This study shows that shifts in political climate influence stock prices. As the party in power changes, there are systematic changes in the industry-level composition of investor portfolios, which weaken arbitrage forces and generate predictable patterns in industry returns. A trading strategy that attempts to exploit demand-based return predictability generates an annualized risk-adjusted performance of 6% during the 1939 to 2011 period. This evidence of predictability spans 17%–27% of the market and is stronger during periods of political transition. Our demand-based predictability pattern is distinct from cash flow-based predictability identified in the recent literature.

This study examines household portfolio choice through the retirement transition. I show that couples significantly decrease their stock allocations after retirement, whereas singles’ allocations remain relatively unchanged. Reallocations are concentrated among couples in which the wife is more risk averse than her husband. Husbands’ and wives’ respective retirement events are followed by opposite-signed changes in stock allocations. These findings are consistent with a model of collective household decision making in which spouses have heterogeneous risk preferences, and suggest that dynamics in the distribution of intrahousehold bargaining power generate time-varying household risk aversion.

Equity markets fail to account for the value-relevant nonpublic information enjoyed by syndicated loan participants and reflected in publicly posted loan prices. A long-short strategy that buys (sells) the equities of firms with recently appreciated (depreciated) loans earns large risk-adjusted returns, suggesting a surprising and economically important level of segmentation across the same firm’s capital structure. The information lag captured by trading strategy returns is not affected by drivers of firm-specific attention, including the publication of loan returns in the Wall Street Journal. Instead, returns to the strategy are eliminated among equities held by mutual funds also trading in syndicated loans.

This study shows that shifts in political climate influence stock prices. As the party in power changes, there are systematic changes in the industry-level composition of investor portfolios, which weaken arbitrage forces and generate predictable patterns in industry returns. A trading strategy that attempts to exploit demand-based return predictability generates an annualized risk-adjusted performance of 6% during the 1939 to 2011 period. This evidence of predictability spans 17%-27% of the market and is stronger during periods of political transition. Our demand-based predictability pattern is distinct from cash flow-based predictability identified in the recent literature.

Contrary to the predictions of traditional life-cycle models, household consumption is excessively sensitive to current income. Similarly, weak evidence of income hedging runs against standard portfolio theory. We link these two puzzles by modifying the theoretical framework of Viceira (2001) to study how consumption-income sensitivities generated by income in the utility function affect households' portfolio choices. Empirically, we find that consumption-income sensitivities affect asset allocation through the income hedging channel. In particular, we show that the interaction between consumption-income sensitivity and the correlation of income growth to stock market returns is an important explanatory variable for households' stock market holdings. Received October 20, 2016; editorial decision April 25, 2018 by Editor Wayne Ferson.

We examine whether corporate bankruptcies influence bank loan characteristics of geographically proximate firms. Controlling for industry contagion and local economic conditions, firms headquartered near a bankruptcy event experience a 7 basis point increase in loan spreads. The effect is transitory and cannot be fully explained by local correlated information or lenders’ financial health. Instead, the effect is more pronounced for informationally opaque bankruptcies and borrowers, and weakened among loans with relationship lenders and lenders with significant local presence.

Combining granular daily data on temperatures across the continental United States with detailed establishment data from 1990 to 2015, we study the causal impact of temperature shocks on establishment sales and productivity. Using a large sample yielding precise estimates, we do not find evidence that temperature exposures significantly affect establishment-level sales or productivity, including among industries traditionally classified as “heat sensitive.” At the firm level, we find that temperature exposures aggregated across firm establishments are generally unrelated to sales, productivity, and profitability. Our results support existing findings of a tenuous relation between temperature and aggregate economic growth in rich countries.

Climate scientists project rising average temperatures and increasing frequency of temperature extremes. We study how extreme temperatures affect corporate profitability across different industries and whether sell-side analysts understand these relationships. We combine granular daily data on temperatures across the continental U.S. with locations of public companies’ establishments and build a panel of quarterly firm-level temperature exposures. Extreme temperatures significantly impact earnings in over 40% of industries , with bi-directional effects that harm some industries while others benefit. Analysts and investors do not immediately react to observable intra-quarter temperature shocks, though earnings forecasts account for temperature effects by quarter-end in many industries.

Using detailed data on U.S. households’ locations, employment, and financial portfolios, we document that individuals employed in locally clustered industries are more likely to invest in risky assets. This pattern is strongest among individuals with high labor income, employed in skilled occupations, and with strong cognitive skills. Our overall evidence suggests the relation between industry clusters and investment decisions is best explained by clusters enhancing human capital among local industry workers, in turn amplifying their effective risk tolerance. Our findings highlight the important role of local labor market composition in generating household portfolio patterns within and across geographies.

We propose a theoretical measure of income hedging demand and show that it affects asset prices. We focus on the value factor and first demonstrate that our demand estimates are correlated with the actual demands of retail and mutual fund investors. We then show that the aggregate high‐minus‐low (HML) demand predicts HML returns. Exploiting the state‐level variation in income risk, we demonstrate that state‐level hedging demands predict state‐level HML returns. A long‐short portfolio that exploits this hedging‐induced predictability earns an annualized risk‐adjusted return of 6%.