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Non‐linear Predictability of Value and Growth Stocks and Economic Activity

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  • Angela J. Black
  • David G. McMillan

Abstract

Recent empirical evidence suggests that stock market index returns are predictable from a variety of financial and macroeconomic variables. We extend this research by examining value and growth portfolios constructed by book‐to‐market ratio, and consider whether such predictability is evident here. Further, we assess whether such predictability is better characterised by a non‐linear form and whether such non‐linear predictability can be exploited to provide superior forecasts to those obtained from a linear model. General non‐linearities are examined using non‐parametric techniques, which suggest possible threshold behaviour. This leads to estimation of a smooth‐transition threshold model, with the results indicating an improved in‐sample performance and marginally superior out‐of‐sample forecast results.

Suggested Citation

  • Angela J. Black & David G. McMillan, 2004. "Non‐linear Predictability of Value and Growth Stocks and Economic Activity," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(3‐4), pages 439-474, April.
  • Handle: RePEc:bla:jbfnac:v:31:y:2004:i:3-4:p:439-474
    DOI: 10.1111/j.0306-686X.2004.00546.x
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    1. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    2. Delgado, Miguel A & Robinson, Peter M, 1992. "Nonparametric and Semiparametric Methods for Economic Research," Journal of Economic Surveys, Wiley Blackwell, vol. 6(3), pages 201-249.
    3. Qi, Min, 1999. "Nonlinear Predictability of Stock Returns Using Financial and Economic Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 419-429, October.
    4. K. S. Chan & H. Tong, 1986. "On Estimating Thresholds In Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(3), pages 179-190, May.
    5. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 119-136, Suppl. De.
    6. Martin Martens & Paul Kofman & Ton C. F. Vorst, 1998. "A threshold error-correction model for intraday futures and index returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(3), pages 245-263.
    7. Keim, Donald B. & Stambaugh, Robert F., 1986. "Predicting returns in the stock and bond markets," Journal of Financial Economics, Elsevier, vol. 17(2), pages 357-390, December.
    8. Shleifer, Andrei & Summers, Lawrence H, 1990. "The Noise Trader Approach to Finance," Journal of Economic Perspectives, American Economic Association, vol. 4(2), pages 19-33, Spring.
    9. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
    10. Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290.
    11. Alan Gregory & Richard D.F. Harris & Maria Michou, 2001. "An Analysis of Contrarian Investment Strategies in the UK," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 28(9‐10), pages 1192-1228, November.
    12. Pesaran, M Hashem & Timmermann, Allan, 2000. "A Recursive Modelling Approach to Predicting UK Stock Returns," Economic Journal, Royal Economic Society, vol. 110(460), pages 159-191, January.
    13. Daniel, Kent & Titman, Sheridan, 1997. "Evidence on the Characteristics of Cross Sectional Variation in Stock Returns," Journal of Finance, American Finance Association, vol. 52(1), pages 1-33, March.
    14. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    15. Pesaran, M Hashem & Timmermann, Allan, 1995. "Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
    16. He, Hua & Modest, David M, 1995. "Market Frictions and Consumption-Based Asset Pricing," Journal of Political Economy, University of Chicago Press, vol. 103(1), pages 94-117, February.
    17. Obstfeld, Maurice & Taylor, Alan M., 1997. "Nonlinear Aspects of Goods-Market Arbitrage and Adjustment: Heckscher's Commodity Points Revisited," Journal of the Japanese and International Economies, Elsevier, vol. 11(4), pages 441-479, December.
    18. Dumas, Bernard, 1992. "Dynamic Equilibrium and the Real Exchange Rate in a Spatially Separated World," Review of Financial Studies, Society for Financial Studies, vol. 5(2), pages 153-180.
    19. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    20. Bulkley, George & Harris, Richard D F, 1997. "Irrational Analysts' Expectations as a Cause of Excess Volatility in Stock Prices," Economic Journal, Royal Economic Society, vol. 107(441), pages 359-371, March.
    21. Cochrane, John H, 1991. "Production-Based Asset Pricing and the Link between Stock Returns and Economic Fluctuations," Journal of Finance, American Finance Association, vol. 46(1), pages 209-237, March.
    22. Campbell, John Y., 1987. "Stock returns and the term structure," Journal of Financial Economics, Elsevier, vol. 18(2), pages 373-399, June.
    23. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    24. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    25. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June.
    26. Fama, Eugene F & French, Kenneth R, 1996. "Multifactor Explanations of Asset Pricing Anomalies," Journal of Finance, American Finance Association, vol. 51(1), pages 55-84, March.
    27. Diamond, Douglas W. & Verrecchia, Robert E., 1981. "Information aggregation in a noisy rational expectations economy," Journal of Financial Economics, Elsevier, vol. 9(3), pages 221-235, September.
    28. Biais, Bruno & Hillion, Pierre, 1994. "Insider and Liquidity Trading in Stock and Options Markets," Review of Financial Studies, Society for Financial Studies, vol. 7(4), pages 743-780.
    29. Sercu, Piet & Uppal, Raman & Van Hulle, Cynthia, 1995. "The Exchange Rate in the Presence of Transaction Costs: Implications for Tests of Purchasing Power Parity," Journal of Finance, American Finance Association, vol. 50(4), pages 1309-1319, September.
    30. Chen, Nai-Fu & Roll, Richard & Ross, Stephen A, 1986. "Economic Forces and the Stock Market," The Journal of Business, University of Chicago Press, vol. 59(3), pages 383-403, July.
    31. Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
    32. Alan Gregory & Richard D.F. Harris & Maria Michou, 2001. "An Analysis of Contrarian Investment Strategies in the UK," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 28(9‐10), pages 1192-1228, November.
    33. Balvers, Ronald J & Cosimano, Thomas F & McDonald, Bill, 1990. "Predicting Stock Returns in an Efficient Market," Journal of Finance, American Finance Association, vol. 45(4), pages 1109-1128, September.
    34. Ferson, Wayne E & Harvey, Campbell R, 1993. "The Risk and Predictability of International Equity Returns," Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 527-566.
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