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Intermediary Asset Pricing during the National Banking Era

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  • Colin Weiss

Abstract

Financial intermediary balance sheets matter for asset returns even when these intermediaries do not directly participate in the relevant asset markets. During the National Banking Era, liquidity conditions for the New York Clearinghouse (NYCH) banks forecast excess returns for stocks, bonds, and currencies. The NYCH banks had little to no direct participation in these markets; their main link to these markets was through securities financing. Liquidity conditions affect asset prices through the credit growth of the NYCH banks, which shapes marginal investors' discount rates. I use institutional features of this era to provide evidence in favor of this mechanism.

Suggested Citation

  • Colin Weiss, 2020. "Intermediary Asset Pricing during the National Banking Era," International Finance Discussion Papers 1302, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgif:1302
    DOI: 10.17016/IFDP.2020.1302
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    References listed on IDEAS

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    1. Scott L. Fulford & Felipe Schwartzman, 2020. "The Benefits of Commitment to a Currency Peg: Aggregate Lessons from the Regional Effects of the 1896 U.S. Presidential Election," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 600-616, July.
    2. Paul Hallwood, C. & MacDonald, Ronald & Marsh, Ian W., 2000. "Realignment expectations and the US dollar, 1890-1897: Was there a 'Peso problem'?," Journal of Monetary Economics, Elsevier, vol. 46(3), pages 605-620, December.
    3. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    4. Balke, Nathan S & Gordon, Robert J, 1989. "The Estimation of Prewar Gross National Product: Methodology and New Evidence," Journal of Political Economy, University of Chicago Press, vol. 97(1), pages 38-92, February.
    5. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    6. Colin Weiss, 2020. "Contractionary Devaluation Risk: Evidence from the Free Silver Movement, 1878-1900," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 705-720, October.
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    More about this item

    Keywords

    Liquidity management; Margin loans; Intermediary asset pricing; National banks;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • N21 - Economic History - - Financial Markets and Institutions - - - U.S.; Canada: Pre-1913

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