This paper introduces a new approach to constructing optimal mean-variance portfolios. The approach relies on a novel unconstrained regression representation of the mean-variance optimization problem combined with high-dimensional sparse-regression methods. Our estimated portfolio, under a mild sparsity assumption, controls for risk and attains the maximum expected return as both the numbers of assets and observations grow. The superior properties of our approach are demonstrated through comprehensive simulation and empirical analysis. Notably, using our strategy, we find that investing in individual stocks, in addition to the Fama-French three-factor portfolios, leads to substantially improved performance. Received October 6, 2014; editorial decision July 13, 2018 by Editor Andrew Karolyi

Data from U.S. public firms show that in booms large firms finance with debt and payout equity, whereas small firms issue both equity and debt. Therefore, large firms generally substitute between debt and equity financing over the business cycle, whereas small firms adhere to a procyclical financing policy for debt and equity. We explain these cyclical financing patterns quantitatively using a heterogeneous firm model with endogenous firm dynamics. We find that cross-sectional differences in investment returns and, therefore, funding needs and exposures to financial frictions are essential to understanding how firms’ financing policies respond to macroeconomic shocks.Received December 24, 2016; editorial decision April 24, 2018 by Editor Stijn Van Nieuwerburgh.

We use computational linguistics to develop a dynamic, interpretable methodology that can detect emerging risks in the financial sector. Our model can predict heightened risk exposures as early as mid-2005, well in advance of the 2008 financial crisis. Risks related to real estate, prepayment, and commercial paper are elevated. Individual bank exposure strongly predicts returns, bank failures, and return volatility. We also document a rise in market instability since 2014 related to sources of funding and mergers and acquisitions. Overall, our model predicts the buildup of emerging risk in the financial system and bank-specific exposures in a timely fashion. Received March 1, 2018; editorial decision November 18, 2018 by Editor Itay Goldstein

We provide large-scale evidence on the occurrence and value of FinTech innovation. Using data on patent filings from 2003 to 2017, we apply machine learning to identify and classify innovations by their underlying technologies. We find that most FinTech innovations yield substantial value to innovators, with blockchain being particularly valuable. For the overall financial sector, internet of things (IoT), robo-advising, and blockchain are the most valuable innovation types. Innovations affect financial industries more negatively when they involve disruptive technologies from nonfinancial startups, but market leaders that invest heavily in their own innovation can avoid much of the negative value effect.ReceivedMay 31, 2017; editorial decision September 30, 2018 by Editor Andrew Karolyi.

We model the interaction of financial constraints, capacity constraints, and the response of production and inventory to cost and demand shocks. The model predicts that in response to favorable shocks, financially constrained firms are unable to build inventory as rapidly as are unconstrained firms. However, because the favorable shocks gradually ease the financial constraints, constrained firms continue to build inventory and eventually carry surplus inventory (relative to unconstrained firms) to unfavorable states. This allows them to deplete inventory more aggressively in response to unfavorable shocks. Our empirical evidence provides broad support for the model’s predictions.Received September 3, 2016; editorial decision January 11, 2018 by Editor David Denis.

Using over 5,000 trades unequivocally based on nonpublic information about firm fundamentals, we find that asymmetric information proxies display abnormal values on days with informed trading. Volatility and volume are abnormally high, whereas illiquidity is low, in equity and option markets. Daily returns reflect the sign of private signals, but bid-ask spreads are lower when informed investors trade. Market makers’ learning under event uncertainty and limit orders help explain these findings. The cross-section of information duration indicates that traders select days with high uninformed volume. Evidence from the U.S. SEC Whistleblower Reward Program and the FINRA involvement addresses selection concerns. Received January 11, 2017; editorial decision December 17, 2018 by Editor Andrew Karolyi.

This paper examines whether banks strategically incorporate their competitors’ liquidity mismatch policies when determining their own and the impact of these collective decisions on financial stability. Using a novel identification strategy exploiting the presence of partially overlapping peer groups, I show that banks’ liquidity transformation activity is driven by that of their peers. These correlated decisions are concentrated on the asset side of riskier banks and are asymmetric, with mimicking occurring only when competitors take more risk. Accordingly, this strategic behavior increases banks’ default risk and overall systemic risk, highlighting the importance of regulating liquidity risk from a macroprudential perspective. ReceivedMay 4, 2016; editorial decision January 1, 2019 by Editor Philip Strahan

Using matched borrower-lender data, we document strong nonprice supplier effects in mortgage contract choice. For given relative price of adjustable and fixed rate mortgages, households borrowing from banks hit by shocks to the cost of long term funding, or to the deposits base or to access to securitization are more likely to choose adjustable rate mortgages. Supply factors have larger effects on less-sophisticated households and at times of price inaction. A model in which banks affect borrowers’ choices through prices and distorted advice predicts these findings. We contrast the distorted advice interpretation of the evidence against the potential alternative nonprice channels. Received April 7, 2017; editorial decision October 21, 2018 by Editor Stijn Van Nieuwerburgh.

Does private equity (PE) contribute to financial fragility during economic crises? The proliferation of poorly structured transactions during booms may increase the vulnerability of the economy to downturns. During the 2008 crisis, PE-backed companies decreased investments less than did their peers and experienced greater equity and debt inflows, higher asset growth, and increased market share. These effects are especially strong among financially constrained companies and those whose PE investors had more resources at the crisis onset. In a survey, PE firms report being active investors during the crisis and spending more time working with their portfolio companies. Received July 19, 2017; editorial decision March 7, 2018 by Editor Wei Jiang

Researchers have long known about backfill bias in hedge fund databases. The most common treatments include either retaining all backfilled returns or truncating a fixed number of returns from each return series. However, we show that truncation largely preserves backfilled returns and document that either of these backfill treatments can lead to biased empirical findings, including cross-sectional results. Thus, our findings show that the best practice for empirical tests is to remove returns prior to the listing date. Because most databases do not have listing dates, we propose a novel method to infer unavailable listing dates. Received August 19, 2018; editorial decision December 4, 2018 by Editor Wei Jiang.