We identify a common misconception that expected future changes in short-term interest rates predict corresponding future changes in long-term interest rates. People forecast similar shapes for the paths of short and long rates over the next four quarters. This is a mistake because long rates should already incorporate public information about future short rates and do not positively comove with expected changes in short rates. We hypothesize that people group short- and long-term interest rates into the coarse category of “interest rates,” leading to overestimation of their comovement. We show that this categorical thinking persists even among professional forecasters and distorts the real behavior of borrowers and investors. Expectations of rising short rates drive households and firms to rush to lock in long-term debt before further increases in long rates, reducing the effectiveness of forward guidance in monetary policy. Investors sell long-term bonds because they anticipate future increases in long rates. The result- ing increase in supply and decrease in demand for long-term debt cause long rates to overreact to expected changes in short rates, and can help explain the excess volatility puzzle in long rates.

We propose a new model of expected stock returns that incorporates quantity information from market trading activities into the factor pricing framework. We posit that the expected return of a stock is determined by not only its factor risk exposures (beta) but also the factor’s quantity fluctuations (q) induced by trading flows, and hence term the model beta times quantity (BTQ). The rationale is that sophisticated investors should require a greater factor premium when they are more exposed to that factor after noise traders sell lots of stocks with high exposures to that factor. The BTQ model provides a compelling risk-based explanation for stock returns, which is otherwise obscured without considering the quantity information. The cross-sectional risk-return association, which is nearly flat unconditionally, strongly depends on the quantity variable. The structured BTQ model reliably predicts monthly stock returns out of sample, and addresses the factor zoo problem by selecting a small number of factors.

Each quarter, the Treasury Department unveils its refunding plan, detailing the following quarter’s treasury issuances in terms of size and maturity composition. We document substantial positive returns on long-term Treasurys on the day before these Treasury Refunding Announcements (TRAs), a pattern persisting since the 1990s and intensifying over the last two decades amidst growing Federal deficits. These pre-TRA gains are distinct from known end-of-month pricing patterns and account for a sizable fraction of annual yield and term premium changes. Implementing a trading strategy focused solely on these four days per year yields a Sharpe ratio of over 4. We provide evidence of uncertainty reduction and associated information production around TRAs as a potential mechanism. Finally, we discuss implications for some documented bond market patterns and the pre-FOMC drift in the equities market.

When a mutual fund has persistent demand for a priced factor, the fund needs to rebalance its portfolio’s exposure to that factor as stock characteristics change over time. We establish this behavior of “factor rebalancing” and examine its implications for return predictability. We show that factor rebalancing is prevalent in mutual funds’ holding changes, and this behavior poses a source of predictable price pressure that operates independently from the passive trading induced by retail flows. Consistent with factor rebalancing, stocks whose characteristics are misaligned with underlying funds’ factor demand subsequently have lower returns, while wellaligned stocks subsequently have higher returns. We rule out alternative explanations based on private information, skills, and herding.

Slicing an asset by payout horizons unseals information about its future returns and cash flows. As an example, we slice an equity market index into granular pieces (dividend strips) and show that valuation ratios of its strips span the underlying state variables of the index. Strip valuation ratios form a term structure. The level and slope strongly predict the index dividends. The slope alone is sufficient for forecasting the index return. The steepening and flattening of valuation term structure reflect discount-rate variations rather than information on the cash-flow trajectory, because market participants have very limited information about long-term cash flows.

We analyze the impact of rating agencies’ beliefs on credit markets. We measure their beliefs as the difference between their forecasts of aggregate credit spreads and the consensus. When rating agencies become more optimistic, they issue higher ratings even though their forecasts do not predict future credit spreads. This optimism leads to lower initial bond yields and subsequent negative excess returns. Firms respond by increasing their leverage and investment. Finally, rating agencies become more optimistic as their head economists’ property values increase. Our analysis shows how subjective beliefs drive aggregate financing and investment through mispricing in credit markets.

I document a robust pattern in how Treasury market participants’ yield curve expectations respond to new information: forecasts for short-term rates underreact to news while forecasts for long-term rates overreact. I propose a new explanation of this based on ``autocorrelation averaging,’’ whereby, due to limited processing capacity, forecasters’ estimate of the autocorrelation of a given process is biased toward the average autocorrelation of all related processes. Consistent with this view, forecasters overestimate the autocorrelation of the less persistent term-premium component of interest rates and underestimate the autocorrelation of the more persistent short-rate component; a calibrated model quantitatively matches the documented pattern of misreaction. Moreover, banks’ allocations to Treasuries vary positively with their expectations of bond returns and misreaction proxies can strongly predict future short- and long-term bond returns, respectively.

The ratio of long- to short-term dividend prices, “price ratio” ($pr_t$), predicts annual market return with an out-of-sample $R^2$ of 19%, subsuming the predictive power of price-dividend ratio ($pd_t$). After controlling for $pr_t$, $pd_t$ predicts dividend growth with an out-of-sample $R^2$ of 30%. Our results hold outside the U.S. An exponential-affine model shows that the key to our findings is the (lack of) persistence of expected dividend growth. We find the expected return is countercyclical and responds strongly to monetary policy shocks. As implied by ICAPM, shocks to $pr_t$, the expected-return proxy, are priced in the cross-section.

Delegation bears an intrinsic form of uncertainty. Investors hire managers for their superior models of asset markets, but delegation outcome is uncertain precisely because managers’ model is unknown to investors. We model investors’ delegation decision as a trade-off between asset return uncertainty and delegation uncertainty. Our theory explains several puzzles on fund performances. It also delivers asset pricing implications supported by our empirical analysis: (1) because investors partially delegate and hedge against delegation uncertainty, CAPM alpha arises; (2) the cross-section dispersion of alpha increases in uncertainty; (3) managers bet on alpha, engaging in factor timing, but factors’ alpha is immune to the rise of their arbitrage capital - when investors delegate more, delegation hedging becomes stronger. Finally, we offer a novel approach to extract model uncertainty from asset returns, delegation, and survey expectations.