Momentum in individual stock returns relates to momentum in factor returns. Most factors are positively autocorrelated: the average factor earns a monthly return of six basis points following a year of losses and 51 basis points following a positive year. We find that factor momentum concentrates in factors that explain more of the cross section of returns and that it is not incidental to individual stock momentum: momentum‐neutral factors display more momentum. Momentum found in high‐eigenvalue principal component factors subsumes most forms of individual stock momentum. Our results suggest that momentum is not a distinct risk factor—it times other factors.

Firms move between growth and value because of changes in either size or book value of equity. The value premium is specific to variation in book-to-market that emanates from size changes. A factor based on this variation earns the entire value premium; one based on the remaining variation earns no premium. Hence, not all high book-to-market firms earn the value premium, and some low book-to-market firms earn value-like returns. Many models price portfolios sorted by size and book-to-market. None distinguish firms that earn the value premium from those that have a high book-to-market but do not earn the premium. Received July 22, 2015; editorial decision June 29, 2017 by Editor Andrew Karolyi.

This article shows that individuals' product market choices influence their investment decisions. Using microdata from the brokerage and automotive industries, we find a strong positive relation between customer relationship, ownership of a company, and size of the ownership stake. Investors are also more likely to purchase and less likely to sell shares of companies they frequent as customers. These effects are stronger for individuals with longer customer relationships. A merger-based natural experiment supports a causal interpretation of our results. We also find evidence of causality in the other direction: inheritances and gifts have an effect on individuals' patronage decisions. A setup in which customer-investors regard stocks as consumption goods, not just as investments, seems to best explain our results. (JEL G11, G24, D83) The Author 2012. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.

A common view of retail finance is that conflicts of interest contribute to the high cost of advice. Within a large sample of Canadian financial advisors and their clients, however, we show that advisors typically invest personally just as they advise their clients. Advisors trade frequently, chase returns, prefer expensive and actively managed funds, and underdiversify. Advisors' net returns of −3% per year are similar to their clients' net returns. Advisors do not strategically hold expensive portfolios only to convince clients to do the same; they continue to do so after they leave the industry.

We put forward a model in which analysts are uncertain about a firm’s earnings process. Faced with the possibility of using a misspecified model, analysts issue forecasts that are robust to model misspecification. We estimate that this mechanism explains approximately 60% of the autocorrelation in analysts’ forecast errors. The remainder stems from the cross-sectional variation in mean forecast errors and in analysts’ estimation errors of the persistence of earnings growth shocks. Consistent with our model, we find that analysts learn about some features of the earnings process but not others, and this learning reduces, but does not eliminate, the autocorrelation of forecast errors as firms age. Other potential explanations for the autocorrelation of analyst forecast errors are rejected. Our model of robust forecasting applies not only to analysts’ forecasts but also to all model-based forecasts.

Using unique data on Canadian households, we show that financial advisors exert substantial influence over their clients' asset allocation, but provide limited customization. Advisor fixed effects explain considerably more variation in portfolio risk and home bias than a broad set of investor attributes that includes risk tolerance, age, investment horizon, and financial sophistication. Advisor effects remain important even when controlling flexibly for unobserved heterogeneity through investor fixed effects. An advisor's own asset allocation strongly predicts the allocations chosen on clients' behalf. This one‐size‐fits‐all advice does not come cheap: advised portfolios cost 2.5% per year, or 1.5% more than life cycle funds.

Using data on $18 trillion of assets under management, we show that actively managed institutional accounts outperformed strategy benchmarks by 75 (31) bps on a gross (net) basis during the period 2000 to 2012. Estimates from a Sharpe model imply that asset managers' outperformance came from factor exposures. If institutions had instead implemented mean‐variance efficient portfolios using index and institutional mutual funds available during the sample period, they would not have earned higher Sharpe ratios. Our results are consistent with the average asset manager having skill, managers competing for institutional capital, and institutions engaging in costly search to identify skilled managers.

Stocks tend to earn high or low returns relative to other stocks every year in the same month (Heston and Sadka, 2008). We show these seasonalities are balanced out by seasonal reversals: a stock that has a high expected return relative to other stocks in one month has a low expected return relative to other stocks in the other months. The seasonalities and seasonal reversals add up to zero over the calendar year, which is consistent with seasonalities being driven by temporary mispricing. Seasonal reversals are economically large and statistically highly significant, and they resemble, but are distinct from, long-term reversals.

Long-term expected returns do not appear to vary in the cross section of stocks. We show that even negligible persistent differences in expected returns, if they existed, would be easy to detect. Markers of such differences, however, are absent from actual stock returns. Our results are consistent with behavioral models and production-based asset pricing models in which firms’ risks change over time. Consistent with the lack of long-term differences in expected returns, persistent differences in firm characteristics do not predict the cross section of stock returns. Our results imply that stock market anomalies have only a limited effect on firm valuations.

We analyze whether IQ influences trading behavior, performance, and transaction costs. The analysis combines equity return, trade, and limit order book data with two decades of scores from an intelligence (IQ) test administered to nearly every Finnish male of draft age. Controlling for a variety of factors, we find that high-IQ investors are less subject to the disposition effect, more aggressive about tax-loss trading, and more likely to supply liquidity when stocks experience a one-month high. High-IQ investors also exhibit superior market timing, stock-picking skill, and trade execution.