Preaching Water But Drinking Wine? Relative Performance Evaluation in International Banking

Abstract

Relative performance evaluation (RPE) is, at least on paper, enjoying widespread popularity in determining the level of executive compensation. Yet existing empirical evidence of RPE is decidedly mixed. Two principal explanations are held responsible for this discord. A constructional challenge arises from intricacies of identifying the correct peers. And on a simpler note, corporate commitments to RPE could be mere exercises in empty rhetoric. We address both issues and test the use of RPE in a new sample of large international non-U.S. banks. Taken as a whole, the banks in our sample show moderate evidence consistent with RPE. We report stronger evidence once we investigate the subsample of banks that disclose the use of peers in their compensation schemes. This finding lends support to the credibility and thus informational value of RPE commitments. Digging deeper, we conclude that RPE usage is driven by firm size and growth options.

Strategic Liquidity Mismatch and Financial Sector Stability

“…Additionally, the estimate in Column (2) of Table 9 shows that such mimicking behavior is relatively stronger among larger banks. This is consistent with large banks taking more risk than small banks in equilibrium since they internalize that their decisions directly affect the government's optimal bailout policy (Dávila and Walther, 2018), but also with risk-taking being driven by the presence of RPE in compensation schemes that tends to be more prevalent in larger banks (Ilic, Pisarov, and Schmidt, 2017;Albuquerque, Cabral, and Guedes, 2019). 22 This finding is particularly important given that the trade-off between liquidity creation and fragility is most consequential for large banks that create the most liquidity (Berger and Bouwman, 2009).…”

“…This type of behavior has been shown empirically by Leary and Roberts (2014) for non-financial listed firms in the United States. Finally, the probability of RPE adoption also increases with bank size (Ilic, Pisarov, and Schmidt, 2017;Albuquerque, Cabral, and Guedes, 2019). 10…”

Market Risk Early Warning Based on Deep Learning and Fruit Fly Optimization

“…where b * is the b value obtained from formula (2) and K(x i , x i ) is the kernel function. Take the radial basis function (RBF) as an example, which can be expressed as [19] K(x, y)…”