Knowledge that Transforms

We test for return co-movements among international commercial real estate markets. Our spatial econometric model estimates the market exposure to the performance of a reference portfolio. This benchmark portfolio contains all markets with a higher level of transparency, which reveals valuable information about the pricing mechanism. Empirical evidence suggests that these indirect effects transmit from more transparent to less transparent markets. We then study the predictive power of different familiarity-based channels to overcome entry barriers by predicting returns in less transparent property markets. The evaluation of the prediction performance indicates that observed price signals in highly transparent markets are attributed to less transparent markets, which we interpret as informational herding.

We examine whether media coverage affects credit rating change decisions by analyzing 732,426 newspaper items published by top U.S. media outlets on S&P 1500 firms. Our results show that negative media coverage has a strong association with credit rating change events, but positive media coverage does not. We find support for two channels that confirm this finding: the media's fundamental information content and the media's reputational pressure. Credit rating agencies appear to consider the fundamental information embedded in negative media coverage and recognize negative market sentiment when making rating change decisions.

Households with limited financial expertise sometimes attempt to avoid investment mistakes by delegating the management of their investments to experts. However, evidence on the efficacy of delegation has been mixed. This paper contributes to understanding the question: why is the acquired expertise of asset managers a limited substitute for investors’ lack of expertise? We consider an economy with investors (who vary in sophistication) and managers (who vary in skill). Unsophisticated investors’ lack of expertise makes it hard for them to distinguish skilled managers from unskilled ones. In the equilibrium that follows, investors exert little effort when searching for managers, leading to a suboptimal composition of managerial types entering the market. When unsophisticated investors are endowed with weak signals, they attempt to time their entry and exit from the market for managers, but their actions are predictable, so performance continues to suffer.

This paper investigates whether managers’ personal connections help corporate productivity to recover after a negative economic shock. Leveraging the heterogeneity in the severity of the financial crisis across different sectors, the paper reports that (i) the financial crisis had a negative effect on within-firm productivity, (ii) the effect was long-lasting and persistent, supporting a productivity-hysteresis hypothesis, and (iii) managers’ personal connections allowed corporations to recover from this productivity slowdown. Among the possible mechanisms, we show that connected managers operating in affected sectors foster productivity recovery through higher input cost efficiency and better access to the credit market, as well as more efficient use of labour and capital.

Taking advantage of granular data we measure the change in bank capital requirement resulting from the implementation of AI techniques to predict corporate defaults. For each of the largest banks operating in France we build by an algorithm pseudo-internal models of credit risk management for a range of methodologies extensively used in AI (random forest, gradient boosting, ridge regression, neural network). We compare these models to the traditional model usually in place that basically relies on a combination of logistic regression and expert judgement. The comparison is made along two sets of criterias capturing: the ability to pass compliance tests used by the regulators during on-site missions of model validation (i), and the induced changes in capital requirement (ii). The different models show noticeable differences in their ability to pass the regulatory tests and to lead to a reduction in capital requirement. While displaying a similar ability than the traditional model to pass compliance tests, neural networks provide the strongest incentive for banks to apply AI models for their internal model of credit risk of corporate businesses as they lead in some cases to sizeable reduction in capital requirement.

This paper aims to test the hypothesis that monetary policy changes affect bank deposits in an environment with high-interest rates. We perform a comprehensive analysis of bank statistics on deposits, credit operations, and bank accounting data between September 1999 and June 2018. We use Brazilian Central Bank statistics to run our tests. Our results show that Brazilian banks increase their spread on deposits in response to monetary policy actions, which reduces their funding due to an outflow of deposits. As a consequence, they grant fewer loans and increase credit restrictions. We also observe evidence that these effects vary with the degree of concentration on deposits observed locally and specifically in banks, and this phenomenon is more intense in regions with high concentration levels. Therefore, our results suggest a positive answer to the question posed in the title, that is, the deposit channel of monetary policy works in a high-interest rate environment, such as Brazil.

While momentum benefits from persistent trends of the market, such strategies are unable to distinguish between upside and downside risk and suffer consequently. We propose a Partial Moment Momentum (PMM) trading strategy that is sensitive to the sign of risk and show risk-adjusted outperformance compared to plain momentum and volatility-adjusted momentum strategies. The outperformance is robust across multiple time periods and in particular during market downturns. Further analysis based on conventional linear factor models shows negligible exposure to factor risk for our PMM portfolio. Finally, the performance of our proposed strategy appears to be enhanced when time series momentum is present and allows for improved risk management by distinguishing between upside and downside risks.

This paper examines whether forward-looking disclosure requirements impact firm business patterns. We rely on the implementation of the Comprehensive Capital Analysis Review (CCAR) stress test on U.S. bank holding companies as our identification strategy. Using a regression discontinuity design to exploit the quasi-experimental properties of the regulation around the different bank-size policy thresholds, we document four key findings. First, stress testing reduces moral hazard by decreasing the ratio of risk-weighted assets to total assets. Second, the decrease in moral hazard is not at the expense of bank lending since reducing risk results in higher concentrations in lending as banks shift out of higher-risk assets. Third, stress test banks’ lower risk is perceived by investors and results in lower funding costs relative to non-stress test banks. Fourth, the increase in regulatory oversight and stricter capital and transparency requirements do not cause large banks to manipulate their bank size to avoid complying with the stress test requirements.

Which factor model do investors in corporate bonds use? We examine this question by tracking investors’ decisions to invest in actively managed corporate bond mutual funds with a revealed preference approach. Our main result is that all bond factor models are dominated by the simple Sharpe ratio and Morningstar ratings. For all major corporate bond mutual fund styles, the Sharpe ratio explains fund flows better than alphas from bond factor models. Since the Sharpe ratio (and to some extent also Morningstar ratings) can be easily manipulated in bond markets, our findings have potentially severe implications for all market participants.

The cash conversion cycle (CCC) is important for fundamental analysis as an indicator of management effectiveness in cash and financing. However, there is a lack of empirical evidence for its implications on asset pricing except for the very recent findings that high CCCs negatively predict stock returns in the U.S. By investigating 47 developed and emerging markets from 1993 to 2018, we find a mild CCC effect across the globe. The Low-minus-High equal-weighted hedge portfolios sorted by components of CCC yield significant Fama-French five-factor alphas ranging from 0.277 to 0.730% per month. Our results are consistent with a mispricing explanation by analyzing earnings prediction, announcement returns around future earnings, and limits of arbitrage although there is also some evidence for a risk-based explanation. Moreover, the CCC effect is stronger in emerging markets than developed markets and for markets with more political risk and less integrated with the global market.