernels into short-term and forward pricing kernels. Using index options, we find that kernels with maturities up to twelve months are U-shaped, and show that this results from the shape of the one-month pricing kernel. Once we remove the impact of the one-month kernel, we show that forward kernels are in line with standard long-run risk models in terms of their shape, level and time-series variation.

Equity duration is a measure of discount-rate sensitivity that is driven by both, stock-specific cash-flow timing and stock-specific discount-rate levels. Established measures of equity duration using market-price information derive their predictive power for returns from using market-implied discount rates. We introduce new measures of pure cash-flow timing which disentangle discount-rate level from cash-flow timing information. Our results indicate an unconditionally flat relationship between timing and average returns. However, it turns out that in recessions (expansion episodes), there is a negative (positive) relation between cash-flow timing and average stock returns.

We study U.S. house price movements using a variance decomposition based on subjective expectations data from the University of Michigan’s Survey of Consumers. Contrary to previous VAR-based models for rational expectations, we find that households’ subjective cash flow (income) expectations account for the dominant share of the overall variation in house prices, whereas subjective discount rate (return) expectations are insignificant. We also show that households’ ex post forecast errors and ex ante belief distortions, defined as the difference between subjective and rational expectations, are persistent and associated with housing data, macroeconomic views of households, and credit conditions.

Information arrives at securities markets through price quotes and trades. Informed traders impose adverse-selection costs on quote suppliers. This creates incentives for the latter to identify relatively uninformed groups and trade with them off-exchange. The marketplace turns hybrid, at the cost of thinner, highly informed (toxic) volume at the center. This pattern has largely eluded econometricians, because the standard approach to measuring information shares is biased against finding it. We show why this is the case, and design a bias-free approach. The novel approach shows that, indeed, the conjectured pattern is strongly present in the data.

Trading at decentralized exchanges (DEXs) requires traders to bid blockchain fees to determine the execution priority of their orders. We employ a structural vector-autoregressive (structural VAR) model to provide evidence that DEX trades with high fees not only reveal more private information, but also respond more to public price innovations on centralized exchanges (CEXs), contributing to price discovery. Using a unique dataset of Ethereum mempool orders, we further demonstrate that high fees do not result from traders competing with each other on private or public information. Rather, our analysis lends support to the hypothesis that they bid high fees to reduce the execution risk of their orders due to blockchain congestion.