Knowledge that Transforms

This paper introduces a new metric, α, to benchmark the performance of individual private equity funds. Our metric is substantially less sensitive to noise in fund cash flows compared to the popular public market equivalent (PME) and its generalization (GPME), while having the same aggregate pricing implications as GPME. For a large data set of fund cash flows, α estimates have much lower standard deviation across funds than does (G)PME. For buyout funds, PME and α are close, but deviate in certain subsamples. Using α increases power in regressions involving fund performance and improves performance predictability of future funds.

We identify a new source for the declining role of small banks in the banking industry: Long-term changes in the banking sector are partially a consequence of changes in the industrial sector. Small banks are relatively more exposed to small business shocks, because small businesses compose a larger share of their customers. Lower real-side demand for small business financial services is responsible for part of the relative decline in small banks’ deposits. Rough calculations suggest that deposits at small banks would have been $280 billion higher from 2002 to 2017 if small firms had grown at the rate of larger firms.

We provide experimental evidence of a positive memory bias that affects individuals’ beliefs, decisions to reinvest, and overconfidence in the stock market. Individuals overremember positive investment outcomes of chosen assets and underremember negative ones. Based on their memories, subjects form overly optimistic beliefs about their investment, reinvest too much, and become overconfident about their investment ability relative to others. We further provide evidence on motivation driving the memory bias. This positive memory bias offers a cognitive microfoundation for why gains weight more than losses when people learn from experiences. This helps reconcile various stylized facts in investor beliefs and behavior.

We investigate the predictive power of loan spreads for forecasting business cycles, specifically focusing on more constrained, intermediary-reliant firms. We introduce a novel loan-market-based credit spread constructed using secondary corporate loan-market prices over the 1999 to 2023 period. Loan spreads significantly enhance the prediction of macroeconomic outcomes, outperforming other credit-spread indicators. We also explore the underlying mechanisms and differentiate between borrower fundamentals and financial frictions. Evidence suggests that supply-side frictions are a decisive factor in the forecasting ability of loan spreads.

We propose the currency hedging channel that connects countries’ external imbalances to their exchange rate behavior. We present a model in which investors increase their currency hedging during periods of financial distress in proportion to their net foreign asset exposure. This behavior coupled with constrained financial intermediation explains observed relationships between gradually adjusting external imbalances and volatile spot and forward exchange rates. We find empirical support for the hedging channel in both the conditional and unconditional moments of exchange rates, option prices, and countries’ uses of Federal Reserve swap lines.

This paper shows that private equity (PE) ownership, in private-to-private buyouts, leads to a reduction in pollution when the target company faces high potential liabilities for polluting. Conversely, PE-backed firms increase pollution when environmental liability risks are low, as shown by a novel natural experiment that reduced these risks for projects located on federal land. Exploiting specific PE deals within the energy industry, I find that PE governance is the main driver of the results. The results suggest that increasing litigation and regulation-related risks can mitigate the potentially detrimental effects of PE ownership on stakeholders.

I show that memory-induced attention can distort prices in financial markets. I exploit rigid earnings announcement schedules to identify which firms are associated in investors’ memory. Firms with randomly overlapping earnings announcements are associated in memory because many investors experience them in the same context. Months later, when only one of the two firms announces earnings, this context is cued, and triggers the recall of the other, associated firm. On such days, I find that memory-induced attention leads to buying pressure in the associated firm’s stock. The strength of this effect varies as predicted by associative memory theory.

We study depositor behavior and capital flight using data from the Freedman’s Savings Bank, a bank established after the U.S. Civil War to support formerly enslaved individuals. White depositors, who generally had better access to financial information, begin providing significant funding as interest terms are enhanced. They respond faster to postpanic stabilization efforts and negative bank-specific information, and are twice as likely to close accounts before failure, passing expected losses to Black depositors, who were the bank’s primary philanthropic target. Our results show how demand deposit funding of bank-like institutions can create systemic disadvantages for less experienced borrowers.

This paper argues that a lesser known yet economically significant tax-deferral feature of ETFs’ security design is crucial to their success. By relying on the in-kind redemption exemption, authorized participants help ETFs avoid distributing capital gains and reduce their tax overhang, partly by deploying heartbeat trades. We estimate that the ETF tax efficiency has increased long-term investors’ after-tax returns by 1.05% per year relative to mutual funds in recent years. Exploiting cross-sectional and time-series variations in investors’ tax burden, we show that tax efficiency is a significant driver of capital migration by high-net-worth investors from mutual funds into ETFs.

This paper explores implications of weak identification in common ‘long memory’ and recent ‘rough’ approaches to modeling volatility dynamics of financial assets. We unveil an asymptotic near-observational equivalence between a long memory model with weak autoregressive dynamics and a rough model with a near-unit autoregressive root. Standard methods struggle to distinguish them, and conventional asymptotics are invalid. We propose an identification-robust approach to construct confidence sets that reveal the uncertainty and aid inference. Empirical studies based on realized volatility and trading volume often fail to statistically reject either model, thereby providing evidence of their potential coexistence.