DEVELOPING MULTIVARIATE MODELS TO PREDICT ABNORMAL STOCK RETURNS - Using Cross-sectional Differences to Identify Stocks with Above Average Return Expectations

Alwyn J. Hoffman

2010

Abstract

This paper describes the development of multivariate models used to identify stocks with above average return expectations. While most other research involving the development of stock return models involves time-series prediction of future returns, this paper focuses on the modelling of cross-sectional differences between stocks. The primary measure used in this paper to evaluate potential predictors of future stock returns is based on sorted category returns, an approach that was previously applied to NYSE listed stocks; in this paper the same approach is applied to stocks listed on the JSE. This measure is used to identify a number of fundamental and technical indicators that differentiates between high and low performing stock categories. Linear and non-linear multivariate models are subsequently developed, utilising these indicators to improve prediction performance. It is demonstrated that much of the useful stock return behaviour is present in the extremes of the population, that significant differences exist between different size categories, and that different aspects of stock behaviour is exposed using appropriate measures for portfolio returns. Portfolio performance results achieved using individual indicators as well as multivariate models are reported and compared with previously published results, and planned future work to improve on the results is discussed.

References

  1. Fama, E. F., French, K. R., August 2008. Dissecting Anomalies. In The Journal of Finance, LXIII(4).
  2. Altay, E., Satman, M. K., Stock market forecasting: artificial neural network and linear regression comparison in an emerging market, 2005. In Journal of Financial Management and Analysis, 18(2).
  3. Lorek, K. S. et al, 1983. Further descriptive and predictive evidence on alternative time-series models for quarterly earnings. In Journal of Accounting Research, 21(1).
  4. Bekiros, S. D., 2007. A neurofuzzy model for stock market trading. In Applied Economics Letters, Vol. 14.
  5. Jasic, T., Wood, D. 2004. The profitability of daily stock market indices trades based on neural network predictions. In Applied Financial Economics, 14.
  6. Blasco, N., Del Rio, C., Santamaria, R., The random walk hypothesis in the Spanish stock market: 1980-1992. June 1997. In Journal of Business Finance and Accounting, 24(5).
  7. Kluppelberg, C. et al, 2002. Testing for reduction to random walk in autoregressive conditional heteroskedasticity models. In Econometrics Journal, 5, pp. 387-416.
  8. Fama, E. F., French, K. R., 2004. The Capital Asset Pricing Model: Theory and Evidence. In Journal of Economic Perspectives, 18(3).
  9. Alcock, J., Gray, P., June 2005. Forecasting stock returns using model-selection criteria. In The Economic Record, 81(253).
  10. Huang, W. et al, 2007. Neural networks in finance and economic forecasting. In International Journal of Information Technology and Decision Making, 6(1).
  11. Sharpe, William F., 1964, Capital asset prices: A theory of market equilibrium under conditions of risk, Journal of Finance 19, 425-442.
Download


Paper Citation


in Harvard Style

J. Hoffman A. (2010). DEVELOPING MULTIVARIATE MODELS TO PREDICT ABNORMAL STOCK RETURNS - Using Cross-sectional Differences to Identify Stocks with Above Average Return Expectations . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 411-419. DOI: 10.5220/0003075704110419


in Bibtex Style

@conference{icnc10,
author={Alwyn J. Hoffman},
title={DEVELOPING MULTIVARIATE MODELS TO PREDICT ABNORMAL STOCK RETURNS - Using Cross-sectional Differences to Identify Stocks with Above Average Return Expectations },
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},
year={2010},
pages={411-419},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003075704110419},
isbn={978-989-8425-32-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
TI - DEVELOPING MULTIVARIATE MODELS TO PREDICT ABNORMAL STOCK RETURNS - Using Cross-sectional Differences to Identify Stocks with Above Average Return Expectations
SN - 978-989-8425-32-4
AU - J. Hoffman A.
PY - 2010
SP - 411
EP - 419
DO - 10.5220/0003075704110419