ABSTRACT We study the effect of a change in property rights on employee behavior in the financial advice industry. Our identification comes from staggered firm‐level entry into the Protocol for Broker Recruiting , which waived nonsolicitation clauses for advisor transitions among member firms, effectively transferring ownership of client relationships from the firm to the advisor. After the shock, advisors appear to tend to client relationships more by investing in client‐facing industry licenses, shifting to fee‐based advising, and reducing customer complaints. Our findings support property rights based investment theories of the firm and document offsetting costs to restricting labor mobility.

Upon the announcement of the Shanghai-Hong Kong Stock Connect program, connected stocks in the Shanghai Stock Exchange experience significant value appreciation of 1.8% over a seven-day announcement window and significant increases in turnover and volatility compared with unconnected stocks with similar firm characteristics, especially for stocks with higher market beta. The beta effect on stock prices is stronger for stocks with higher beta-to-idiosyncratic variance ratios and is reversed within three months. The results support the speculative nature of beta and the multiplier effect of speculation on demand shocks as predicted by Hong, Scheinkman, and Xiong (2006) and Hong and Sraer (2016). The announcement of the Shenzhen-Hong Kong Stock Connect program serves as an out-of-sample test and confirms our findings.

The field of asset pricing is a rich and diverse discipline that has contributed to many areas of discourse, including those of fundamental importance to policy makers, investors, and households.1 As we look ahead during a time of substantial economic and political change, it is apparent that society faces many pressing questions, both new and old, that the field is uniquely suited to informing.To contribute to this conversation, the NBER Asset Pricing program convened a panel discussion on “Perspectives on the Future of Asset Pricing” at its November 8, 2019, meeting that took place at Stanford University. The objective of the panel was to identify some of the important questions the field could productively address in the next five to 10 years. The panelists, consisting of experts in several subfields of asset pricing, were invited to share their views on these questions with an eye toward innovative research topics that are ripe for exploring, and the metrics the field could be using to gauge progress.

Allocating appointment slots is presented as a new application for market design. Online booking systems are commonly used by public authorities to allocate appointments for visa interviews, driver’s licenses, passport renewals, etc. We document that black markets for appointments have developed in many parts of the world. Scalpers book the appointments that are offered for free and sell the slots to appointment seekers. We model the existing first-come-first-served booking system and propose an alternative batch system. The batch system collects applications for slots over a certain time period and then randomly allocates slots to applicants. The theory predicts and lab experiments confirm that scalpers profitably book and sell slots under the current system with sufficiently high demand, but that they are not active in the proposed batch system. We discuss practical issues for the implementation of the batch system and its applicability to other markets with scalping. (JEL C92, D47)

ABSTRACT This study examines how an increase in tick size affects algorithmic trading (AT), fundamental information acquisition (FIA), and the price discovery process around earnings announcements (EAs). Leveraging the SEC's randomized Tick Size Pilot experiment, we show that a tick size increase results in a decline in AT and a sharp drop in absolute cumulative abnormal returns and volume around EAs. More importantly, we find increased FIA in the preannouncement period. Specifically, we show: (1) treatment firms' pre-announcement returns better anticipate next quarter's standardized unexpected earnings; (2) these firms experience an increase in EDGAR web traffic prior to EAs; and (3) they exhibit a drop in price synchronicity with index returns. Taken together, our evidence suggests that while an increase in tick size reduces AT and abnormal market reaction after EAs, it also increases FIA activities prior to EAs. JEL Classifications: M40; M41; G12; G14.

Using data from 2002 to 2013, we show that algorithmic trading has a positive impact on firm value. Most of this positive impact flows through the channels of stock liquidity, idiosyncratic volatility, and idiosyncratic skewness, but algorithmic trading also has a large economic effect outside those channels. We use the advent of auto quotation on the New York Stock Exchange as an exogenous shock to algorithmic trading to rule out reverse causality. The positive effects of algorithmic trading on firm value are stronger for larger firms and in the post-2007 period when algorithmic trading intensity is higher.

Privacy is a feature inherent to the use of cash. With steadily increasing market shares of digital payment platforms, privacy in payments may no longer be attainable in the future. We explore the potential welfare impacts of reductions in privacy in payments. In our framework, firms may use data collected through payments to price discriminate future consumers. A public good aspect arises because individuals do not internalize the full cost of failing to protect their privacy and reduce social welfare by suboptimally choosing not to protect their privacy in payments. We discuss potential remedies, including the issuance of electronic cash.

We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between choice sets and preferences. We first characterize the sharp identification region of the model's parameters by a finite set of conditional moment inequalities. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with low levels of risk aversion and heterogeneous non‐singleton choice sets, and that more than three in four households require limited choice sets to explain their deductible choices. We also provide simulation evidence on the computational tractability of our method in applications with larger feasible sets or higher‐dimensional unobserved heterogeneity.

We study reforms of nonlinear income tax systems from a political economy perspective. We present a median voter theorem for monotonic tax reforms, reforms so that the change in the tax burden is a monotonic function of income. We also provide an empirical analysis of tax reforms, with a focus on the United States. We show that past reforms have, by and large, been monotonic. We also show that support by the median voter was aligned with majority support in the population. Finally, we develop sufficient statistics that enable to test whether a given tax system admits a politically feasible reform. (JEL D72, H21, H24)

We study a repeated game of price leadership in which a firm proposes supermarkups over Bertrand prices to a coalition of rivals. Supermarkups and marginal costs are recoverable from data on prices and quantities using the model’s structure. In an application to the beer industry, we find that price leadership increases profit relative to Bertrand competition by 17 percent in fiscal years 2006 and 2007, and by 22 percent in 2010 and 2011, with the change mostly due to consolidation. We simulate two mergers, which relax binding incentive compatibility constraints and increase supermarkups. These coordinated effects arise even with efficiencies that offset price increases under Bertrand competition. (JEL G34, K21, L13, L14, L41, L66)