Knowledge that Transforms

This article investigates spillovers from foreign economies to the U.S. through changes in long-term Treasury yields. We document a decline in the contribution of U.S. domestic news to the variance of long-term Treasury yields and an increased importance of overnight yield changes, a proxy for foreign shocks’ contribution to U.S. yields. A model that identifies U.S., Euro area, and U.K. shocks that move global yields suggests that foreign shocks account for at least 20% of the daily variation in long-term U.S. yields in recent years. We also document the predictability of long-term U.S. yields by the U.S.–foreign yield spread.

We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based on observable characteristics, using machine learning principles with linear models. The best-performing methods are flexible two-stage sparse models that capture group-membership predictive relationships. Portfolios formed to exploit such group-varying predictions based on a parsimonious set of characteristics deliver economically meaningful returns with low turnover. We propose statistical tests based on nonparametric bootstrapping for our results, and detail how different characteristics may matter for different groups of firms, making comparisons to the existing literature.

We investigate the implications of recovering real-world conditional expectation of return functions using options on the S&P 500 index and Treasury bond futures. First, we construct estimates of the probability of disasters, defined as higher than 6%, 5%, or 4% equity market declines over option expiration cycles. This measure of disaster probability forecasts realized disasters. Second, we employ options on the futures of the 10- and 30-year Treasury bonds to construct estimates for the expected return of bond futures. These measures display forecasting ability for subsequent futures returns beyond the level, slope, and curvature variables extracted from the yield curve.

We study how the market return depends on the time of the day using E-mini S&P 500 futures actively traded around the clock. Strikingly, 4 hours around European open account for the entire average market return. This period’s returns have a 1.6 Sharpe ratio and remain high after transaction costs. Average returns are a noisy zero during the remaining 20 hours. High returns are consistent with European investors processing information accumulated overnight and thus resolving uncertainty. Indeed, uncertainty reflected by VIX futures prices rises overnight and falls around European open. The results are stronger during the 2020 COVID crisis.

We study the role of state controlling shareholders in corporate payout policy. The State Capital Operation Program in China requires parent central state-owned enterprises (CSOEs) to contribute part of their consolidated income to a new fiscal fund. We find that listed CSOEs, partially controlled by parent CSOEs, experience significant reductions in dividend payouts as the income-contribution ratio increases. The dividend reductions are concurrent with increases in intragroup resource transfers— listed CSOEs’ loans to, and commercial trades with, group peers. The program yields adverse consequences for listed CSOEs’ investment and employment, yet being mitigated by group-level dividend reductions.

Practitioners allocate substantial resources to technical analysis whereas academic theories of market efficiency rule out technical trading profitability. We study this long-standing puzzle by applying a diverse set of machine learning algorithms. The results show that an investor can find profitable technical trading rules using past prices, and that this out-of-sample profitability decreases through time, showing that markets have become more efficient over time. In addition, we find that the evolutionary genetic algorithm’s attitude in not shying away from erroneous predictions gives it an edge in building profitable strategies compared to the strict loss-minimization-focused machine learning algorithms.

We document a novel strategic motive for family business groups to utilize their internal capital markets (ICMs) during financial crises. We find that crisis-period group ICM activity is targeted toward exerting product market dominance over standalone rivals. Groups make significant post-crisis gains in market share that are concentrated among affiliates (and industry segments within affiliates) operating in highly competitive product markets, where capturing such gains is difficult in normal times. These patterns are observed only in emerging markets, suggesting that ICMs enable groups to exploit crises to realize long-term competitive advantages only when rivals face chronic financing frictions.

We decompose long-term nominal bond yields into real and inflation components in an international context using inflation-linked and nominal bonds. In contrast to extant results, real rate variation dominates the variation in inflation-linked and nominal yields. Cross-country nominal and inflation-linked yield correlations have declined since the Great Recession. Real rates are the main source of the correlation between nominal yields. Our results are robust to various alternative measurements of inflation expectations and the liquidity premium. They continue to hold when a no-arbitrage term structure model with real, nominal, and inflation factors is used to effect the yield decomposition.

We examine whether natural disaster experiences affect households’ portfolio choice decisions. Using data from the National Longitudinal Survey of Youth 1979, we find that adversely affected households are less likely to participate in risky asset markets. After a disaster shock, households become more risk-averse and lower their expectations on future stock market returns. Such conservative portfolio choices persist even after households relocate to less disaster-prone areas, consistent with risk preferences being altered by disaster experiences. Overall, our evidence suggests that transient but salient experiences can be an important factor in explaining the limited participation puzzle.

The average maturity of newly issued corporate bonds has declined substantially over the past 40 years, and the traditional determinants of debt maturity fail to explain this decline fully. We show that the changing composition of investors in the corporate bond market influences bond maturities. The results of a Granger causality test, an instrumental variable approach, and a natural experiment suggest that a decline in the insurance companies’ – which prefer long-term bonds – ownership share in the corporate bond market explains a significant part of the unexplained maturity decline. These findings illustrate how investor preferences can have real effects on corporations.