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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. Shih, Kuang Hsun & Cheng, Ching Chan & Wang, Yi Hsien, 2011. "Financial Information Fraud Risk Warning for Manufacturing Industry - Using Logistic Regression and Neural Network," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 54-71, March.
    2. Francesco Chincoli & Massimo Guidolin, 2017. "Linear and nonlinear predictability in investment style factors: multivariate evidence," Journal of Asset Management, Palgrave Macmillan, vol. 18(6), pages 476-509, October.
    3. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.

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