Research article Special Issues

Market Value Volatility and the Volume of Traded Stock for U.S. Industrial Corporations

  • Received: 18 July 2017 Accepted: 27 November 2017 Published: 13 December 2017
  • A novel two-phase econometric approach was used to first obtain the variance (volatility) of the firm's market value adjusted for its common stock repurchases and other determinants (the traditional approach). Then, the variance of some 1,077 firms was used to predict the volume of the firm's common stock traded over a given period of time (the novel approach). The hypothesis was that fast traders in the stock market can use the variance of the firm's market value as a source of risk information, when deciding on what stock to purchase. An unbalanced panel of firms covering the quarterly time periods from 1999 (4) to 2017 (1) was analyzed by the longitudinal method to obtain the variances. Then, linear regression was used to relate the volume of stock traded to the variances. The novel method goes beyond the traditional volatility approach. The statistical results were acceptable for both phases, but with some concern over the use of the variance as an independent determinant in the second phase analysis.

    Citation: James P. Gander. Market Value Volatility and the Volume of Traded Stock for U.S. Industrial Corporations[J]. Quantitative Finance and Economics, 2017, 1(4): 403-410. doi: 10.3934/QFE.2017.4.403

    Related Papers:

  • A novel two-phase econometric approach was used to first obtain the variance (volatility) of the firm's market value adjusted for its common stock repurchases and other determinants (the traditional approach). Then, the variance of some 1,077 firms was used to predict the volume of the firm's common stock traded over a given period of time (the novel approach). The hypothesis was that fast traders in the stock market can use the variance of the firm's market value as a source of risk information, when deciding on what stock to purchase. An unbalanced panel of firms covering the quarterly time periods from 1999 (4) to 2017 (1) was analyzed by the longitudinal method to obtain the variances. Then, linear regression was used to relate the volume of stock traded to the variances. The novel method goes beyond the traditional volatility approach. The statistical results were acceptable for both phases, but with some concern over the use of the variance as an independent determinant in the second phase analysis.
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    © 2017 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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