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EVA: The bubble years, meltdown and beyond

Author

Listed:
  • James Chong

    (Real Estate, & Insurance, California State University)

  • Drew Fountaine
  • Monica Her
  • Michael Phillips

Abstract

The objective of this study is to examine whether information, if any, is indeed embedded in economic value added (EVA) that would prove useful in creating wealth, and in minimising risk, for the investor during bull and bear market environments. Should this be so, then past EVAs should contain information that aids in the creation of stock portfolios with favourable future risk-return structure. EVA-based stock portfolios were found to be similar to the S&P500 Index, but yet produced positive alphas across sub-samples, an indication that EVA contains information beneficial to increasing shareholder wealth, even in bear markets. On closer examination of the EVA-based stock portfolios, it was suggested that in times of market upswings, one should construct a portfolio based on lower EVA-ranked stocks, while switching to higher EVA-ranked stocks during market downturns.

Suggested Citation

  • James Chong & Drew Fountaine & Monica Her & Michael Phillips, 2009. "EVA: The bubble years, meltdown and beyond," Journal of Asset Management, Palgrave Macmillan, vol. 10(3), pages 181-191, August.
  • Handle: RePEc:pal:assmgt:v:10:y:2009:i:3:d:10.1057_jam.2009.4
    DOI: 10.1057/jam.2009.4
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    References listed on IDEAS

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    1. Clare, A D & Thomas, S H & Wickens, M R, 1994. "Is the Gilt-Equity Yield Ratio Useful for Predicting UK Stock Returns?," Economic Journal, Royal Economic Society, vol. 104(423), pages 303-315, March.
    2. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    5. Biddle, Gary C. & Bowen, Robert M. & Wallace, James S., 1997. "Does EVA(R) beat earnings? Evidence on associations with stock returns and firm values," Journal of Accounting and Economics, Elsevier, vol. 24(3), pages 301-336, December.
    6. Javier Estrada, 2006. "Downside Risk in Practice," Journal of Applied Corporate Finance, Morgan Stanley, vol. 18(1), pages 117-125, March.
    7. Knez, Peter J & Ready, Mark J, 1996. "Estimating the Profits from Trading Strategies," The Review of Financial Studies, Society for Financial Studies, vol. 9(4), pages 1121-1163.
    8. Gary C. Biddle & Robert M. Bowen & James S. Wallace, 1999. "Evidence On Eva," Journal of Applied Corporate Finance, Morgan Stanley, vol. 12(2), pages 69-79, June.
    9. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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