Policy interventions may have increased measured systemic risk in the long or the short run due to costs associated with regulatory restrictions. In many situations, bonuses were prohibited, and dividends could be distributed only to the government. Additionally, the government occupied seats on some supervisory boards, especially in the case of recapitalizations. In addition, interventions could imply a series of commitments, such as divestments, acquisition bans, or price leadership bans, which can affect investors’ expectations about the future profitability of the bank.17 For the banks in our sample, the restrictions were imposed temporarily by the regulator until the intervention was unwound.
To explore the impact of regulatory restrictions on the relation between emergency rescue actions and systemic risk, we consider the following constraints: supervisory board intrusions, management pay limitations, and capital payout bans. Intrusions on supervisory boards may ensure stricter supervision of investment and lending practices, while caps on executive compensation are likely to reduce portfolio risk (Dam and Kotter 2012 ). Refraining from paying dividends or from buybacks could improve banks’ financial health, as retained earnings increase banks’ capacity to rebuild capital buffers and promote lending. On the other hand, as capital payout limits have strong implications for shareholders, it is likely that they incentivize managers to increase portfolio risk to repay the bailout faster so that the regulator removes the restriction. Acharya and Yorulmazer (2008 (link)) show that the government’s stake in the bank should be large enough to overcome risk-taking incentives.
As our MES measure captures both systemic risk realizations and forward-looking systemic risk, the effect of regulatory restrictions on the relation between interventions and systemic risk could be twofold. If shareholders perceive regulatory restrictions to be effective tools for moving portfolio risk toward optimal levels and assuring stricter monitoring of the bank, then the market valuation of more restricted rescued banks is likely to be higher than that of less restricted rescued banks, leading to lower measured systemic importance for the former. In turn, if investors perceive the regulatory burden to be costly, then stricter restrictions may lead to underperforming stock returns and therefore higher measured systemic risk for more restricted than for less restricted bailed-out banks. This translates into a higher systemic importance for bailed-out banks when regulators impose tighter restrictions.
The impact of restrictions on the link between policy interventions and systemic risk is examined with the following specification: SystemicRiskij,t=β0+β1×Policyinterventionsij,eventwindow+β2×Policyinterventionsij,eventwindow×Restrictionsij,t-1+β3×Restrictionsij,t-1+β4×IMRij,t-1+Φ×Bankcontrolsij,t-1+Ψ×Market&Macrocontrolsj,t-1+μjt+εij,t
In addition to Eq. (2), we include the interaction term of policy interventions with the restrictions imposed by the regulator. The latter are captured by dummy variables that reflect the following dimensions: supervisory board intrusions, management pay limitations, and capital payout bans. The coefficient β2 should be negative and significant if such restrictions reduce the systemic importance of banks when interventions are implemented and positive otherwise. As in the baseline specification, we use the same bank-level, market and macro controls. Additionally, we account for sample selection bias by including the inverse Mills ratio generated by the Probit model in Eq. (1). The strategy involves estimating the empirical models separately for each interaction of policy interventions with the restrictions using OLS FE for the restricted sample of rescued banks.
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