These remarks discuss why the “cluster” of financial services and local banking markets are still relevant for antitrust analysis in banking. A key portion of the Federal Reserve’s Order approving the NationsBank–Barnett merger is interpreted, and the extent to which antitrust is a practical constraint on the development of a nationwide banking structure is commented upon.

We provide evidence regarding the extent to which commercial banking organizations that have entered investment banking have adopted pay-performance compensation systems that are like those used by investment banks. We find that pay-performance sensitivities for these banks once they begin securities underwriting are very similar to the sensitivities for commercial banks that have chosen not to enter investment banking. We also find that pay-performance sensitivities for both types of commercial banks are less than for investment banks.

We analyze the theoretical foundations of the efficient market hypothesis by stressing the efficient use of information and its effect upon price volatility. The “random walk” hypothesis assumes that price volatility is exogenous and unexplained. Randomness means that a knowledge of the past cannot help to predict the future. We accept the view that randomness appears because information is incomplete. The larger the subset of information available and known, the less emphasis one must place upon the generic term randomness. We construct a general and well accepted intertemporal price determination model, and show that price volatility reflects the output of a higher order dynamic system with an underlying stochastic foundation. Our analysis is used to explain the learning process and the efficient use of information in our archetype model. We estimate a general unrestricted system for financial and agricultural markets to see which specifications we can reject. What emerges is that a system very close to our archetype model is consistent with the evidence. We obtain an equation for price volatility which looks a lot like the GARCH equation. The price variability is a serially correlated variable which is affected by the Bayesian error, and the Bayesian error is a serially correlated variable which is affected by the noisiness of the system. In this manner we have explained some of the determinants of what has been called the “randomness” of price changes.

We examine the effect of discount rate changes on stock market returns, volatility, and trading volume using intraday data. Equity returns generally respond negatively and significantly to the unexpected announcements; however, the effect of expected changes on equity returns is insignificant. Furthermore, our results indicate that equity prices respond to announcements within the trading period/hour after the information release. An indication of a return reversal is too small to cover the full transaction costs. Unexpected discount rate changes also contribute to higher market volatility although the volatility is short-lived. Similarly, unexpected changes in discount rates induce larger trading volume while expected changes do not. Abnormal trading volume occurs only in period t. Our results also support the notion that unexpected changes in the discount rates impact market returns irrespective of the Federal Reserve operating procedures.

The enactment of property–casualty insurance guaranty fund statutes in the US was associated with a decrease in insurers' reserves for Homeowners and Commercial Multi-Peril insurance. The evidence is: loss ratios among states enacting guaranty fund statutes declined relative to other states. Tests on loss accruals confirm that the effect was due to decrease in reserves. Other tests that distinguish between guaranty fund statutes offer no evidence that guaranty funds encouraged sound insurers to monitor competitors and assist regulators in identifying weak insurers. Instead, the data are consistent with an explanation where insurance regulators identify and discourage risk-increasing activity.