Knowledge that Transforms

In the investment theory, firms with high expected investment growth earn higher expected returns than firms with low expected investment growth, holding investment and expected profitability constant. Building on cross-sectional growth forecasts with Tobin’s q, operating cash flows, and change in return on equity as predictors, an expected growth factor earns an average premium of 0.84% per month (t = 10.27) in the 1967–2018 sample. The q5 model, which augments the Hou–Xue–Zhang (2015, Rev. Finan. Stud., 28, 650–705) q-factor model with the expected growth factor, shows strong explanatory power in the cross-section and outperforms the Fama–French (2018, J. Finan. Econom., 128, 234–252) six-factor model.

This article examines the effect of regulatory constraints on fund performance and risk by comparing conventional and UCITS hedge funds. Using a matching estimator approach, we estimate the indirect cost of UCITS regulation to be between 1.06% and 4.05% per annum in terms of risk-adjusted returns. These performance differences are likely to stem from UCITS constraints such as those governing eligible assets, diversification, and short selling, and cannot be explained by differences in redemption terms or level of leverage. We confirm that our performance results are not driven by management company characteristics, fund manager characteristics, or unobserved confounder bias.

We show that returns to value strategies in individual equities, industries, commodities, currencies, global government bonds, and global stock indexes are predictable in the time series by their respective value spreads. In all these asset classes, expected value returns vary by at least as much as their unconditional level. A single common component of the value spreads captures about two-thirds of value return predictability and the remainder is asset class specific. We argue that common variation in value premia is consistent with rationally time-varying expected returns, because (i) common value is closely associated with standard proxies for risk premia, such as the dividend yield, intermediary leverage, and illiquidity, and (ii) value premia are globally high in bad times.

Firms in the US economy are closely interconnected in a production network and are subject to shocks that propagate within the network. This study examines the liquidity management of firms centrally connected in the network. I show that, while central firms are more exposed to aggregate swings, they maintain higher cash holdings to protect themselves and connected firms against such exposure. Central firms’ cash holding motives are alleviated by firm diversification but are aggravated by industry competition. Such motives are not explained by alternative determinants of cash policies. My findings suggest that systematically important firms proactively dampen the propagation of shocks in the production network.

Exploiting China’s anti-corruption campaign, we show that following a decrease in corruption, firm performance improves. Small and young firms benefit more. We identify the channels through which corruption hampers firm performance. Following the anti-corruption campaign, the allocation of capital and labor becomes more efficient. Firms operating in ex ante more corrupt environments experience larger productivity gains, higher growth of sales, and lower cost of debt than other firms. Taken together, our results suggest that corruption is an inefficient equilibrium for an economy because it creates negative externalities.

I ask whether machine learning (ML) algorithms improve the efficiency in lending without compromising on equity in a credit environment where soft information dominates. I obtain loan application-level data from an Indian bank. To overcome the problem of the selective labels, I exploit the incentive-driven within officer difference in leniency within a calendar month. I find that the ML algorithm can lend 60% more at loan officers’ delinquency rate or achieve a 33% lower delinquency rate at loan officers’ approval rate. The efficiency is maintained even when the algorithm is explicitly prevented from discriminating against disadvantaged social classes.

We investigate the new reality of exchange-traded funds (ETFs). We show that most ETFs are active investments in form (designed to generate alpha) or function (serve as building blocks of active portfolios). We define a new activeness index to capture these dimensions, finding that the cross-section of ETFs is now increasingly characterized by highly active investment vehicles. Active-in-form ETFs have positive flow-performance sensitivity, charge the highest fees among ETFs, and have high within-portfolio turnover. Active-in-function ETFs have more concentrated holdings, less within-portfolio turnover, but higher turnover in the secondary market. We show how more active ETFs are gaining market share over less active ETFs, leading to competitive fee pressure both within the ETF space and across the investment management industry. We suggest that the growing activeness of ETFs may assuage concerns about ETFs harming price discovery.

Using merger announcements and applying methods from computational linguistics we find strong evidence that stock prices underreact to information in financial media. A one standard deviation increase in the media-implied probability of merger completion increases the subsequent 12-day return of a long-short merger strategy by 1.2 percentage points. Filtering out the 28% of announced deals with the lowest media-implied completion probability increases the annualized alpha from merger arbitrage by 9.3 percentage points. Our results are particularly pronounced when high-yield spreads are large and on days when only few merger deals are announced.

We use the text of financial stability reports (FSRs) published by central banks to analyze the relation between the sentiment they convey and the financial cycle. We construct a dictionary tailored specifically to a financial stability context, which classifies words as positive or negative based on the sentiment they convey in FSRs. With this dictionary, we construct financial stability sentiment (FSS) indexes for thirty countries between 2005 and 2017. We find that central banks’ financial stability communications are mostly driven by developments in the banking sector. Moreover, the sentiment captured by the FSS index explains movements in financial cycle indicators related to credit, asset prices, systemic risk, and monetary policy rates. Finally, our results show that the sentiment in central banks’ communications is a useful predictor of banking crises—a one percentage point increase in FSS is followed by a twenty-nine percentage point increase in the probability of a crisis.

This article shows that postcrisis stress tests have negative effects on entrepreneurship and innovation at young firms. Exploiting unique data on business-related home equity loans in Home Mortgage Disclosure Act, I show that stress-tested banks strongly cut small business loans secured by home equity, an important source of financing for entrepreneurs. Lower credit supply leads to a relative decline in entrepreneurship in counties with higher exposure to stress-tested banks. The decline is stronger in sectors with a higher share of young firms using home equity financing, that is, in which the reduction in credit hits hardest. More-exposed counties also see a decline in young firms’ patent applications as well as labor productivity, reflecting young firms’ disproportionate contribution to growth.