We study whether subadvising abroad provides an information advantage that improves the performance of international equity mutual funds. We find that it does not. In fact, internationally outsourced funds underperform on a risk-adjusted basis by up to 162 bps annually. The underperformance is concentrated in funds managed by single subadvisors, who are less likely to be terminated after poor performance compared to funds with multiple subadvisors. We dissect fund performance by the location of its subadvisors and find that international subadvisors underperform primarily in their local holdings. Finally, we show that the industry is nonetheless on a path toward equilibrium.

We show that structured equity derivatives can cause significant price pressure of the underlying stock upon an event of dramatic payoff change. Moreover, one event causes another: the event cascade amplifies the magnitude of the impact. We find that a single event accounts for a −6.4% return on the event day, and it increases the probability of a subsequent event by 21.3%. Given the negative price impact, traders try to liquidate ahead of each other, exacerbating the degree of price pressure. Our results uncover the chain-reaction and (mis)coordination mechanism in complex derivatives markets that can provoke substantial price shocks.

Factors related to carry, duration, equity momentum, and the term structure are the most important risk factors in corporate bond markets. From a large set of factor candidates, we condense an optimal model with a two-step approach. First, we filter out factors that do not systematically move bond prices. Second, we use a Bayesian model selection approach to determine the optimal, parsimonious model. Many prominent factors do not move prices or are redundant. We document the new model’s good performance compared to that of existing models in time-series and cross-sectional tests and analyze the economic drivers of the factors.

We propose a dynamic equilibrium model with stochastic interest rates in which agents hold heterogeneous valuations for the same asset and take on positions against each other. The model shows that interest rate uncertainty and investor heterogeneity are key determinants of price dispersion. Higher search intensity reduces price dispersion, while raising volume, leading to a negative volatility-volume relation. The sensitivity of volatility to volume is high when liquidity is low, interest rate variations are high and investors’ valuations are more heterogeneous. Evidence supports our model’s predictions and shows that search frictions play an important role in driving the volatility-volume relation.

When brokers, analysts, and fund managers buy or sell stocks for their own accounts, these “access employees” of financial institutions outperform retail investors over short windows up to a month. They earn particularly high abnormal returns when they trade before earnings announcements, revisions of analyst recommendations, and large stock price changes. We also find evidence consistent with profitable front-running and information leakage around the execution of corporate insider trades and block trades by mutual funds, as well as the release of revised recommendations by analysts who work at the same brokerage firm

We propose a naïve model averaging (NMA) method that averages the OLS out-of-sample forecasts and the historical means and produces mostly positive out-of-sample R2s for the variables significant in sample in forecasting market returns. Surprisingly, more sophisticated weighting schemes that combine the predictive variable and historical mean do not consistently perform better. With unstable economic relations and a limited sample size, sophisticated methods may lead to overfitting or be subject to more estimation errors. In such situations, our simple methods may work better. Model misspecification, rather than declining return predictability, likely explains the predictive performance of the NMA method

Predicted stock issuers (PSIs) are firms with expected high-investment and low-profit profiles that earn extremely low returns. We evaluate alternative explanations for this empirical phenomenon. Our results show top-PSI firms are cash-strapped, have lottery-like payoffs, high volatility, high beta, low liquidity, and high shorting costs. Over the next 2 years, top-PSI firms earn return on assets of −30% per year, report disappointing earnings, and experience strongly negative forecast revisions. They perform poorly in down markets and are six times more likely to delist for performance-related reasons. Overall, we find substantial support for mispricing, some support for nonstandard preferences, and virtually no support for the risk explanation.

We develop a model of information and portfolio choice in which ex ante identical investors choose to specialize because of fixed attention costs required in learning about securities. Without this friction, investors would invest in all securities and would be indifferent across a wide range of information choices. When securities’ dividends depend on an aggregate (macro) risk factor and idiosyncratic (micro) shocks, fixed attention costs lead investors to specialize in either macro or micro information. Our results favor Samuelson’s dictum that markets are more micro than macro efficient. We derive testable predictions from our model and find empirical support for our predictions in specialization by U.S. equity mutual funds.

Why do so few mutual fund families launch so many funds and styles around the world? We argue that launching numerous funds on an increasingly granular style grid allows incumbent families to congest the product space and deter market entry. Key to this argument is the persistently low dimensionality of the mutual fund product space, a fact we establish by analyzing the names of over 40,000 equity funds sold in 91 countries between 1931 and 2015. Over time, the strategy of filling up the style grid has led to the dominance of few families offering large, granular, and similar fund menus.

Through textual analyses of 7.7 million patents, we develop a novel intercompany innovation similarity measure which enables us to find that technologically connected firms cross-predict one another’s returns. Investors impound information about firms’ technological connectedness, although not immediately and fully. Buying (shorting) shares of technological peers earning high (low) returns during the previous month yields a 1.29% monthly return. Firms’ return predictability increases with patent complexity or limited technological disclosures but decreases with better information transparency. Results suggest that investor inattention explains technology momentum. Unlike momentum stemming from simpler, class-based technological links, our Big Data text-based return predictability remains active.