Knowledge that Transforms

We create a culture dictionary using one of the latest machine learning techniques—the word embedding model—and 209,480 earnings call transcripts. We score the five corporate cultural values of innovation, integrity, quality, respect, and teamwork for 62,664 firm-year observations over the period 2001–2018. We show that an innovative culture is broader than the usual measures of corporate innovation – R&D expenses and the number of patents. Moreover, we show that corporate culture correlates with business outcomes, including operational efficiency, risk-taking, earnings management, executive compensation design, firm value, and deal making, and that the culture-performance link is more pronounced in bad times. Finally, we present suggestive evidence that corporate culture is shaped by major corporate events, such as mergers and acquisitions.

We introduce a parsimonious framework for choosing among alternative expected-return proxies (ERPs) when estimating treatment effects. By comparing ERPs’ measurement error variances in the cross-section and in the time series, we provide new evidence on the relative performance of firm-level ERPs nominated by recent studies. Generally, “implied-costs-of-capital” metrics perform best in the time series, whereas “characteristic-based” proxies perform best in the cross-section. Factor-based ERPs, even the latest renditions, perform poorly. We revisit four prior studies that use ex ante ERPs and illustrate how this framework can potentially alter either the sign or the magnitude of prior inferences.

We study how financial constraints affect the housing market by exploiting a regulatory change that increases the down payment requirement for homes selling for $$$1M or more. Using Toronto data, we find that the policy causes excess bunching of homes listed at $$$1M and heightened bidding intensity for these homes, but only a muted response in sales. While difficult to reconcile in a frictionless market, these findings are consistent with the implications derived from an equilibrium search model with auctions and financial constraints. Our analysis points to the importance of designing macroprudential policies that recognize the strategic responses of market participants.

Big data is revolutionizing the finance industry and has the potential to significantly shape future research in finance. This special issue contains papers following the 2019 NBER-RFS Conference on Big Data. In this introduction to the special issue, we define the “big data” phenomenon as a combination of three features: large size, high dimension, and complex structure. Using the papers in the special issue, we discuss how new research builds on these features to push the frontier on fundamental questions across areas in finance—including corporate finance, market microstructure, and asset pricing. Finally, we offer some thoughts for future research directions.

Data snooping is a major concern in empirical asset pricing. We develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data snooping. By exploiting a variety of machine learning techniques, our multiple-testing procedure is robust to omitted factors and missing data. We also prove its asymptotic validity when the number of tests is large relative to the sample size, as in many finance applications. To improve the finite sample performance, we also provide a wild-bootstrap procedure for inference and prove its validity in this setting. Finally, we illustrate the empirical relevance in the context of hedge fund performance evaluation.

How can fragility be averted in open-end mutual funds? In recent years, markets have observed an innovation that changed the way open-end funds are priced. Alternative pricing rules (known as swing pricing) adjust funds’ net asset values to pass on funds’ trading costs to transacting shareholders. Using unique data on investor-level transactions in U.K. corporate bond funds, we show that swing pricing eliminates the first-mover advantage arising from the traditional pricing rule and significantly reduces outflows during market stress. Swing pricing also reduces concavity in the flow-performance relationship and dilution in fund performance.

We exploit the merger between BlackRock and Barclays Global Investors to study how changes in expected ownership concentration affect the investment behavior of funds and the cross-section of stocks worldwide. We find that funds with open-end structures and large exposure to commonly held stocks begin avoiding these stocks following the merger announcement. This leads to a permanent change in the composition of institutional ownership and a negative price and liquidity impact. We confirm these results in a large sample of global asset management mergers. Our findings suggest that market participants behave strategically in response to changes in expected financial fragility.

Recent evidence indicates that market model alphas are stronger predictors of mutual fund flows than alphas with other models. Some recent papers have interpreted this evidence to mean that CAPM is the best asset pricing model, but some others have interpreted it as evidence against investor sophistication. We evaluate the merits of these mutually exclusive interpretations. We show that no tenable inference about the validity of any asset pricing model can be drawn from this evidence. Rejecting the investor sophistication hypothesis is tenable, but the appropriate benchmark to judge sophistication is different from that used in this literature.

We show how securitization affects the size of the nonbank lending sector through a novel price-based channel. We identify the channel using a regulatory spillover shock to the cross-section of mortgage-backed security prices: the U.S. liquidity coverage ratio. The shock increases secondary market prices for FHA-insured loans by granting them favorable regulatory status once securitized. Higher prices lower nonbanks’ funding costs, prompting them to loosen lending standards and originate more FHA-insured loans. This channel accounts for 22% of nonbanks’ growth in overall mortgage market share over 2013–2015. While the shock creates risks for financial stability, homeownership also increases.

We evaluate the impacts of tax policy on asset returns using the U.S. municipal bond market. In theory, tax-induced ownership segmentation limits risk sharing, creating downward-sloping regions of the aggregate demand curve for the asset. In the data, cross-state variation in tax privilege policies predicts differences in in-state ownership of local municipal bonds; the policies create incentives for concentrated local ownership. High tax privilege states have muni bond yields that are more sensitive to variations in supply and local idiosyncratic risk. The effects are stronger when local investors face correlated background risk and/or diminishing marginal nonpecuniary benefits from holding local assets.