Universal Banking and the Pricing of Securities Risk: Historical Evidence from Germany
This paper investigates the importance of financial institutions, particularly universal banks, in the pricing of risk in securities markets. Recent research on modern economies, finds that three factors explain the cross-section of average stock returns: (i) a stock's sensitivity to market-wide price movements (``beta''), (ii) market capitalization, and (iii) book value of equity relative to its market price (the value effect). The German financial system of the pre-World War I period is a particularly informative case, since it enjoyed both a large and active stock market as well as powerful, relationship-oriented universal banks. In such a system, banks are often seen as particularly important for resolving information problems and may therefore improve risk sharing. If so, the three-factor model may fail to explain securities pricing. Banks (as opposed to finance companies and mutual funds) have diminished in importance in American corporate finance. One goal of this study, therefore, is to determine whether the decline of banks is irrelevant or is detrimental to social welfare. The future usefulness of banks hinges on their ability to contribute something that competitive markets cannot or do not. The theoretical literature offers conflicting hypotheses about this issue, making the question ultimately an empirical one. The study uses standard methodology in order to facilitate cross-reference to existing empirical literature. The methodology can be criticized on various asset-pricing theoretic grounds (most imortantly, it is not robust to deleting conditioning information). Nonetheless, such analyses have led to the discovery of several robust statistical regularities. While the approach clearly is not a formal test of a particular asset pricing model (the CAPM or APT), such models typically fail when confronted with the data. By using various sampling techniques, the analysis also estimates the importance of survivorship bias in the estimation of pricing models.
|Date of creation:||01 Aug 2000|
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