Knowledge that Transforms

We test the trade finance channel of exports by controlling for the bank credit channel. Using Turkey’s July 2012 adoption of Basel II as a quasi-natural experiment, we examine whether shocks to trade financing costs affect exports. With data for 16,662 Turkish exporters shipping 2,888 different products to 158 countries, we find that the share of letters-of-credit-based exports decreases (increases) when the associated risk weights for counterparty exposure increase (decrease) after the adoption of Basel II. However, growth of firm-product-country-level exports remains unaffected. Trade financing might have a lesser role in exports than previously suggested by the previous literature.

We document a little-known source of information exploited by sophisticated institutional investors: the Freedom of Information Act (FOIA), a law that allows for the disclosure of previously unreleased information by the U.S. Government. Through FOIA requests, we identify several sophisticated institutional investors, chiefly hedge funds, that request information from the FDA. We explore the type of information commonly requested by funds and the types of firms that are targets of FDA-FOIA requests and show that FOIA requests allow these investors to generate abnormal returns. Thus, we illustrate a detailed mechanism through which costly information becomes incorporated into market prices.

We investigate the socially optimal design of financial networks, that allows to tackle the trade-off between risk sharing and contagion. We identify conditions on the shock distribution under which full integration or maximal segmentation is optimal. We also show that, under different conditions, the optimal network displays different levels of strength of linkages to other firms or intermediate degrees of segmentation. In the latter case, the individual and social incentives to establish linkages are not necessarily aligned. When firms face heterogeneous distributions of risks, they should optimally form linkages only with firms facing risks of the same kind.

We examine how alumni ties with corporate boards differentially affect male and female analysts' job performance and career outcomes. Connections improve analysts' forecasting accuracy and recommendation impact, but the effect is two to three times as large for men as for women. Connections also contribute to analysts' likelihood of being voted by institutional investors as "star" analysts, but act as a partial substitute to performance for men, while a complement to performance for women. Our evidence indicates that men benefit more than women from connections in both job performance and the subjective evaluation by others.

We study the effect of a bond’s place in its issuer’s maturity structure on credit risk. Using a structural model as motivation, we argue that bonds due relatively late in their issuers’ maturity structure have greater credit risk than do bonds due relatively early. Empirically, we find robust evidence that these later bonds have larger yield spreads and greater comovement with equity and that the magnitude of the effects is consistent with model predictions for investment-grade bonds. Our results highlight the importance of bond-specific credit risk for understanding corporate bond prices.

We develop an international financial market model in which domestic and foreign residents differ in their beliefs about the information content in public signals. We determine how informational advantages of domestic investors in the interpretation of home public signals affect equity markets. We evaluate the ability of our model to generate four international-finance anomalies: (i) the co-movement of returns and capital flows, (ii) home-equity preference, (iii) the dependence of firm returns on home and foreign factors, and (iv) abnormal returns around foreign firm cross-listing in the home market. Their relationships with empirical differences-of-opinion proxies are consistent with the model. Received January 15, 2011; editorial decision May 16, 2016 by Editor Geert Bekaert.

Markowitz (1952) portfolio selection requires an estimator of the covariance matrix of returns. To address this problem, we promote a nonlinear shrinkage estimator that is more flexible than previous linear shrinkage estimators and has just the right number of free parameters (i. e., the Goldilocks principle). This number is the same as the number of assets. Our nonlinear shrinkage estimator is asymptotically optimal for portfolio selection when the number of assets is of the same magnitude as the sample size. In backtests with historical stock return data, it performs better than previous proposals and, in particular, it dominates linear shrinkage.

We study the dynamics of high-frequency market efficiency measures. We provide evidence that these measures comove across stocks and with each other, suggesting the existence of a systematic market efficiency component. In vector autoregressions, we show that shocks to funding liquidity (the TED spread), hedge fund assets under management, and a proxy for algorithmic trading are significantly associated with systematic market efficiency. Thus, stock market efficiency is prone to systematic fluctuations, and, consistent with recent theories, events and policies that impact funding liquidity can affect the aggregate degree of price efficiency.

State-of-the-art term structure models of commodity prices have serious difficulties extrapolating the prices of long-maturity futures contracts from short-dated contracts. This situation is problematic for valuing real commodity-linked assets. We estimate a nonlinear four-factor continuous time model of commodity price dynamics. The model nests many previous specifications. To estimate the model, we use crude oil prices and inventories. The inventory data and nonlinear price dynamics have a large impact on oil price forecasts. The additional factor in our model compared with current three-factor models has a significant impact on model-implied long-maturity futures prices.

We propose a new approach for estimating expected returns on individual stocks from a large number of firm characteristics. We treat expected returns as latent variables and apply the partial least squares (PLS) estimator that filters them out from the characteristics under an assumption that the characteristics are linked to expected returns through one or few common latent factors. The estimates of expected returns constructed by our approach from 26 firm characteristics generate a wide cross-sectional dispersion of realized returns and outperform estimates obtained by alternative techniques. Our results also provide evidence of commonality in asset pricing anomalies.