Skip to main content
SpringerLink
Log in

Equity style allocation: A nonparametric approach

Journal of Asset Management volume 17, pages 141–164 (2016)Cite this article

Abstract

In this article we provide a framework to assist with style allocation in Asian equity funds. We implement a nonparametric methodology to capture short-term stable time-varying relationships of otherwise long-term unstable relationships between numerous macroeconomic variables and style returns. We find that a nonparametric forecasting methodology produces positive performance after allowing for transaction costs, while the equivalent parametric forecasts are negative. The model can be implemented through tilting a funds style exposure to enhance performance. Even in the context of a long-only fund, the style exposures of the proposed model can be implemented as long–short exposures relative to a benchmark. Because the model is presented as a self financing market-neutral model, its implementation can be leveraged directly in a market-neutral fund or indirectly as (leveraged) style exposures in a long-only fund.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Figure 1
Figure 2
Figure 3
Figure 4

Notes

  1. Although we accept an alternative ‘bottom-up’ approach of using style indices characteristics (for example, the momentum or valuation of a style index) can be used to forecast style returns as perfectly valid, we consider it as beyond the scope of this article.

  2. iShares exist in MSCI US Size, Value, Momentum and Quality factors.

  3. Asness and Frazzini (2013) show that the classical FF3 HML factor is best thought of as an 80/20 combination of value and momentum.

  4. Bernanke and Boivin (2001) reconfirm the findings in Stock and Watson papers that large data sets can be utilized to improve forecast accuracy.

  5. Early papers describing nonparametric regression include Rosenblatt (1956), Parzen (1962), Watson (1964), Nadaraya (1964) and Sheather and Jones (1991).

  6. The Bloomberg codes for the variables are available upon request.

  7. This technique is also known as the latent root criterion, or the eigenvalue-one criterion.

  8. Turlach (1993) provides a summary of some of the possible choices.

  9. A description of some of these methods and their implementation in statistical packages is provided in Sheather (2004).

  10. Bloomberg categories {1, 16, 18, 2, 5, 19} and {6, 20, 3}.

  11. See Table 2 for the definition of nrd.

  12. The regression results are available upon request.

  13. Sometimes referred to as the mean square prediction error.

  14. A linear regression on a constant is equivalent to a 1-sample t-test.

  15. The results of these regressions are available upon request.

  16. The results from these regressions are available upon request.

  17. Available upon request.

  18. Tests related to OOS predictive models are described more fully in Clark and West (2007) and Clark and McCracken (2012).

References

  • Abdi, H. and Williams, L.J. (2010) Jackknife, Thousand Oaks, CA: Sage Publications, pp. 656–661.

    Google Scholar 

  • Abramson, I. (1982) On bandwidth variation in Kernal estimates – A square root law. The Annals of Statistics 10 (4): 1217–1223.

    Article  Google Scholar 

  • Ait-Sahalia, Y. and Brandt, M. (2001) Variable selection for portfolio choice. Journal of Finance 56 (4): 1297–1351.

    Article  Google Scholar 

  • Alessi, L., Barigozzi, M. and Capasso, M. (2008) A Robust Criterion for Determining the Number of Static Factors in Approximate Factor Models. ECB Working Paper Series, No. 903.

  • Amenc, N., Malaise, P., Martellini, L. and Sfeir, D. (2003) Tactical style allocation – A new form of market neutral strategy. Journal of Alternative Investments 6 (1): 8–22.

    Article  Google Scholar 

  • Ammann, M. and Verhofen, M. (2006) The effect of market regimes on style allocation. Financial Markets & Portfolio Management 20 (3): 309–337.

    Article  Google Scholar 

  • Ang, A. and Bekaert, G. (2002) International asset allocation with regime shifts. Review of Financial Studies 15 (4): 1137–1187.

    Article  Google Scholar 

  • Ang, A. and Bekaert, G. (2004) How regimes affect asset allocation. Financial Analysts Journal 68 (March/April): 86–99.

    Article  Google Scholar 

  • Archanapalli, B.G., Switzer, L.N. and Panju, K. (2007) Equity style timing: A multi-style rotation model for the Russell large-cap and small cap growth and value style indexes. Journal of Asset Management 8 (1): 9–23.

    Article  Google Scholar 

  • Asness, C. and Frazzini, A. (2013) The devil in HML’s details. Journal of Portfolio Management 39 (4): 49–68.

    Article  Google Scholar 

  • Bai, J. and Ng, S. (2002) Determining the number of factors in approximate factor models. Econometrica 70 (1): 191–221.

    Article  Google Scholar 

  • Beckers, S. and Blair, B. (2002) Non parametric forecasting for conditional asset allocation. Journal of Asset Management 3 (3): 213–228.

    Article  Google Scholar 

  • Bender, J., Briand, R., Melas, D., Subramanian, R.A. and Subramanian, M. (2013) Deploying multi fator index allocations in institutional portfolios. MSCI Research Insights.

  • Bernanke, B. and Boivin, J. (2001) Monetary Policy in a Data Rich Environment. NBER Working Paper Series 8379.

  • Bowman, A.W. (1984) An alternative method of cross-validation for the smoothing of kernel density estimates. Biometrika 71 (2): 353–360.

    Article  Google Scholar 

  • Bretscheger, L. and Lechthaler, F. (2012) Common Risk Factors and the Macroeconomy: New Evidence from the Japanese Stock Market. Centre of Economic Research, ETH Zurich, Working Paper 12/160.

  • Bundoo, S.K. (2008) An augmented Fama and French three-factor model: New evidence from an emerging stock market. Applied Economics Letters 15 (15): 1213–1218.

    Article  Google Scholar 

  • Cakici, N., Fabozzi, F. and Tan, S. (2013) Size, value and momentum in emerging market stock returns. Emerging Markets Review 16 (C): 46–65.

    Article  Google Scholar 

  • Campbell, J.Y. and Thompson, S.B. (2008) Predicting excess stock returns out of sample: Can anything beat the historical average? Review of Financial Studies 21 (4): 1509–1531.

    Article  Google Scholar 

  • Carhart, M.M. (1997) On persistence in mutual fund performance. Journal of Finance 52 (1): 57–82.

    Article  Google Scholar 

  • Chamberlain, G. and Rothschild, M. (1983) Arbitrage, factor structure and mean-variance analysis in large asset markets. Econometrica 51 (5): 1305–1324.

    Article  Google Scholar 

  • Clark, T.E. and McCracken, M.W. (2012) In-sample tests of predictive ability: A new approach. Journal of Econometrics 170 (1): 1–14.

    Article  Google Scholar 

  • Clark, T.E. and West, K.D. (2007) Approximately normal tests for equal predictive accuracy in nested models. Journal of Econometrics 138 (1): 291–311.

    Article  Google Scholar 

  • Connor, G. and Korajczyk, R. (1986) Performance measurement with the arbitrage pricing theory: A new framework for analysis. Journal of Financial Economics 15 (3): 373–394.

    Article  Google Scholar 

  • Demir, S. and Toktamis, O. (2010) On the adaptive Nadaraya-Watson Kernal regression estimators. Journal of Mathematics and Statistics 39 (3): 429–437.

    Google Scholar 

  • Djajadikerta, H. and Nartea, G. (2005) The Size and Book-to Market Effects and the Fama-French Three Factor Model in Small Markets: Preliminary Findings from New Zealand. Edith Cowan University Working Paper 0510.

  • Fabozzi, F., Focardi, S.M., Svetlozar, Rachev, T. and Arshanapalli, B.G. (2014) The Basics of Financial Econometrics: Tools, Concepts, and Asset Management Applications. Hoboken, NJ: John Wiley & Sons.

    Book  Google Scholar 

  • Faff, R.W. (2001) An examination of the Fama and French three-factor model using commercial available factors. Australian Journal of Management 26 (1): 1–17.

    Article  Google Scholar 

  • Faff, R. (2004) A simple test of the Fama and French model using daily data: Australian evidence. Applied Financial Economics 14: 83–92.

    Article  Google Scholar 

  • Fama, E.F. and French, K.R. (1992) The cross-section of expected stock returns. Journal of Finance 47 (2): 427–465.

    Article  Google Scholar 

  • Fama, E. and French, K.R. (2012) Size, value, and momentum in international stock returns. Journal of Financial Economics 105 (3): 457–472.

    Article  Google Scholar 

  • Faraway, J. and Jhun, M. (1990) Bootstrap choice of bandwidth for density estimation. Journal of the American Statistical Association 85 (412): 1110–1122.

    Article  Google Scholar 

  • Gaunt, C. (2004) Size and book-to-market effects and the Fama French three factor asset pricing model: Evidence from the Australian stock market. Accounting and Finance 44 (1): 27–44.

    Article  Google Scholar 

  • Griffin, J. (2002) Are the Fama and French factors global or country specific? The Review of Financial Studies 15 (3): 783–803.

    Article  Google Scholar 

  • Guirguis, H., Suen, M. and Theodore, T. (2012) How to avoid the value trap: Regime switching value investing, timing the value style index in a Markov regimes-switching model. Journal of Investment Management 10 (1): 52–64.

    Google Scholar 

  • Hallin, M. and Liška, R. (2007) Determining the number of factors in the general dynamic factor model. Journal of the American Statistical Association 102 (478): 603–617.

    Article  Google Scholar 

  • Homsud, N., Wasunsakul, J., Phuangnark, S. and Joongpong, J. (2009) A study of Fama French three factors model and capital asset pricing model in the stock exchange of Thailand. International Research Journal of Finance and Economics 25 (March): 31–40.

    Google Scholar 

  • Jegadeesh, N. and Titman, S. (1993) Return to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance 48 (1): 65–91.

    Article  Google Scholar 

  • Kaiser, H. (1960) The application of electronic computers to factor analysis. Educational and Psychological Measurement 20 (1): 141–151.

    Article  Google Scholar 

  • Kao, D. and Shumaker, R. (1999) Equity style timing. Financial Analysts Journal 55 (1): 37–48.

    Article  Google Scholar 

  • Keim, D.B. and Stambaugh, R.F. (1986) Predicting returns in the stock and bond markets. Journal of Financial Economics 17 (2): 357–390.

    Article  Google Scholar 

  • Liu, P., Xu, K. and Zhao, Y. (2010) Market regimes, sectorial investments and time varying risk premiums. International Journal of Managerial Finance 7 (2): 107–133.

    Article  Google Scholar 

  • Maio, P. and Philip, D. (2012) Do Macro Variables Drive Stock and Bond Returns. Hanken School of Economics, Working Paper.

  • Nadaraya, E.A. (1964) On estimating regression. Theory of Probability and its Applications 9 (1): 141–142.

    Article  Google Scholar 

  • Narsky, I. and Porter, F.C. (2013) Statistical Analysis Techniques in Particle Physics. Hoboken, NJ: John Wiley & Sons.

    Book  Google Scholar 

  • Onatski, A. (2010) Determining the number of factors from empirical distribution of eigenvalues. Review of Economics and Statistics 92 (4): 1004–1016.

    Article  Google Scholar 

  • Parzen, E. (1962) On estimation of a probability density function and mode. Annals of Mathematical Statistics 33 (3): 1065–1076.

    Article  Google Scholar 

  • Quenouille, M.H. (1956) Notes on bias in estimation. Biometrika 43 (3/4): 353–360.

    Article  Google Scholar 

  • Rosenblatt, M. (1956) Remarks on some nonparametric estimates of a density function. Annals of Mathematical Statistics 27 (3): 832–837.

    Article  Google Scholar 

  • Rudemo, M. (1982) Empirical choice of histograms and kernel density estimators. Scandinavian Journal of Statistics 9 (2): 65–78.

    Google Scholar 

  • Sain, S.R. (2002) Multivariate locally adaptive density estimation. Computational Statistics & Data Analysis 39 (2): 165–186.

    Article  Google Scholar 

  • Scott, D.W. and Terrell, G.R. (1987) Biased and unbiased cross-validation in density estimation. Journal of the American Statistical Association 82 (414): 1131–1146.

    Article  Google Scholar 

  • Sheather, S.J. (2004) Density estimation – Statistical science. Institute of Mathematical Sciences 19 (4): 588–597.

    Google Scholar 

  • Sheather, S.J. and Jones, M.C. (1991) A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society, Series B 53 (3): 683–690.

    Google Scholar 

  • Silverman, B.W. (1986) Density Estimation. London: Chapman and Hall.

    Book  Google Scholar 

  • Stock, J.H. and Watson, M.W. (2002) Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association 97 (460): 1167–1179.

    Article  Google Scholar 

  • Tu, J. (2010) Is regime switching in stock returns important in portfolio decisions. Management Science 56 (7): 1198–1215.

    Article  Google Scholar 

  • Tukey, J.W. (1958) Bias and confidence in not quite large samples. Annals of Mathematical Statistics 29 (2): 614.

    Article  Google Scholar 

  • Turlach, B. (1993) Bandwidth selection in kernal density estimation: A review. Institut für Statistik und Ökonometrie, Humboldt-Universität zu Berlin, Discussion Paper 9307.

  • Walid, E.M. and Ahlem, E.M. (2009) New Evidence on the Applicability of Fama and French Three – Factor Model to the Japanese Stock Market. Osaka University, Working Paper.

  • Watson, G.S. (1964) Smooth regression analysis. Sankhyā Ser. A 26 (15): 175–184.

    Google Scholar 

  • Zambom, A.Z. and Dias, R. (2012) A Review of Kernel Density Estimation with Applications to Econometrics. Universidade Estadual de Campinas, Working Paper.

Download references

Author information

Authors and Affiliations

  1. BR7 5XS, London, UK

    Mohan Subbiah

Authors
  1. Mohan Subbiah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank J Fabozzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohan Subbiah.

Additional information

Supplementary information accompanies this article on the Journal of Asset Management website (www.palgrave-journals.com/jam)

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Subbiah, M., Fabozzi, F. Equity style allocation: A nonparametric approach. J Asset Manag 17, 141–164 (2016). https://doi.org/10.1057/jam.2016.1

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jam.2016.1

Keywords

search

Navigation