This paper focuses on actively managed bond mutual funds, whose performance is most sensitive to monetary policy shocks. We document significant and persistent FOMC-day outperformance by macro-active bond funds, which is particularly pronounced during periods of heightened macroeconomic disagreement and attention. Consistent with their forecasting ability, these funds’ pre-FOMC portfolio duration adjustments can predict FOMC-day monetary policy shocks beyond the information offered by economic data and asset returns. Furthermore, we show that this macro-investing skill extends to GDP and CPI announcements and is consistently shared across various fund styles within fund families.
AI promises to accelerate and broaden access to automated investment advice. But can it capture the investment preferences and rationales of historically underrepresented investors? We ask 1272 human survey respondents and 1350 AI-generated agents to rate stocks, bonds, and cash. First, default AI-generated responses overrepresent young, high-income individuals. However, algorithmic bias is reduced with demographically-seeded prompts. Second, AI-generated free-form responses correctly reflect human rationales: risk and return, financial knowledge, and past experiences. Third, AI can help identify where a lack of financial knowledge poses issues in human responses, as shown in textual analyses of transitivity violations.
Firms often expose themselves to currency risk by borrowing in foreign currencies, even when the local currency actually provides a better hedge in downturns. Motivated by this novel fact, we develop an international corporate finance model in which firms facing adverse selection choose the foreign currency share of their debt. In the unique separating equilibrium, good firms optimally expose themselves to currency risk to signal their type. Crucially, the nature of this equilibrium depends on the co-movement between cash flows and the exchange rate. We provide evidence consistent with the predictions of the model using a granular dataset including more than 4800 firms in 19 emerging markets and 5700 firms in 22 advanced economies between 2005 and 2021. The signaling mechanism is most salient for firms in emerging markets and also those subject to greater information asymmetries in advanced economies. These findings are important for assessing and managing risks from currency mismatches on corporate balance sheets.
Tax loss carryforwards (TLCF) — accumulated losses that reduce taxable income — are an important and risky corporate asset. We show theoretically how TLCF affect equity risk: If TLCF are low and used with certainty, equity risk decreases in TLCF because TLCF represent a safe cash flow; if TLCF are high, equity risk increases in TLCF because TLCF increase after-tax cash flows in good times but may expire unused or be deferred in bad times. In a calibrated model the latter effect dominates and risk increases in TLCF on average. Empirically, TLCF positively forecast firms’ volatility, beta, and return.
Are banks safer when they hold more safe assets? This paper builds a model in which banks supply liquidity services through uninsured deposits. Define a safe asset as an asset with both low payoff risk and high pledgeability. Banks consider equity costly and because of safe assets’ low payoff risk prefer to fund them largely with deposits. High pledgeability makes this feasible. The rise in banks’ deposit supply increases deposit rates and forces banks to deviate from socially desirable levels of capital to meet return-on-equity targets. Regulatory leverage ratios can be tightened to increase welfare.
