Knowledge that Transforms

The second special issue on big data in finance showcases advancements in research related to data of large size, high dimension, and complex structure since the first NBER/RFS big data conference. The papers published in this next chapter address some questions that were proposed in the initial special issue in 2021. Other papers are more directly connected to recent developments in the markets. We discuss some new research directions, following on the papers published here. They include evaluating market microstructure reforms, understanding medium-frequency trading, improving missing data imputations, and deepening data valuation. We look forward to more developments to follow.

I show that investor composition affects bond price dynamics and capital allocation during crises. Using large-scale holdings data and within-firm ownership variation across near-identical bonds, I causally identify bond returns’ investor composition elasticities. Corporate bonds held predominantly by insurers rather than mutual funds suffer milder losses in downturns: increasing insurer holdings by half a bond’s size causes 20% shallower drawdowns. A shift-share instrument isolates variation from large insurers’ idiosyncratic primary-market allocations. Differences in intermediaries’ liability structures drive these results, which hold across countries. During crises, firms with more stable bondholders maintain higher borrowing at lower cost and invest more.

We model firms’ allocation of resources across surplus-creating (ie, productive) and surplus-appropriating (ie, rent-seeking) activities. Our model predicts that industry-wide technological advancements, such as recent progress in data collection and processing, induce a disproportionate and socially inefficient reallocation of resources toward surplus-appropriating activities. As technology improves, firms rely more on appropriation to obtain their profits, thereby endogenously reducing the impact of technological progress on economic progress and inflating the price of the resources used for both types of activities. Our theoretical insights shed light on the rise of high-frequency trading.

Using a semisupervised topic model on 7 million New York Times articles spanning 160 years, we test whether topics of media discourse predict future stock market excess returns to test rational and behavioral hypotheses about market valuation of disaster risk. Media discourse data address the challenge of sample size even when disasters are rare. Our methodology avoids look-ahead bias and addresses semantic shifts. Our discourse topics positively predicts market excess returns, with War having an out-of-sample R^2 of 1.35%. We call this effect the war return premium. The war return premium has increased in more recent time periods.

We show a negative effect of opioid prescriptions on subsequent individual employment among employers in our sample using doctor-opioid-prescribing propensity as our instrument. This finding has implications for firms that must now contend with lower local labor supply. We find a negative relationship between opioid prescriptions and subsequent establishment growth. However, firms respond to labor shortages by investing more in technology, replacing the relatively scarcer labor with capital, especially when they are not financially constrained. We find positive abnormal returns, upon the passage of state laws intended to limit opioid prescriptions, that are driven by firms more reliant on labor.

We document political sentiment effects on U.S. inventors. Democratic inventors are more likely to patent (relative to Republicans) after the 2008 election of Obama but less likely after the 2016 election of Trump. These effects are at least twice as strong among politically active Democrats and are present even within firms and within firm$ × $technology. We also show that partisans tend to cluster in technologies (e.g., Democrats in Biotechnology and Republicans in Weapons), so that sentiment effects aggregate to more patents in the technologies dominated by the winning party.

We show that widely used macroprudential regulations that rely on historical cost accounting (HCA) to insulate banks’ balance sheets from financial market volatility significantly affect the transmission of monetary policy onto bank lending. Using detailed supervisory data from Italian banks, we find that HCA mutes the transmission of quantitative easing and other monetary policies that affect the long end of the yield curve, weakening the effectiveness of interventions aimed at reducing firm credit constraints. We suggest alternative policies that have the benefits of HCA but allow monetary policy to pass through.

We analyze effects of tacit collusion in a dynamic general equilibrium model of oligopolistic sectors with capital investment and real frictions. Through their effects on equilibrium- and off-equilibrium stock prices, fundamental shocks affect incentives for defection from tacit collusion, amplifying the interaction between the real economy and financial markets as well as firms’ risk exposure. The model implies ambiguous relationship between industry concentration and equity returns depending on operating leverage, which helps reconcile conflicting evidence in the literature. We find quantitative and empirical support for novel theoretical predictions regarding the effects of concentration on returns and investment.

We study how bank advertising practices contribute to racial disparities in financial markets by steering minorities toward inferior products. We exploit a regulatory change that incentivizes deposit collection at the Freedman’s Savings Bank, the first institution to collect deposits from African Americans post-Emancipation, by facilitating misuse of depositor funds. After deregulation, advertising volume rises, particularly in African American newspapers, leading to increased deposits from Black depositors despite the bank’s insolvency. We identify persuasion as a key mechanism; prescriptive stereotypes and false claims likely amplify advertising’s impact on African Americans, exacerbating historically large depositor losses after the bank’s collapse.

Exchange-traded funds (ETFs) are typically viewed as passive index trackers. In contrast, we show that corporate bond ETFs actively manage their portfolios, trading off index tracking against liquidity transformation. In our model, ETFs optimally choose creation and redemption baskets that include cash and only a subset of index assets, especially if those assets are illiquid. Our evidence supports the model. We find that ETFs dynamically adjust their baskets to correct portfolio imbalances while facilitating ETF arbitrage. Basket inclusion improves bond liquidity in general, but worsens it in periods of large imbalance between creations and redemptions, such as the COVID-19 crisis.