Paris December 2021

We introduce a new proxy for the health of financial intermediaries—the Leverage Bearing Capacity (LBC). LBC is the leverage of a fictitious intermediary that targets a fixed level of risk and rebalances its capital structure on an ongoing basis. Our measure is based on market values, available at any frequency, and naturally incorporates high-order risks. Building on an intermediary asset pricing model, we validate LBC theoretically and show that it proxies the marginal wealth of intermediaries. We conduct two event studies to highlight that LBC captures aggregate and financial sector-specific uncertainty. These features translate into superior asset pricing performance.

The "ESG lending" market, where loan contract terms are contingent on borrower ESG performance (i.e., ESG-linked loans), or where loans are issued for specific green projects (i.e., Green loans), has grown exponentially from $6 billion in 2016 to $173 billion in 2019. Much of this growth is driven by ESG-linked loans which are widespread across various industries and well developed capital markets, especially in civil law countries. ESG-linked loans are issued in sizeable amounts by large and publicly listed borrowers, and are often structured through revolving credit facilities by large groups of syndicates led by reputable "ESG specialist" global banks, who keep tight relationships with borrowers. Green loans are smaller project finance vehicles, similar in format to green bonds, yet issued to mostly privately held borrowers. They do not tend to attract large cross-border syndicates. Interestingly, we find no evidence that ESG loan issuance improves borrower or lender ESG performance ex-post, but they tend to be written by those with superior ESG profiles ex-ante. Overall, our results are consistent with ESG-related reputational and signaling incentives among borrowers and lenders as key drivers of the emergence of ESG banking activities around the globe.

Shareholders in locations recently hit by hurricanes significantly increase their support for environmental proposals even if they never previously voted for similar initiatives. Our results show that changed beliefs about salient climate risks rather than firms’ fundamentals drive the increased support. More favorable voting following a hurricane strike has real consequences: Climate-related proposals are more likely to pass, and when they do, firm performance weakens. These findings highlight the role of investor psychology in altering shareholders’ perceptions about climate risks and, consequently, their support for corporate environmental policies.

We analyze the size and power of a large number of \robust" asset pricing tests, investigating the hypothesis that the price of risk of a candidate factor is equal to zero. Different from earlier studies, our bootstrap approach puts all tests on an equal footing and focuses on sample sizes comparable to standard applications in asset pricing research. Thus, our paper provides guidance for researchers about which method to use. We find that the classic Fama-MacBeth/Shanken approach does not over-reject useless factors and provides a reasonable balance between size and power. In contrast, some of the "robust" methods suffer from poor power in realistic sample sizes, especially in situations where the asset pricing model is mildly misspecified.

We quantify the implications of tropical cyclone-related physical risks due to climate change. We generate synthetic cyclones consistent with climate scenarios of the Couple Model Intercomparison Project. Sovereign exposure and vulnerability assessments are based on projections of population densities in shared socioeconomic pathways coupled with downscaled physical asset values constructed using mixed data with local damage functions. We compute the direct climate impact on emerging countries’ bond spreads using their sensitivity to the debt to GDP ratio, assuming that damage costs are financed by issuing new government debt. The ‘business as usual’ RCP8.5 scenario coupled with the ‘middle road’ pathway leads to global average annual damages 142% larger than in the scenario RCP2.6 allowing to remain under 2°C warming. In 2070-2100, we expect the impact of extreme cyclones on the bond spread of most vulnerable countries to be 200 bps higher in the RCP8.5 pathway than in the 2°C baseline.

This study provides a new perspective to understand the rise and future potential of FinTech lending by linking it to the informational role of cashless payments. We uncover both theoretically and empirically a synergy between FinTech lending and cashless payments. FinTech lenders screen borrowers more efficiently when borrowers use more cashless payments that produce transferrable and verifiable information. Because borrowers expect lenders to rely on such payment information to screen them, a strategic consideration for a borrower to stand out of other borrowers then pushes more borrowers to adopt cashless payments. Using novel loan-level data from a large Indian FinTech lender who focuses on small-business lending, we find that a larger use of verifiable cashless payments (relative to cash) predicts a higher chance of loan approval, a lower interest rate, and lower default conditional on the interest rate obtained. These relationships are more pronounced for higher-quality firms. The uncovered synergy provides a plausible explanation for the joint rise of FinTech lending and cashless payments, and suggests an alternative banking model without a balance sheet or traditional banking relationships. Our findings also provide new policy implications on data sharing and open banking.

We introduce an international active fund management industry model in which competing managers, each having heterogeneous incentives (effort productivities, costs) for searching domestic versus foreign investment opportunities. In equilibrium, incentive heterogeneity leads to a novel prediction: increasing foreign competitiveness, which improves (worsens) domestic manager incentives, induces an increase (decrease) of both domestic performance and size. Empirically, we find that 30 global markets’ performance and size, on average, decrease with U.S. concentration. This evidence is consistent with our theoretical predictions but is inconsistent with extrapolation of single-country (implying homogeneous incentives) equilibria to one “global village” [e.g., Feldman, Saxena, and Xu (2020)].

This study investigates the effect of stock market overvaluation of non-peer firms on firm investment measured by capital expenditures. To test this effect, Stambaugh et al.' s (2015) misvaluation measure and Text-Based Network Industry Classification (TNIC) codes are used. The results indicate that firm investment is negatively associated with the overvaluation of non-peer firms. Using a path analysis, it is shown that the misvaluation of non-peer firms influences firm investment through financing and non-financing channels. The financing channel mainly works via debt issuance, but the predominant part of this effect is driven by the non-financing channel. Moreover, the empirical results suggest that the effect of non-peer misvalutaion on firm investment is stronger in bubble periods. The findings are consistent with the idea that overvalued firms are more attractive to investors and other stakeholders, which crowds out firm investment of non-peer firms in other industry sectors.

Firms often bundle announcements of corporate events, such as dividend changes and repurchases, together with quarterly earnings news. This paper studies the real consequence of this disclosure practice in the context of the market for corporate control. I find bundled repurchase announcements significantly increase the announcer's likelihood of becoming a takeover target. Daily Bloomberg news readership data confirms increased awareness is a channel for this effect. Consistent with this channel, targets in bundling-induced takeovers are ex ante less visible. To facilitate causal inference, I use difference-in-differences and instrumental variable estimations to alleviate omitted variables and self-selection concerns.

Prior literature mostly finds bond yield spreads to be insufficiently explained by credit risk in structural models (the 'credit spread puzzle'). In contrast, several recent results consider credit spreads to consist of credit risk to a substantially larger extent (if not even entirely). We address this dissent in the literature using a different empirical methodology. We utilize the removal of sovereign guarantees for savings banks and state banks in Germany as a unique quasi-natural experiment allowing identification of the credit risk component. During a transition period of over ten years, bonds of the same issuer with and without credit risk could be directly compared. Interestingly, less than 20% of the yield spread is due to credit risk for these bonds.

This paper investigates heterogeneity in residential property yields using rental and sale listings from a major German online real estate platform between 2007 and 2017. Equipped with property-level rent-to-price ratios obtained by matching properties for sale and for rent, we show that these yields strongly co-move with regional factors, such as population age structure, industry structure, housing supply rigidities, and the liquidity and size of the housing market. Regional differences are particularly pronounced between globally relevant cities and other areas. However, a large fraction of the variation of yields can be explained neither by local factors nor by an extensive array of property-specific observable features, possibly pointing to the crucial role of idiosyncratic factors, as well as of within-city aggregation effects, informational frictions, and regulatory restrictions.

We study an intrinsic property of Bayesian information processing which does not rely on individuals having rational absolute beliefs: two equally-diagnostic signals of opposite direction should cancel out. Using evidence from both the lab and field, we show that individuals not always follow this counting-based principle. Systematic violations occur whenever a sequence of identical evidence is interrupted by a signal of opposite direction, which produces strong and robust overreactions. Conversely, individuals correctly follow this counting-based principle whenever signals alternate while they underreact to sequences of same-directed evidence. Next, we empirically analyze announcement and post-announcement stock return reactions in financial markets. Consistent with our experimental evidence, we find that initial stock reactions are significantly stronger and subsequent price drifts weaker for opposite-directed earnings surprises than for same-directed earnings surprises. Our results provide novel insights to the paradoxical co-existence of over- and underreaction to new information at the individual and market level.

How does corporate reorganization affect labor reallocation in bankruptcy? In this paper, we provide evidence that reorganization is an important source of labor insurance against bankruptcy shocks, including for workers who eventually move to other firms. We measure the effect of reorganization on labor outcomes using data from Portugal and the random allocation of corporate reorganization cases to judges, which creates exogenous variation in the probability of reorganization. Reorganized firms only keep about 20\% of their workforce five years after reorganization and we find no evidence that reorganization affects the reallocation of workers to efficient of profitable firms. However, reorganization has a positive and persistent effect on wages. In the short term (first year after reorganization), workers are more likely to have jobs. In the longer term (subsequent five years), workers have higher paying jobs. Consistent with the literature on the scarring effect of negative production shocks, reorganization reduces labor transitions to less skill intensive occupations with lower wage premia. Finally, we show that reorganization provides labor insurance to workers who move to new employers. Reorganization reduces the probability that workers move to low-paying jobs but increases the probability that they find high paying jobs in new employers.

This paper presents an equilibrium model of dynamic trading, learning, and pricing by strategic investors with trading targets and price impact. Since trading targets are private, rebalancers and liquidity providers filter the child order flow over time to estimate the latent underlying parent trading demand imbalance and its expected impact on subsequent price pressure dynamics. We prove existence of the equilibrium and solve for equilibrium trading strategies and prices in terms of the solution to a system of coupled ODEs. We show that trading strategies are combinations of trading towards investor targets, liquidity provision for other investors’ demands, and front-running based on learning about latent underlying trading demand imbalances and future price pressure.

We quantify how bargaining power is distributed when spouses make financial decisions together. We build a model in which each spouse has a risk preference and must bargain with each other to make asset decisions for the household. By structurally estimating the model with longitudinal data from Australian households, we show that the average household’s as- set allocation reflects the husband’s risk preference 44% more than the wife’s. This gap in bargaining power is partially explained by gender differences in income and employment sta- tus, but is also due to gender effects. We provide further evidence that links the distribution of bargaining power to views on gender norms in the cross-section.

Does a firm benefit if its employees personally invest in start-ups? To answer this question, we exploit novel data, which link angel investors in the US with their employment history. A firm's economic value of patents decreases by 3% - 5% when its employees personally invest in start-ups. We establish causality with matching and instrumental variable regressions, which rely on quasi-exogenous competition in the early-stage financing market. The negative relationship is stronger for angel investors in innovation-related roles, if the start-ups are more time consuming, and for exploratory patents. Compared to other angel investors, angel investors employed at corporations have a positive impact on future innovation and success of their invested start-ups. Our results indicate that angel investors divert time and effort from their employers to their personal investments. We highlight a trade-off between the benefits of angel investors for start-ups and the costs for their employer.

This paper shows a strong link between granular information contained in individual stock prices and sectoral movements. Using machine learning algorithms, we find that a predictor that aggregates the price movements of a broad cross-section of individual stocks predicts sector ETF returns out-of-sample. When we combine the structural information of economic links among firms with machine learning, the resulting information signals have even stronger return predictability. These results support the hypothesis of granular origins of aggregate shocks, and illustrate the advantages of structural machine learning.

This paper provides the first evidence of positive bank-to-bank spillovers. I show that geographic linkages between banks that engage in home lending in the same geographic region transmit positive shocks from one bank to another. I exploit shocks to the deposit base of banks located in counties experiencing shale oil booms – and show that a non-shocked bank in a non-boom county expands lending more if its linkages have greater exposure to shale booms. Results show that the shock exposure of linkages has a positive impact on home prices of non-boom counties and non-shocked banks located therein respond with increased lending.

We investigate the replenishment of 102 asset-backed securities (ABS) backed by more than 1.7 million small- and medium-sized enterprise loans. Based on our extensive data set from 2012 to 2017 obtained from the first and only central loan-level repository for ABS in Europe, we reveal that loans added to securitized loan portfolios after the transactions' closing perform worse than loans that are part of the initial portfolio. On average, we find that loans added to securitized loan portfolios demonstrate a 0.42 percentage points higher probability of default. We additionally provide evidence that originators induce these performance differences since they exploit their information advantage by deliberately adding low-quality loans to securitized loan portfolios. This adverse behavior is mitigated by originators' reputation efforts, by increasing transparency in the ABS market, as for example per the European Central Bank's loan-level initiative, and most effectively by their interaction.

We study the role of Western CEO incentives in fostering the technological rise of China. Due to China's quid pro quo policy, foreign multinationals face a trade-off between the short-term benefits of accessing China's vast market and the long-term costs of transferring technology to China. Leveraging microdata on the global patent network, we construct novel measures to describe technological interactions between US firms and over 70 countries. We find that firms managed by myopic CEOs form more partnerships with China and transfer more technology to China. These firms subsequently lose R&D human capital to China and face more patenting competition from China, suggesting negative long-term consequences in innovation. The results reveal an important real effect of CEO incentives and highlight a novel channel behind China's technological catch-up.

We develop a model in which long-term swap spreads are determined by preferred habitat investors' demand for swaps, constrained intermediaries' supply of swaps, and compensation for the risk that spreads temporarily widen due to future shocks to demand or supply. Empirically, we identify these separate demand and supply factors, and assess their respective contributions to the level of swap spreads and the returns on swap spread trades.

We provide the first analysis of the risk exposure and consequent risk-adjusted performance of impact investing funds, private market funds with dual financial and social goals. We introduce a new dataset of impact fund cash flows constructed directly from financial statements. When accounting for market risk exposure, impact funds underperform the market, but outperform venture capital (VC) funds, consistent with the presence of substantial frictions in private markets. Impact funds perform on par with funds matched on size, asset class, and vintage years. We exploit known distortions in measures of VC performance to characterize the risk profile of impact funds. Impact funds have substantially lower market beta than VC funds, contradicting the idea of sustainability as a “luxury good.” Adding additional factors does not change the estimate of performance.

When a liquidity crisis hits non-bank financial intermediaries, which central bank interventions help? We show that investment funds faced large investor outflows as the COVID-19 shock hit and assess the effectiveness of central bank asset purchases and additional liquidity provision to banks in alleviating the crisis. We use detailed fund-level data and proprietary data on bank take-ups in liquidity-providing operations and bank-fund repo transactions. Analyzing asset purchases, we find that funds with higher shares of assets eligible for central bank purchases in their portfolio before the COVID-19 crisis saw their performance improve by 3.7% and outflows decrease by 63% relative to otherwise similar funds. Analyzing repo activity, we find that additional central bank liquidity provision supported bank repo lending to funds, by alleviating bank liquidity constraints. Banks more exposed to the March 2020 liquidity crisis that took up central bank liquidity increased their repo transactions with funds by 3% to 4% compared to other banks. Our results suggest that central bank interventions were effective in stopping fire-sale dynamics and staving off runs on non-bank financial intermediaries, even though funds did not have direct access to the lender of last resort.

Identifying outperforming mutual funds ex-ante is a notoriously difficult task. We use machine learning to exploit numerous fund characteristics and construct portfolios of equity funds that earn out-of-sample annual alpha of 4.2% net of costs. We show that such performance is the joint outcome of both exploiting multiple fund characteristics and allowing for flexibility in the relation between characteristics and performance. We demonstrate that even retail investors can benefit from investing in actively managed funds. The performance of our portfolios has declined over time, however, consistent with increased competition in asset markets and diseconomies of scale at the industry level.

We study how the COVID-19 pandemic reshaped the relation between corporate and sovereign credit risk in the cross-section of countries in the European Union. Surprisingly, the outbreak triggered higher elasticity of corporate to sovereign CDS spreads in core countries, which realigned to that of peripheral countries, with lower fiscal capacity, for which the impact of the pandemic on the elasticity was essentially muted. During the pandemic, we observe systematic departures of actual CDS from those implied by a standard structural model of default for larger firms in core EU countries with budgetary slackness. We interpret this evidence in light of a disaster-risk asset pricing model with bailout guarantees and defaultable public debt. Based on the model and a synthetic control method, we show that CDS-implied risk-adjusted bailout guarantees over the medium term were about three times larger in the Core than in the Periphery.

Failing to account for transaction costs materially impacts inferences drawn when evaluating asset pricing models, biasing tests in favor of those employing high cost factors. Ignoring transaction costs, the Hou, Xue, and Zhang (2015) q-factor model and the Barillas and Shanken (2018) six-factor model models have high maximum squared Sharpe ratios and small alphas across 120 anomalies. They do not, however, come close to spanning the achievable mean-variance efficient frontier. Accounting for transaction costs, the Fama and French (2015, 2018) five-factor model has a significantly higher squared Sharpe ratio than either of these alternative models, while variations employing cash profitability perform better still. More generally, these results highlight the importance of incorporating real-world concerns into financial research.