Knowledge that Transforms

This paper studies why investors buy dividend-paying assets and how they time consumption accordingly. We combine administrative bank data linking customers’ consumption and income to portfolio data and survey responses on financial behavior. We find that private consumption is excessively sensitive to dividend income. Investors across wealth, income, and age distributions increase spending precisely around days of dividend receipt. Our results are at odds with a number of existing rational and behavioral explanations, such as financial constraints and impulsiveness. Instead, consumption responses reflect “planned” excess sensitivity, driven by investors who select dividend portfolios, anticipate dividend income, and plan consumption accordingly.

Portfolio optimization often struggles in realistic out-of-sample contexts. We deconstruct this stylized fact by comparing historical forecasts of portfolio optimization inputs with subsequent out-of-sample values. We confirm that historical forecasts are imprecise guides of subsequent values, but we discover the resultant forecast errors are not entirely random. They have predictable patterns and can be partially reduced using their own history. Learning from past forecast errors to calibrate inputs (akin to empirical Bayesian learning) generates portfolio performance that reinforces the case for optimization. Furthermore, the portfolios achieve performance that meets expectations, a desirable yet elusive feature of optimization methods.

We study consumption, savings, and asset prices in economies with disagreement and production, focusing on settings with real effects of disagreement. Aggregate savings may be significantly distorted under disagreement, possibly related to the undersaving puzzle. In the production economy, mispricing mainly manifests itself in idiosyncratic risk, in contrast to the exchange economy, where the risk-free rate and expected return on the market may be distorted. Potential policy implications include the introduction of investment taxes or subsidies. Our results highlight the real effects of disagreement in financial markets, and the differences between economies with and without production.

Microcredit and other forms of small-scale finance have failed to catalyze entrepreneurship in developing countries. In these credit markets, borrowers and lenders often bargain over not only the interest rate but also implicit restrictions on types of investment. We build a dynamic model of informal lending and show this may lead to endogenous debt traps. Lenders constrain business growth for poor borrowers, yet richer borrowers may grow their businesses faster than they could have without credit. The theory offers nuanced comparative statics and rationalizes the low average impact and low demand of microfinance, despite its high impact on larger businesses.

We examine international equity allocations at the fund level and show how excess foreign returns influence portfolio rebalancing, capital flows, and currencies. Our equilibrium model of incomplete foreign exchange (FX) risk trading where exchange rate risk partially segments international equity markets is consistent with the observed dynamics of equity returns, exchange rates, and fund-level capital flows. We document that rebalancing is more intense under higher FX volatility and find heterogeneous rebalancing behavior across different fund characteristics. A granular instrumental variable approach identifies a positive currency supply elasticity.

We study how dynamic research affects information acquisition in financial markets. In our strategic trading model, the trader performs costly research to generate private information but does not always succeed. Optimal research activity responds to market conditions and generates novel implications. First, more frequent public disclosures can “crowd in” private information acquisition, increase price informativeness, and harm liquidity, instead of “leveling the playing field.” Second, observed research activity does not necessarily imply that traders are better informed. Finally, improvements in research effectiveness or higher market participation by uninformed investors can simultaneously increase price informativeness and liquidity.

We examine the recent rise of institutional investment in the single-family home rental market and its implications for renters’ welfare. Using institutional mergers to identify local exogenous variation in institutional landlords’ scale and market share, we show that rents increase in neighborhoods where both merging firms owned properties (i.e., overlapped neighborhoods) relative to other nonoverlapped neighborhoods. Meanwhile, the crime rate also significantly decreases in overlapped neighborhoods after mergers. Our findings suggest that while institutional landlords leverage their market power to extract greater surplus from renters, they also improve the quality of rental services by enhancing neighborhood safety.

We study the impact of FinTech competition in payment services when a monopolist bank uses payment data to learn about consumers’ credit quality. Competition from FinTech payment providers disrupts this information spillover. The bank’s price for payment services and its loan offers are affected. FinTech competition promotes financial inclusion, may hurt consumers with a strong bank preference, and has an ambiguous effect on the loan market. Both FinTech data sales and consumer data portability increase bank lending, but the effects on consumer welfare are ambiguous. Under mild conditions, consumer welfare is higher under data sales than with data portability.

Empirical studies demonstrate striking patterns in stock returns related to scheduled macroeconomic announcements. A large proportion of the total equity premium is realized on days with macroeconomic announcements. The relation between market betas and expected returns is far stronger on announcement days as compared with nonannouncement days. Finally, these results hold for fixed-income investments as well as for stocks. We present a model in which agents learn the probability of an adverse economic state on announcement days. We show that the model quantitatively accounts for the empirical findings. Evidence from options data provides support for the model’s mechanism.

We propose a novel measure of arbitrage activity to examine whether arbitrageurs can have a destabilizing effect on the stock market. We focus on stock price momentum, a classic example of a positive-feedback strategy that our theory predicts can be destabilizing. Our measure, dubbed comomentum, is the high-frequency abnormal return correlation among stocks on which a typical momentum strategy would speculate. When comomentum is low, momentum strategies are stabilizing, reflecting an underreaction phenomenon that arbitrageurs correct. When comomentum is high, the returns on momentum stocks strongly revert, reflecting prior overreaction from crowded momentum trading that pushes prices away from fundamentals.