Knowledge that Transforms

We document aggregate outflows from corporate bond mutual funds days before and after the announcement of increases in the Federal Funds Target rate (FFTar). To rationalize this phenomenon, we build a model in which funds’ net-asset-values (NAVs) are stale and investors strategically redeem to profit from the mispricing when they learn about the increases of FFTar. Consistent with the model’s predictions, we find that stale NAVs and loose monetary policy environments weaken (strengthen) outflows sensitivity to increases in FFTar during illiquid (liquid) market conditions. Our results highlight when and how monetary policy could systematically exacerbate the fragility of corporate bond funds.

We test the efficient market hypothesis by using machine learning to forecast stock returns from historical performance. These forecasts strongly predict the cross-section of future stock returns. The predictive power holds in most subperiods and is strong among the largest 500 stocks. The forecasting function has important nonlinearities and interactions, is remarkably stable through time, and captures effects distinct from momentum, reversal, and extant technical signals. These findings question the efficient market hypothesis and indicate that technical analysis and charting have merit. We also demonstrate that machine learning models that perform well in optimization continue to perform well out-of-sample.

We develop a simple model of concentrated lending where lenders have incentives for evergreening loans by offering better terms to firms that are close to default. We detect such lending behavior using loan-level supervisory data for the United States. Banks that own a larger share of a firm's debt provide distressed firms with relatively more credit at lower interest rates. Building on this empirical validation, we incorporate the theoretical mechanism into a dynamic heterogeneous-firm model to show that evergreening affects aggregate outcomes, resulting in lower interest rates, higher levels of debt, and lower productivity.

We examine how executive compensation can be designed to facilitate product market collusion. We look at the 2013 decision to close several regional offices of the U.S. Department of Justice, which lowered antitrust enforcement for firms located near these closed offices. We argue this made collusion more appealing to shareholders, and find that these firms increased the sensitivity of executive pay to local rivals' performance, consistent with rewarding the managers for colluding with them. The affected CEOs were also granted longer vesting periods, which provides long-term incentives that could foster collusive arrangements.

This paper studies the rise of direct lending using a comprehensive dataset of investments by business development companies (BDC). We exploit three exogenous shocks to credit supply, including new banking regulations and a major finance company collapse, to establish that BDC capital acts as a substitute for traditional financing. Using firm-level data, we further document that firms’ access to BDC funding stimulates their employment growth and patenting activity. Beyond credit provision, BDCs contribute to firm growth through managerial assistance.

We show that firm valuations fell after a key expense became more visible in financial statements. FAS 123-R required firms to deduct option compensation costs from earnings, instead of disclosing them in footnotes. Firms that granted high option pay experienced earnings reductions, while fundamentals remained unchanged. These firms were more likely to miss earnings forecasts, and they experienced recommendation downgrades and valuation declines. Our findings suggest that market participants exhibited limited attention to option costs before FAS 123-R. As we reuse the FAS 123-R natural experiment, we show how one can address confounding channels by integrating reduced-form and structural estimation.

We propose no-arbitrage term structure models with volatility factors that follow GARCH processes. The models’ tractability is similar to canonical affine term structure models, but they fit yield volatility much better, especially for long-maturity yields. This improvement does not come at the expense of a deterioration in yield fit. Because of the improved volatility fit, the model performs substantially better in pricing Treasury futures options. We conclude that the specification of the volatility factors is critical. Modeling volatility as a function of (lagged) squared innovations to factors improves on models where volatility is a linear function of the factors.

Using granular data on U.S. hotel investments over the past two decades, we show that industry-specialist PE firms achieve higher net income from operations and higher capital gains from sale than generalist PE firms for comparable properties. Those results are driven by specialists implementing more and larger cost savings without compromising revenues. Fundamentally, specialists utilize their hotel-specific operating expertise to produce superior performance outcomes. We show that specialists across investment sectors possess deeper industry-specific operating expertise. Our results suggest that specialist PE firms can compete with their generalist rivals by leveraging such expertise in a chosen market niche.

Investment-based asset pricing models typically predict a close link between a firm’s stock return and its characteristics at any point in time. Yet, previous studies have primarily focused on the weaker prediction that this link holds on average, finding substantial empirical support. We show how to incorporate the time-series predictions in the estimation and testing of investment-based models using the generalized method of moments. We find that standard specifications of investment-based models with one physical capital input fail to match the time series properties of stock returns in the data, and discuss the implications of the findings for future research.

How does financial market concentration affect capital allocation? We propose a complete-markets model in which real investment and financial price impact are jointly determined in general equilibrium. We identify a two-way feedback mechanism whereby price impact induces misallocation and misallocation raises price impact. The mechanism is stronger if productivity is low or productivity dispersion is high. Given rising dispersion, the model can rationalize trends in corporate discount rates, cash holdings, investment, asset prices, and capital reallocation over the last two decades, even when market concentration is relatively stable. Overall, our findings suggest that financial market concentration may hamper allocative efficiency.