Bryan R. Routledge

Exotic Preferences for Macroeconomists

We provide a users guide to exotic preferences: nonlinear time aggregators, departures from expected utility, preferences over time with known and unknown probabilities, risk-sensitive and robust control, hyperbolic discounting, and preferences over sets (temptations). We apply each to a number of classic problems in macroeconomics and finance, including consumption and saving, portfolio choice, asset pricing, and Pareto optimal allocations.

Social Capital and Growth

We define and characterize social capital in a simple growth model. We capture social capital in a model where individuals in a community maximize their lifetime gains to trade. Each trade between two members of a community has the structure of the prisoners’ dilemma. Trades are repeated indefinitely, but not necessarily each period. Social capital is defined as the social structure which facilitates cooperative trade as an equilibrium. The trading model is incorporated into a growth model to explore the connections between growth, labor mobility, and social capital. The key assumption is that technological innovation, which drives growth, involves a reallocation of labor that affects social capital. Modifying the responsiveness of labor to a technological shock, has implications for both labor efficiency and social capital.

Predicting Risk from Financial Reports with Regression

We address a text regression problem: given a piece of text, predict a real-world continuous quantity associated with the texts meaning. In this work, the text is an SEC-mandated financial report published annually by a publicly-traded company, and the quantity to be predicted is volatility of stock returns, an empirical measure of financial risk. We apply well-known regression techniques to a large corpus of freely available financial reports, constructing regression models of volatility for the period following a report. Our models rival past volatility (a strong baseline) in predicting the target variable, and a single model that uses both can significantly outperform past volatility. Interestingly, our approach is more accurate for reports after the passage of the Sarbanes-Oxley Act of 2002, giving some evidence for the success of that legislation in making financial reports more informative.

GENETIC ALGORITHM LEARNING TO CHOOSE AND USE INFORMATION

A genetic algorithm (GA) is used to model learning in a financial model similar to the Grossman–Stiglitz model. Individuals need to learn how to use a signal, how to make an inference about a signal from a market-clearing price, and whether or not a signal is worth acquiring. We provide examples in which the GA does and does not converge to the rational expectations equilibrium. Similar to earlier results, the behavior depends heavily on the rate of experimentation or mutation in the GA and the size of the risky-asset supply noise in the economy.

Information Content of Public Firm Disclosures and the Sarbanes-Oxley Act

We find evidence that public firm disclosure, in the form of Management Discussion and Analysis (Sections 7 and 7a of annual reports), is more informative about the firms future risk following the passage of the Sarbanes-Oxley Act of 2002. Employing a novel text regression, we are able to predict, out of sample, firm return volatility using the Management Discussion and Analysis section from annual 10-K reports (which contains forward-looking views of the management). Using the relative performance of the text model as a proxy for the informativeness of reports, we show that the MD&A sections are significantly more informative after the passage of SOX. We further show that this additional information is associated with a reduction in share illiquidity, suggesting that the information divulged was new to investors. Finally, we find that the increase in informativeness of MD&A reports is most pronounced for firms with higher costs of adverse selection.

Project Assignment Rights and Incentives for Eliciting Ideas

In this paper, we study an incentive problem that arises between a principal and two agents because they value a real option differently. The real option in our model is a timing option. The agents have limited capacity to undertake projects, and each agents capacity can be filled now or later. Because the principal cares about capacity in the aggregate but each agent cares only about his own capacity, the agents assign a higher value to the option to wait. As a result, agents sometimes withhold ideas from the principal. We show that decentralization can be a solution to this problem. Delegating assignment rights to an agent reduces the option value of waiting for the other agent sufficiently that he is willing to reveal his ideas.

Large-scale insider trading analysis: patterns and discoveries

How do company insiders trade? Do their trading behaviors differ based on their roles (e.g., chief executive officer vs. chief financial officer)? Do those behaviors change over time (e.g., impacted by the 2008 market crash)? Can we identify insiders who have similar trading behaviors? And what does that tell us? This work presents the first academic, large-scale exploratory study of insider filings and related data, based on the complete Form 4 fillings from the U.S. Securities and Exchange Commission. We analyze 12 million transactions by 370 thousand insiders spanning 1986–2012, the largest reported in academia. We explore the temporal and network-based aspects of the trading behaviors of insiders, and make surprising and counterintuitive discoveries. We study how the trading behaviors of insiders differ based on their roles in their companies, the types of their transactions, their companies’ sectors, and their relationships with other insiders. Our work raises exciting research questions and opens up many opportunities for future studies. Most importantly, we believe our work could form the basis of novel tools for financial regulators and policymakers to detect illegal insider trading, help them understand the dynamics of the trades, and enable them to adapt their detection strategies toward these dynamics.

Inside insider trading: patterns & discoveries from a large scale exploratory analysis

How do company insiders trade? Do their trading behaviors differ based on their roles (e.g., CEO vs. CFO)? Do those behaviors change over time (e.g., impacted by the 2008 market crash)? Can we identify insiders who have similar trading behaviors? And what does that tell us? This work presents the first academic, large-scale exploratory study of insider filings and related data, based on the complete Form 4 fillings from the U.S. Securities and Exchange Commission (SEC). We analyzed 12 million transactions by 370 thousand insiders spanning 1986 to 2012, the largest reported in academia. We explore the temporal and network-centric aspects of the trading behaviors of insiders, and make surprising and counter-intuitive discoveries. We study how the trading behaviors of insiders differ based on their roles in their companies, the transaction types, the company sectors, and their relationships with other insiders. Our work raises exciting research questions and opens up many opportunities for future studies. Most importantly, we believe our work could form the basis of novel tools for financial regulators and policymakers to detect illegal insider trading, help them understand the dynamics of the trades and enable them to adapt their detection strategies towards these dynamics.

Does Macro-Asset Pricing Matter for Corporate Finance?

In an asset-pricing model calibrated to match the standard asset pricing empirical properties -- in particular, the time-variation in the equity premium -- we calculate the value implications of sub-optimal capital budgeting decisions. Specifically, we calculate that an investment policy that ignores the time variation in the equity premium, such as would occur with a cost of capital following the CAPM, incurs a 11.7% value loss. We also document the implications for a firms asset returns in this context.

Friends with Motives: Using Text to Infer Influence on SCOTUS.

We present a probabilistic model of the influence of language on the behavior of the U.S. Supreme Court, specifically influence of amicus briefs on Court decisions and opinions. The approach assumes that amici are rational, utility-maximizing agents who try to win votes or affect the language of court opinions. Our model leads to improved predictions of justices’ votes and perplexity of opinion language. It is amenable to inspection, allowing us to explore inferences about the persuasiveness of different amici and influenceability of different justices; these are consistent with earlier findings. “Language is the central tool of our trade.” John G. Roberts, 2007 (Garner, 2010)