Knowledge that Transforms

Regulatory loan ceilings are commonly found in the prosocial lending sector, yet they can have unintended perverse effects. By mitigating the risk of adverse selection, loan caps catalyze co-financing arrangements between subsidized lenders and commercial banks. These arrangements can, in turn, crowd out the most vulnerable borrowers, i.e., those typically targeted by regulators. To assess this claim, we proceed in two steps. First, we build a theoretical model. Second, we test it, drawing on a rich hand-collected dataset on the clientele of an unregulated French microcredit provider that turned into a regulated institution—following a shock affecting its funding sources. Using a difference-in-differences linear probability model with propensity score matching, we empirically confirm the theoretical prediction that the imposition of a loan ceiling will lead to mission drift.

In this paper, we propose a method to analyze the structure of the credit market. Using historical data from Japan, we explore banks’ lending patterns to the real economy. We find that generalist banks (with diversified lending) and specialist banks (with focused lending) coexist, and tend to stick to their strategies over time. Similarly, we also document the coexistence of generalist and specialist industries (based on their borrowing patterns). The observed interaction patterns in the credit market indicate a strong overlap in banks’ loan portfolios, mainly due to specialist banks focusing their investments on the very same generalist industries. A stylized model matches these patterns and allows us to identify economically meaningful sets of generalist banks/industries. Lastly, we find that generalist banks are not necessarily less vulnerable to shocks compared to specialists. In fact, high leverage levels can undo the benefits of diversification.

This paper examines the daily responses of market interest rates to three monetary policy announcements in China using the event-study approach. I find that the interest rate responses to announced changes in the regulated retail interest rate and the required reserve ratio are positive and significant at all maturities of interest rates but smaller at the long end of the yield curve. By contrast, market interest rates barely respond to the qualitative MPC announcements about the monetary policy stance. These findings are robust to alternative econometric methods that correct for potential bias arising from violations of the identification assumption. These findings suggest that the PBC should formulate its policy communication in a quantitative way.

We study how stock option grants are funded through share repurchases under conditions of option exercisability and moneyness. Using daily repurchase disclosures by U.K. firms, we corroborate our hypothesis that driven by flexibility, firms repurchase early in an option schedule while options are out-of-money and before becoming exercisable. Our findings show that when daily stock prices are below weighted average option exercise price and when options are not immediately exercisable, firms (a) increase daily repurchase volume (value), (b) increase repurchase frequency, and (c) have lower relative repurchase prices. We further evidence this by examining the change in treasury regulation that enabled firms to hold on to repurchased shares rather than cancelling them. Our findings show a strong support for option funding motives in the post-treasury regulation period when repurchase flexibility is greater.

This study examines the influence of regret aversion on asset pricing by proposing a regret-based capital asset pricing model in which individuals maximize the expected returns from chosen portfolios of assets while minimizing anticipated regrets. In equilibrium, a closed-form pricing formula is derived, whereby a risky asset's excess return is proportional to its “regret beta” that measures the exposure to investors’ emotions. The market as a whole pays investors a positive “regret premium” as compensation for regret aversion. As such, this study proposes a conceptual framework to understand the aggregate effects of regret. The model indicates that employing a regret-related beta can help explain cross-sectional returns. It also implies that regret aversion is a possible reason for the flat security market line and high equity premium.

The paper investigates firms’ willingness to match the currency composition of their assets and liabilities. Using detailed information at the loan contract level for the Hungarian non-financial corporate sector, the paper provides strong evidence to support the theory that currency matching plays a role in exporters’ debt currency choices. However, natural hedging is not the primary motive for firms to choose a foreign currency: it explains only 3.8 per cent of the overall new corporate foreign currency loans contracted by exporters and 2.9 per cent of the aggregate new foreign currency bank loans. Besides hedging, our results suggest that both carry trade and diversification strategies are relevant factors in firms’ currency-of-denomination decisions. Supply side factors are also found to be responsible for the prevalence of foreign currency loans among Hungarian corporate borrowers.

Are overconfident executives more likely to be promoted to CEOs? Using an option-based overconfidence measure, we show that firms with overconfident executives tend to hire internally. Further, when firms hire internally, they are more likely to pick a more confident candidate. The results suggest that governance and board inattention can play a role, with overconfident executives being more likely to become CEOs in firms with entrenched and busy boards, suggesting that such boards might confuse luck-with-skill following the confident executives’ tendencies towards greater risk-taking.

This paper investigates how competition affects the double-bottom-line performance of microfinance institutions (MFIs). While classical economic theory highlights that competition enhances efficiency and benefits both customers and firms, we argue that this is unlikely to apply to institutions operating in socially oriented industries, such as microfinance. Using a cross-country dataset of 4576 MFI-year observations (1139 unique MFIs) operating in 59 countries over a 10-year period (2005-2014), we find that competition has an adverse effect on MFIs’ economic sustainability and that competition undermines their breadth of outreach but enhances their depth of outreach. These results are robust to alternative specifications of competition and to the use of a two-stage least squares (2SLS) analysis to alleviate the endogeneity concern. The findings from our analysis have important implications when considering the regulation of MFI competition, especially in the light of the recent turmoil of MFI markets in some developing countries.

We introduce a statistical model for operational losses based on heavy-tailed distributions and bipartite graphs, which captures the event type and business line structure of operational risk data. The model explicitly takes into account the Pareto tails of losses and the heterogeneous dependence structures between them. We then derive estimators and provide estimation methods for individual as well as aggregated tail risk, measured in terms of Value-at-Risk and Conditional-Tail-Expectation for very high confidence levels, and introduce also an asymptotically full capital allocation method for portfolio risk. Having access to real-world operational risk losses from the Italian banking system, we apply our model to these data, and carry out risk estimation in terms of the previously derived quantities. Simulation studies further reveal first that even with a small number of observations, the proposed estimation methods produce estimates that converge to the true asymptotic values, and second, that quantifying dependence by means of the empirical network has a big impact on estimates at both individual and aggregate level, as well as for capital allocations.

We study a class of backtests for forecast distributions in which the test statistic depends on a spectral transformation that weights exceedance events by a function of the modeled probability level. The weighting scheme is specified by a kernel measure which makes explicit the user’s priorities for model performance. The class of spectral backtests includes tests of unconditional coverage and tests of conditional coverage. We show how the class embeds a wide variety of backtests in the existing literature, and further propose novel variants which are easily implemented, well-sized and have good power. In an empirical application, we backtest forecast distributions for the overnight P&L of ten bank trading portfolios. For some portfolios, test results depend materially on the choice of kernel.