Skip to main content
SpringerLink
Log in

Combining Fundamental Measures for Stock Selection

  • Chapter
Handbook of Quantitative Finance and Risk Management

Abstract

Securities selection is the attempt to distinguish prospective winners from losers – conditional on beliefs and available information. This article surveys some relevant academic research on the subject, including work about the combining of forecasts (Operational Research Quarterly 20, 451–468, 1969), the Black-Litterman model (Journal of Fixed Income 1(2), 7–18, 1991; Financial Analysts Journal (September/October) 28–43, 1992), the combining of Bayesian priors and regression estimates (Journal of Finance 55(1), 179–223, 2000), model uncertainty and Bayesian model averaging (Statistical Science 14(4), 382–417, 1999; Review of Financial Studies 15(4), 1223–1249, 2002), the theory of competitive storage (Review of Economic Studies 59, 1–23, 1992), and the combination of valuation estimates (Review of Accounting Studies 12(2–3), 227–256, 2007). Despite its wide-ranging applicability, the Bayesian approach is not a license for data snooping. The second half of this article describes common pitfalls in fundamental analysis and comments on the role of theoretical guidance in mitigating these pitfalls.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 869.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 1,099.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The author’s equity-valuation students enrolled in an MBA elective at a leading business school choose to take weighted averages of their valuation estimates even though nothing in the class materials advises them to combine estimates. When asked why they do this, students report they learned to do this at work, other classes, or that weighing makes common sense. The students do not articulate how they determine their weights, and the weights vary greatly between students.

  2. 2.

    Net asset value is the liquidation value of assets minus the fair or settlement value of liabilities. Net asset value is not the same as accounting “book” value. Book value, because it derives from historical cost accounting, arcane accounting depreciation formulas, and accounting rules that forbid the recognition of valuable intangible assets, is known to be an unreliable (typically conservatively biased) estimate of net asset value.

  3. 3.

    The Internal Revenue Code addresses the valuation of closely held securities in Section 2031(b). The standard of value is “fair market value,” the price at which willing buyers or sellers with reasonable knowledge of the facts are willing to transact. Revenue Ruling 59–60 (1959–1 C.B. 237) sets forth the IRS’s interpretation of IRC Section 2031(b).

  4. 4.

    For example, a large block trade of micro-cap shares may cause temporary price volatility. Non-synchronous trading may cause apparent excessive price stability, as seen in emerging equity markets.

  5. 5.

    Y ∼ Nm, τ2 means Y is normally distributed with mean m and standard deviation τ. Note that we leave the possibility that the standard deviations may be heteroskedatic – nothing in our model requires them to be independent of V.

  6. 6.

    Accounting treatments like mark-to-market accounting or cookie-jar accounting may correlate accounting numbers to market noise. If so, then DCF estimates that rely on accounting numbers to make their cash flow projections may correlate to market noise, so that corr(e I , e)≠0. For this reason, the Appendix provides the generalization of Theorem 11.1 to the case when corr(e I , e) = ρ I ≠0.

  7. 7.

    Bell vs. Kirby Lumber Corp., Del. Supr., 413 A.2d 137 (1980).

  8. 8.

    For comparison, a random number homogeneously distributed between 0 and 1 has average value 50% and standard deviation 28.9%. Since the market value and earnings value weights have significantly smaller spread and their means differ from 50%, it does not appear these weights are homogeneously distributed random variables.

  9. 9.

    Most of my 15 series, including gold, began well after 1901. Only the cotton, corn, and wheat series started in January 1901.

  10. 10.

    While it is unrealistic to believe that investors can trade these commodities at the quoted historical cash prices, a parallel exercise (not reported here) using commodity futures prices would yield similar results.

  11. 11.

    Unfortunately, the theory of storage is still incomplete. As Deaton and Laroque (2003, p. 1) explain, the model, “although capable of introducing some autocorrelation into an otherwise i.i.d. process, appears to be incapable of generating the high degree of serial correlation of most commodity prices. … We are therefore left without a coherent explanation for the high degree of autocorrelation in commodity prices …”

  12. 12.

    Yee (2008b) deconstructs the round-trip process of fundamental analysis, stock selection, and profit taking. Klarman (1991) presents a hearty practitioner’s account.

  13. 13.

    Yoo (2006) empirically examined, in a large-scale study of Compustat firms, whether taking a linear combination of several univariate method of comparables estimates would achieve more precise valuation estimates than any comparables estimate alone. He concluded that the forward price-earnings ratio essentially beats the trailing ratios, or combination of trailing ratios, so much that combining it with other benchmark estimates does not help. Yoo did not consider DCF, liquidation value, or market price in his study.

References

  • Altman, E. I. 1968. “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy.” Journal of Finance 23, 589–699.

    Article  Google Scholar 

  • Ang, A. and J. Liu. 2004. “How to discount cashflows with time-varying expected returns.” Journal of Finance 59(6), 2745–2783.

    Article  Google Scholar 

  • Armstrong, J. 1989. “Combining forecasts: the end of the beginning or the beginning of the end?” International Journal of Forecasting 5(4), 585–588.

    Article  Google Scholar 

  • Arnott, R., J. Hsu, and P. Moore. 2005. “Fundamental indexation.” Financial Analysts Journal 61(2), 83–99.

    Article  Google Scholar 

  • Avramov, D. 2000. “Stock return predictability and model uncertainty.” Journal of Financial Economics 64, 423–458.

    Article  Google Scholar 

  • Bates, J. and C. Granger. 1969. “The combination of forecasts.” Operational Research Quarterly 20, 451–468.

    Article  Google Scholar 

  • Beatty, R., S. Riffe, and R. Thompson. 1999. “The method of comparables and tax court valuations of private firms: an empirical investigation.” Accounting Horizons 13(3), 177–199.

    Article  Google Scholar 

  • Beneish, M., C. Lee, and R. Tarpley. 2001. “Contextual fundamental analysis through the prediction of extreme returns.” Review of Accounting Studies 6(2/3), 165–189.

    Article  Google Scholar 

  • Bhojraj, S. and C. Lee. 2003. “Who is my peer? A valuation-based approach to the selection of comparable firms.” Journal of Accounting Research, 41(5), 745–774.

    Article  Google Scholar 

  • Black, F. 1986. “Noise.” Journal of Finance, vol. 16(3), 529–542.

    Article  Google Scholar 

  • Black, F. and R. Litterman. 1991. “Asset allocation: combining investor views with market equilibrium.” Journal of Fixed Income 1(2), 7–18.

    Article  Google Scholar 

  • Black, F. and R. Litterman. 1992. “Global portfolio optimization.” Financial Analysts Journal (September/October) 28–43.

    Google Scholar 

  • Chan, L. and J. Lakonishok. 2004. “Value and growth investing: review and update.” Financial Analysts Journal 60(1), 71–87.

    Article  Google Scholar 

  • Chen, F., K. Yee, and Y. Yoo. 2007. “Did adoption of forward-looking methods improve valuation accuracy in shareholder litigation?” Journal of Accounting, Auditing, and Finance 22(4), 573–598.

    Google Scholar 

  • Chen, F., K. Yee, and Y. Yoo. 2010. “Robustness of judicial decisions to valuation-method innovation: an exploratory empirical study.” Journal of Business, Accounting, and Finance, forthcoming.

    Google Scholar 

  • Clemen, R. 1989. “Combining forecasts: a review and annotated bibliography.” International Journal of Forecasting 5, 559–583.

    Article  Google Scholar 

  • Clemen, R. and R. Winkler. 1986. “Combining economic forecasts.” Journal of Economic and Business Statistics 4, 39–46.

    Google Scholar 

  • Coulson, N. and R. Robins. 1993. “Forecast combination in a dynamic setting.” Journal of Forecasting 12, 63–67.

    Article  Google Scholar 

  • Crack, T. 1999. “A classic case of “data snooping” for classroom discussion.” Journal of Financial Education 92–97.

    Google Scholar 

  • Cremers, K. 2002. “Stock return predictability: a Bayesian model selection perspective.” Review of Financial Studies 15(4), 1223–1249.

    Article  Google Scholar 

  • Damodaran, A. 2002. Investment valuation: tools and techniques for determining the value of any asset, 2nd ed., Wiley, NY.

    Google Scholar 

  • Daniel, K., D. Hirshleifer, and A. Subrahmanyam. 2001. “Overconfidence, Arbitrage, and Equilibrium Asset Pricing.” Journal of Finance 56(3), 921–965.

    Article  Google Scholar 

  • Deaton, A. and G. Laroque. 1992. “On the behavior of commodity prices.” Review of Economic Studies 59, 1–23.

    Article  Google Scholar 

  • Deaton, A. and G. Laroque. 2003. “A model of commodity prices after Sir Arthur Lewis.” Journal of Development Economics 71, 289–310.

    Article  Google Scholar 

  • Diebold, F. 1988. “Serial correlation and the combination of forecasts.” Journal of Business and Economic Statistics 6, 105–111.

    Google Scholar 

  • Diebold, F. and J. Lopez. 1996. “Forecast evaluation and combination,” in Handbook of statistics, G. S. Maddala and C. R. Rao (Eds.). North Holland, Amsterdam.

    Google Scholar 

  • Diebold, F. and P. Pauly. 1987. “Structural change and the combination of forecasts.” Journal of Forecasting 6, 21–40.

    Article  Google Scholar 

  • Fama, E. and K. French. 1992. “The cross-section of expected stock returns.” Journal of Finance 47, 427–465.

    Article  Google Scholar 

  • Feltham, G. and J. Ohlson. 1999. “Residual earnings valuation with risk and stochastic interest rates.” Accounting Review 74(2), 165–183.

    Article  Google Scholar 

  • Fung, W. and D. Hsieh. 2001. “The risk in hedge fund strategies: theory and evidence from trend followers.” Review of Financial Studies 14, 313–341.

    Article  Google Scholar 

  • Gilson, S., E. Hotchkiss, and R. Ruback. 2000. “Valuation of bankrupt firms.” Review of Financial Studies 13(1), 43–74.

    Article  Google Scholar 

  • Gorton, G., F. Hayashi, and K. Rouwenhorst. 2007. The fundamentals of commodity futures, Yale working paper.

    Google Scholar 

  • Granger, C. and P. Newbold. 1973. “Some comments on the evaluation of economic forecasts.” Applied Economics 5, 35–47.

    Article  Google Scholar 

  • Granger, C. and R. Ramanathan. 1984. “Improved methods of forecasting.” Journal of Forecasting 3, 197–204.

    Article  Google Scholar 

  • Grinold, R. 1989. “The fundamental law of active management.” Journal of Portfolio Management 15(3), 30–37.

    Article  Google Scholar 

  • He, G. and R. Litterman. 1999. The Intuition Behind Black-Litterman Model Portfolios. Investment Management Division, Goldman Sachs.

    Google Scholar 

  • Hoeting, J., D. Madigan, A. Raftery, and C. Volinsky. 1999. “Bayesian model averaging: a tutorial.” Statistical Science 14(4), 382–417.

    Article  Google Scholar 

  • Kahn, J., S. Landsburg, and A. Stockman. 1996. “The positive economics of methodology.” Journal of Economic Theory 68(1), 64–76.

    Article  Google Scholar 

  • Kaldor, N. 1939. “Speculation and economic stability.” Review of Economic Studies 7, 1–27.

    Article  Google Scholar 

  • Kaplan, S. and R. Ruback. 1995. “The valuation of cash flow forecasts: an empirical analysis.” Journal of Finance 50(4), 1059–1093.

    Article  Google Scholar 

  • Klarman, S. 1991. Margin of safety: risk-averse value investing strategies for the thoughtful investor, HarperBusiness, New York, NY.

    Google Scholar 

  • Klein, R. and V. Bawa. 1976. “The effect of estimation risk on optimal portfolio choice.” Journal of Financial Economics 3, 215–231.

    Article  Google Scholar 

  • Lewellen, J. and J. Shanken. 2002. “Learning, asset-pricing tests, and market efficiency.” Journal of Finance, 57(3), 1113–1145.

    Article  Google Scholar 

  • Lie, E. and H. Lie. 2002. “Multiples used to estimate corporate value.” Financial Analysts Journal 58(2), 44–53.

    Article  Google Scholar 

  • Litzenberger, R. and N. Rabinowitz. 1995. “Backwardation in oil futures markets: theory and empirical evidence.” Journal of Finance 50(5), 1517–1545.

    Article  Google Scholar 

  • Liu, D., D. Nissim, and J. Thomas. 2001. “Equity valuation using multiples.” Journal of Accounting Research 40, 135–172.

    Article  Google Scholar 

  • Lo, A. 2007. Where do alphas come from? A new measure of the value of active investment management, Working paper, Sloan School of Business.

    Google Scholar 

  • Lo, A. and A. MacKinlay. 1990. “Data snooping biases in tests of financial asset pricing models.” Review of Financial Studies 3, 431–468.

    Article  Google Scholar 

  • Ohlson, J. 1980. “Financial ratios and the probabilistic prediction of bankruptcy.” Journal of Accounting Research 18(1), 109–131.

    Article  Google Scholar 

  • Ohlson, J. and Z. Gao. 2006. “Earnings, earnings growth and value.” Foundations and Trends in Accounting 1(1), 1–70.

    Article  Google Scholar 

  • Pastor, L. 2000. “Portfolio selection and asset pricing models.” Journal of Finance 55(1), 179–223.

    Article  Google Scholar 

  • Pastor, L. and R. Stambaugh. 1999. “Cost of equity capital and model mispricing.” Journal of Finance 54(1), 67–121.

    Article  Google Scholar 

  • Penman, S. 2001. Financial statement analysis and security valuation. McGraw-Hill, Boston.

    Google Scholar 

  • Perold, A. 2007. “Fundamentally flawed indexing.” Financial Analysts Journal 63(6), 31–37.

    Article  Google Scholar 

  • Piotroski, J. 2000. “Value investing: the use of historical financial statement information to separate winners from losers.” Journal of Accounting Research 38 (Supplement), 1–41.

    Google Scholar 

  • Seligman, J. 1984. “Reappraising the appraisal remedy.” George Washington Law Review 52, 829.

    Google Scholar 

  • Sharfman, K. 2003. “Valuation averaging: a new procedure for resolving valuation disputes.” Minnesota Law Review 88(2), 357–383.

    Google Scholar 

  • Siegel, J. 2006. “The noisy market hypothesis.” Wall Street Journal June 14, 2006, A14.

    Google Scholar 

  • Sullivan, R., A. Timmermann, and H. White. 1999. “Data-snooping, technical trading rule performance, and the bootstrap.” Journal of Finance 54(5), 1647–1691.

    Article  Google Scholar 

  • Treynor, J. and F. Black. 1973. “How to use security analysis to improve portfolio selection.” Journal of Business 46, 66–86.

    Article  Google Scholar 

  • White, H. 2000. “Reality check for data snooping.” Econometrica 68(5), 1097–1126.

    Article  Google Scholar 

  • Yee, K. 2002. “Judicial valuation and the rise of DCF.” Public Fund Digest 76, 76–84.

    Google Scholar 

  • Yee, K. 2004a. “Perspectives: combining value estimates to increase accuracy.” Financial Analysts Journal 60(4), 23–28.

    Article  Google Scholar 

  • Yee, K. 2004b. “Forward versus trailing earnings in equity valuation.” Review of Accounting Studies 9(2–3), 301–329.

    Article  Google Scholar 

  • Yee, K. 2005a. “Aggregation, dividend irrelevancy, and earnings-value relations.” Contemporary Accounting Research 22(2), 453–480.

    Article  Google Scholar 

  • Yee, K. 2005b. “Control premiums, minority discounts, and optimal judicial valuation.” Journal of Law and Economics 48(2), 517–548.

    Article  Google Scholar 

  • Yee, K. 2007. “Using accounting information for consumption planning and equity valuation.” Review of Accounting Studies 12(2–3), 227–256.

    Article  Google Scholar 

  • Yee, K. 2008a. “A Bayesian framework for combining value estimates.” Review of Quantitative Finance and Accounting 30(3), 339–354.

    Article  Google Scholar 

  • Yee, K. 2008b. “Deep-value investing, fundamental risks, and the margin of safety.” Journal of Investing, 17(3), 35–46.

    Article  Google Scholar 

  • Yee, K. 2008c. “Dueling experts and imperfect verification.” International Review of Law and Economics 28(4), 246–255.

    Article  Google Scholar 

  • Yoo, Y. 2006. “The valuation accuracy of equity valuation using a combination of multiples.” Review of Accounting and Finance 5(2), 108–123.

    Article  Google Scholar 

  • Zellner, A. and V. Chetty. 1965. “Prediction and decision problems in regression models from the Bayesian point of view.” Journal of American Statistical Association 60, 608–616.

    Article  Google Scholar 

Download references

Acknowledgments

The statements and opinions expressed in this article are those of the author as of the date of the article and do not necessarily represent the views of the Bank of New York Mellon, Mellon Capital Management, or any of their affiliates. This article does not offer investment advice.

Author information

Authors and Affiliations

  1. Columbia Business School, New York, NY, USA

    Kenton K. Yee

Authors
  1. Kenton K. Yee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kenton K. Yee .

Editor information

Editors and Affiliations

  1. Department of Finance and Economics, Rutgers University, 94 Rockafeller Road, Janice H. Levin Bldg., New Brunswick, NJ, 08854-8054, USA

    Cheng-Few Lee

  2. State Street Corp., Boston, MA, 94132, USA

    Alice C. Lee

  3. Center for PBBEF Research, North Brunswick, NJ, USA

    John Lee

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Yee, K.K. (2010). Combining Fundamental Measures for Stock Selection. In: Lee, CF., Lee, A.C., Lee, J. (eds) Handbook of Quantitative Finance and Risk Management. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-77117-5_11

Download citation

Publish with us

Policies and ethics

Search

Navigation