The returns of stocks are partially driven by changes in their expected cashflow. Using revisions in analyst earnings forecasts, we construct an analyst earnings beta that measures the covariance between the cashflow innovations of an asset and those of the market. A higher analyst earnings beta implies greater sensitivity to marketwide revisions in expected cashflow, and therefore higher systematic risk. Our analyst earnings beta captures exposure to macroeconomic fluctuations and has a positive risk premium that provides a partial explanation for the value premium, size premium, and long-term return reversals. From 1984 to 2005, 55.1% of the return variation across book-to-market, size, and long-term return reversal portfolios is captured by their analyst earnings betas.

We argue that the empirical evidence against the capital asset pricing model (CAPM) based on stock returns does not invalidate its use for estimating the cost of capital for projects in making capital budgeting decisions. Because stocks are backed not only by projects in place, but also by the options to modify current projects and undertake new ones, the expected returns on stocks need not satisfy the CAPM even when expected returns of projects do. We provide empirical support for our arguments by developing a method for estimating firms' project CAPM betas and project returns. Our findings justify the continued use of the CAPM by firms in spite of the mounting evidence against it based on the cross section of stock returns.

We find the disparity between long-term and short-term analyst forecasted earnings growth is a robust predictor of future returns and long-term analyst forecast errors. After adjusting for industry characteristics, stocks whose long-term earnings growth forecasts are far above or far below their implied short-term forecasts for earnings growth have negative and positive subsequent risk-adjusted returns along with downward and upward revisions in long-term forecasted earnings growth, respectively. Additional results indicate that investor inattention toward firm-level changes in long-term earnings growth is responsible for these risk-adjusted returns.

Hedging short gamma exposure requires trading in the direction of price movements, thereby creating price momentum. Using intraday returns on over 60 futures on equities, bonds, commodities, and currencies between 1974 and 2020, we find strong market intraday momentum everywhere. The return during the last 30 minutes before the market close is positively predicted by the return during the rest of the day (from previous market close to the last 30 minutes). The predictive power is economically and statistically highly significant, and reverts over the next days. We provide novel evidence that links market intraday momentum to the gamma hedging demand from market participants such as market makers of options and leveraged ETFs.

Short selling efficiency (SSE), measured each month by the slope coefficient of cross-sectionally regressing abnormal short interest on a mispricing score, significantly and negatively predicts stock market returns both in-sample and out-of-sample, suggesting that mispricing gets corrected after short sales are executed on the right stocks. We show conceptually and empirically that SSE has favorable predictive ability over aggregate short interest, as SSE reduces the effect of noises in short interest and better captures the amount of aggregate short selling capital devoted to overpricing. The predictive power is stronger during the periods of recession, high volatility, and low public information. In addition, low SSE precedes the months when the CAPM performs well and signals an efficient market. Overall, our evidence highlights the importance of the disposition of short sales in stock markets.

Using novel data from a crowdsourcing platform for ranking stocks, we investigate how investors form expectations about stock returns over the next week. We find that investors extrapolate from stocks’ recent past returns, with more weight on more recent returns, especially when recent returns are negative, salient, or from a dispersed cross-section. Such extrapolative beliefs are stronger among nonprofessionals and large stocks. Moreover, consensus rankings negatively predict returns over the next week, more so among stocks with low institutional ownership and a high degree of extrapolation. A trading strategy that sorts stocks on investor beliefs generates an economically significant profit.

Frequent, yet uninformed, market timing recommendations by a financial advisory firm generate significant flows for Chilean pension funds. These flows induce substantial changes in the Chilean foreign exchange rate due to the funds’ high allocation to international securities. Local banks provide liquidity to pension funds in the spot market and their hedging transactions propagate the demand fluctuations from the spot to the forward market, resulting in deviations from covered interest rate parity. Using bank balance sheet data, we confirm that banks’ risk bearing constraints create limits to arbitrage.

We construct a monthly presidential economic approval rating (PEAR) index from 1981 to 2019, by averaging ratings on the president’s handling of the economy across various national polls. In the cross-section, stocks with high betas to changes in the PEAR index significantly under-perform those with low betas by 1.00% per month in the future, on a risk-adjusted basis. The low PEAR beta premium persists up to one year, and is present in various sub-samples and even in other G7 countries. PEAR beta dynamically reveals a firm’s perceived alignment to the incumbent president’s economic policies and investors seem to misprice such an alignment.