This paper tests whether the “disposition effect,” that is the tendency of investors to ride losses and realize gains, induces “underreaction” to news, leading to return predictability. I use data on mutual fund holdings to construct a new measure of reference purchasing prices for individual stocks, and I show that post‐announcement price drift is most severe whenever capital gains and the news event have the same sign. The magnitude of the drift depends on the capital gains (losses) experienced by the stock holders on the event date. An event‐driven strategy based on this effect yields monthly alphas of over 200 basis points.

We use mutual fund flows as a measure of individual investor sentiment for different stocks, and find that high sentiment predicts low future returns. Fund flows are dumb money–by reallocating across different mutual funds, retail investors reduce their wealth in the long run. This dumb money effect is related to the value effect: high sentiment stocks tend to be growth stocks. High sentiment also is associated with high corporate issuance, interpretable as companies increasing the supply of shares in response to investor demand.

Many financial instruments are designed with embedded leverage, such as options and leveraged exchange-traded funds (ETFs). Embedded leverage alleviates investors’ leverage constraints, and, therefore, we hypothesize that embedded leverage lowers required returns. Consistent with this hypothesis, we find empirically that options and leveraged ETFs provide significant amounts of embedded leverage; this embedded leverage increases return volatility in proportion to the embedded leverage; and higher embedded leverage is associated with lower risk-adjusted returns. The results are statistically and economically significant, and we provide extensive robustness tests and discuss the broader implications of embedded leverage for financial economics.

We present a model with leverage and margin constraints that vary across investors and time. We find evidence consistent with each of the model's five central predictions: (1) Because constrained investors bid up high-beta assets, high beta is associated with low alpha, as we find empirically for US equities, 20 international equity markets, Treasury bonds, corporate bonds, and futures. (2) A betting against beta (BAB) factor, which is long leveraged low-beta assets and short high-beta assets, produces significant positive risk-adjusted returns. (3) When funding constraints tighten, the return of the BAB factor is low. (4) Increased funding liquidity risk compresses betas toward one. (5) More constrained investors hold riskier assets.

This paper finds evidence of return predictability across economically linked firms. We test the hypothesis that in the presence of investors subject to attention constraints, stock prices do not promptly incorporate news about economically related firms, generating return predictability across assets. Using a data set of firms' principal customers to identify a set of economically related firms, we show that stock prices do not incorporate news involving related firms, generating predictable subsequent price moves. A long–short equity strategy based on this effect yields monthly alphas of over 150 basis points.

We define quality as characteristics that investors should be willing to pay a higher price for. Theoretically, we provide a tractable valuation model that shows how stock prices should increase in their quality characteristics: profitability, growth, and safety. Empirically, we find that high-quality stocks do have higher prices on average but not by a large margin. Perhaps because of this puzzlingly modest impact of quality on price, high-quality stocks have high risk-adjusted returns. Indeed, a quality-minus-junk (QMJ) factor that goes long high-quality stocks and shorts low-quality stocks earns significant risk-adjusted returns in the United States and across 24 countries. The price of quality varies over time, reaching a low during the internet bubble, and a low price of quality predicts a high future return of QMJ. Analysts’ price targets and earnings forecasts imply systematic quality-related errors in return and earnings expectations.

The size premium has been accused of having a weak historical record, being meager relative to other factors, varying significantly over time, weakening after its discovery, being concentrated among microcap stocks, residing predominantly in January, relying on price-based measures, and being weak internationally. We find, however, that these challenges disappear when controlling for the quality, or its inverse, junk, of a firm. A significant size premium emerges, which is stable through time, robust to specification, not concentrated in microcaps, more consistent across seasons, and evident for non-price-based measures of size, and these results hold in 30 different industries and 24 international equity markets. The resurrected size effect is on par with anomalies such as value and momentum in terms of economic significance and gives rise to new tests of, and challenges for, existing asset pricing theories.

We study the impact of social networks on agents’ ability to gather superior information about firms. Exploiting novel data on the educational background of sell‐side analysts and senior corporate officers, we find that analysts outperform by up to 6.60% per year on their stock recommendations when they have an educational link to the company. Pre‐Reg FD, this school‐tie return premium is 9.36% per year, while post‐Reg FD it is nearly zero. In contrast, in an environment that did not change selective disclosure regulation (the U.K.), the school‐tie premium is large and significant over the entire sample period.

Management for helpful comments. We also thank Nick Kennedy, Stephen Wilson, Laura Dutson, Matthew Healey, Meng Ning, Courtney Stone, and Bennett Surajat for excellent research assistance. We are grateful to BoardEx and Linda Cechova for providing firm board data, Morningstar and Annette Larson for providing mutual fund data, and to the Chicago GSB Initiative on Global Markets for financial support. This paper uses social networks to identify information transfer in security markets. We focus on connections between mutual fund managers and corporate board members via shared education networks. We find that portfolio managers place larger bets on firms they are connected to through their network, and perform significantly better on these holdings relative to their non-connected holdings. A replicating portfolio of connected stocks outperforms a replicating portfolio of non-connected stocks by up to 7.8 % per year. Returns are concentrated around corporate news announcements, consistent with mutual fund managers gaining an informational advantage through the education networks. Our results

We test whether the low-risk effect is driven by leverage constraints and, thus, risk should be measured using beta versus behavioral effects and, thus, risk should be measured by idiosyncratic risk. Beta depends on volatility and correlation, with only volatility related to idiosyncratic risk. We introduce a new betting against correlation (BAC) factor that is particularly suited to differentiate between leverage constraints and behavioral explanations. BAC produces strong performance in the US and internationally, supporting leverage constraint theories. Similarly, we construct the new factor SMAX to isolate lottery demand, which also produces positive returns. Consistent with both leverage and lottery theories contributing to the low-risk effect, we find that BAC is related to margin debt while idiosyncratic risk factors are related to sentiment.