Advertisement

Financial Markets and Portfolio Management

, Volume 33, Issue 4, pp 417–445 | Cite as

Incorporating financial market volatility to improve forecasts of directional changes in Australian share market returns

  • Riza Erdugan
  • Nada Kulendran
  • Riccardo NatoliEmail author
Article

Abstract

This study examines whether incorporating volatility improves the forecast of directional changes in the returns of Australia’s banking, industrial and resource sectors. This study first estimates a benchmark non-volatility logit regression model and assesses it against four estimated volatility logit models measured by mean absolute deviation, standard deviation, return squared (U2) and range. An out-of-sample prediction performance, assessed by Brier’s QPS statistic and hit ratio, confirms that volatility improves the prediction of directional changes of returns. A simple trading strategy is utilized to provide practical improvement in investors’ market timing decisions.

Keywords

Binary regression model Volatility estimates Marginal probability Forecast comparison 

JEL Classification

G17 

Notes

Acknowledgements

We would like to thank the editor Markus Schmid and the anonymous referees for their constructive recommendations, which helped to improve the quality of this paper. In addition, we would like to thank the feedback received during prior drafts from colleagues Dr. Guneratne Wickremasinghe and Dr. Ranjtih Ihalanayake.

References

  1. Amemiya, T.: Qualitative response models: a survey. J. Econ. Lit. 19, 1483–1536 (1981)Google Scholar
  2. Anatolyev, S., Gospodinov, L.: Modelling financial return dynamics via decomposition. J. Bus. Econ. Stat. 28, 232–245 (2010)CrossRefGoogle Scholar
  3. Anderson, H.M., Vahid, F.: Forecasting the volatility of Australian stock returns: do common factors help? J. Bus. Econ. Stud. 25, 76–90 (2007)CrossRefGoogle Scholar
  4. Anneart, J., De Ceuster., M., Valckx, N.: Financial market volatility: informative in predicting recessions. Bank of Finland Discussion Papers vol. 14, Helsinki (2001)Google Scholar
  5. Beenstock, M., Chan, K.: Economic forces in the London stock market. Oxf. Bull. Econ. Stat. 50, 27–39 (1988)CrossRefGoogle Scholar
  6. Bekiros, S.D., Georgoutsos, D.: Non-linear dynamics in financial asset returns: the predictive power of the CBOE volatility index. Eur. J. Finance 14, 397–408 (2008)CrossRefGoogle Scholar
  7. Benson, K., Faff, R., Smith, T.: Fifty years of finance research in the Asia Pacific Basin. Account. Finance 54, 335–363 (2014)CrossRefGoogle Scholar
  8. Benson, K., Clarkson, P., Smith, T., Tutticci, I.: A review of accounting research in the Asia Pacific region. Australian Journal of Management 40, 36–88 (2015)CrossRefGoogle Scholar
  9. Bertram, W.K.: An empirical investigation of Australian Stock Exchange data. Physica A 341, 533–546 (2004)CrossRefGoogle Scholar
  10. Burdekin, R., Siklos, P.: Enter the dragon: Interactions between Chinese, US and Asia-Pacific equity markets 1995–2010. Pac. Basin Finance J. 20, 521–541 (2012)CrossRefGoogle Scholar
  11. Campbell, J., Thompson, S.: Predicting excess stock returns out of sample: can anything beat the historical average? Rev. Financ. Stud. 21, 1509–1531 (2008)CrossRefGoogle Scholar
  12. Chaudhuri, K., Smiles, S.: Stock market and aggregate economic activity: evidence from Australia. Appl. Financ. Econ. 14, 121–129 (2004)CrossRefGoogle Scholar
  13. Chen, N.-F., Roll, R., Ross, S.: Economic forces and the stock market. J. Bus. 59, 383–403 (1986)CrossRefGoogle Scholar
  14. Chen, C.H., Yu, W.C., Zivot, E.: Predicting stock volatility using after-hours information: evidence from the Nasdaq actively traded stocks. Int. J. Forecast. 28, 366–383 (2012)CrossRefGoogle Scholar
  15. Cheung, Y.W., Chinn, M., Pascual, G.: Empirical exchange rate models of the nineties: are any fit to survive? University of Santa Cruz, Department of Economics, working paper series qt12z9x4c5, Santa Cruz, CA (2003)Google Scholar
  16. Christoffersen, P., Diebold, F.: Financial assets returns, direction of change forecasting, and volatility dynamics. Manag. Sci. 52, 1273–1287 (2006)CrossRefGoogle Scholar
  17. Christoffersen, P., Diebold, F., Marino, R., Tay, R., Tse, Y.: Direction-of-change forecasts based on conditional variance, skewness and kurtosis dynamics: international evidence. J. Financ. Forecast. 1, 3–24 (2007)Google Scholar
  18. Durack, N., Durand, R., Maller, R.: A best choice among asset pricing models? The conditional capital asset pricing model in Australia. Account. Finance 44, 139–162 (2004)CrossRefGoogle Scholar
  19. Durand, R., Koh, S., Watson, I.: Who moved Asian-Pacific stock markets? A further consideration of the impact of the US and Japan. Aust. J. Manag. 26, 125–146 (2001)CrossRefGoogle Scholar
  20. Durand, R., Scott, D.: iShares Australia: a clinical study in international behavioral finance. Int. Rev. Financ. Anal. 12, 223–239 (2003)CrossRefGoogle Scholar
  21. Engle, R.F., Ng, V., Rothschild, M.: Asset pricing with a factor ARCH covariance structure: empirical estimates for Treasury bills. J. Econ. 45, 213–239 (1990)CrossRefGoogle Scholar
  22. Engle, R.F., Susmel, R.: Common volatility in international equity markets. J. Bus. Econ. Stat. 11, 167–176 (1993)Google Scholar
  23. Eun, C., Shim, S.: International transmission of stock market movements. J. Financ. Quant. Anal. 24, 241–256 (1989)CrossRefGoogle Scholar
  24. Faust, J., Wright, J.: Efficient prediction of excess returns. Rev. Econ. Stat. 93, 647–659 (2011)CrossRefGoogle Scholar
  25. Fama, E., French, K.: Common risk factors in the returns on stocks and bonds. J. Financ. Econ. 33, 3–56 (1993)CrossRefGoogle Scholar
  26. Ferson, W., Harvey, C.: The variations of economic risk premium. J. Polit. Econ. 99, 385–415 (1991)CrossRefGoogle Scholar
  27. Forbes, K., Rigobon, R.: No contagion, only interdependence: measuring stock market comovements. J. Finance 57, 2223–2261 (2002)CrossRefGoogle Scholar
  28. Gallo, G., Otranto, E.: Volatility spillovers, interdependence and comovements: a Markow switching approach. Comput. Stat. Data Anal. 52, 311–326 (2008)CrossRefGoogle Scholar
  29. Gencay, R.: Optimization of technical trading strategies and the profitability in security markets. Econ. Lett. 59, 249–254 (1998)CrossRefGoogle Scholar
  30. Ghosh, A., Saidi, R., Johnson, K.: Who moves the Asia-Pacific stock markets—US or Japan? Empirical evidence based on the theory of cointegration. Financ. Rev. 34, 159–169 (1999)CrossRefGoogle Scholar
  31. Groenewold, N., Fraser, P.: Share prices and macroeconomic factors. J. Bus. Finance Account. 24, 1367–1383 (1997)CrossRefGoogle Scholar
  32. Hong, Y., Chung, J.: Are the directions of stock price changes predictable?. Cornell University, Ithaca (2003)Google Scholar
  33. Humpe, A., Macmillan, P.: Can macroeconomic variables explain long term stock market movements? A comparison of the US and Japan. Appl. Financ. Econ. 19, 111–119 (2009)CrossRefGoogle Scholar
  34. Jagannathan, R., Wang, Z.: The conditional CAPM and the cross-section of expected returns. J. Finance 51, 3–53 (1996)CrossRefGoogle Scholar
  35. Kalev, P., Liu, W.-M., Pham, P., Jarnecic, E.: Public information arrival and volatility of intraday stock returns. J. Bank. Finance 28, 1441–1467 (2004)CrossRefGoogle Scholar
  36. Kauppi, H., Saikkonen, P.: Predicting US recessions with dynamic binary response models. Rev. Econ. Stat. 90, 777–791 (2008)CrossRefGoogle Scholar
  37. Kazi, M.: Systematic risk factors for Australian stock market returns: a cointegration analysis. Aust. Account. Bus. Finance J. 2, 89–101 (2008)Google Scholar
  38. Knight, F.H.: Risk, uncertainty, and profit. Hart, Schaffner and Marx, Houghton Mifflin Co, Boston (1921)Google Scholar
  39. Kuan, C.M., Liu, T.: Forecasting exchange rates using feed-forward and recurrent neural networks. J. Appl. Econ. 10, 347–364 (1995)CrossRefGoogle Scholar
  40. Kulendran, N., Wong, K.: Determinants versus composite leading indicators in predicting turning points in growth cycle. J. Travel Res. 50, 415–430 (2011)CrossRefGoogle Scholar
  41. Larsen, G.A., Wozniak, G.: Market timing can work in the real world. J. Portfolio Manag. 21, 74–81 (1995)CrossRefGoogle Scholar
  42. Lee, S.B., Kim, K.: Does the October 1987 crash strengthen the co-movements among national stock markets? Rev. Financ. Econ. 3, 89–102 (1993)CrossRefGoogle Scholar
  43. Leitch, G.J., Tanner, J.: Economic forecast evaluation: profit versus the conventional error measures. Am. Econ. Rev. 81, 580–590 (1991)Google Scholar
  44. Leung, M., Daouk, H., Chen, A.-S.: Forecasting stock indices: a comparison of classification and level estimation models. Int. J. Forecast. 16, 173–190 (2000)CrossRefGoogle Scholar
  45. Lo, A.W., MacKinlay, A.G.: Stock market prices do not follow random walks: evidence from a simple specification test. Rev. Financ. Stud. 1, 41–66 (1988)CrossRefGoogle Scholar
  46. Maddala, G.S.: Introduction to Econometrics. Wiley, Chichester (2001)Google Scholar
  47. Mian, G.M., Adam, C.M.: Volatility dynamics in high frequency financial data: an empirical investigation of the Australian equity returns. Appl. Financ. Econ. 11, 341–352 (2001)CrossRefGoogle Scholar
  48. Mukherjee, T.K., Naka, A.: Dynamic relations between macroeconomic variables and the Japanese stock market: an application of a vector error correction model. J. Financ. Res. 18, 223–237 (1995)CrossRefGoogle Scholar
  49. Nyberg, H.: Forecasting the direction of the US stock market with dynamic binary probit models. Int. J. Forecast. 27, 561–578 (2011)CrossRefGoogle Scholar
  50. Nyberg, H., Ponka, H.: International sign predictability of stock returns: the role of the United States. Econ. Model. 58, 323–338 (2016)CrossRefGoogle Scholar
  51. Peirson, G., Brown, R., Easton, S., Howard, P., Pinder, S.: Business Finance, 12th edn. McGraw-Hill, New York City (2015)Google Scholar
  52. Pesaran, M.H., Timmerman, A.: Predictability of stock return: robustness and economic significance. J. Finance 50, 1201–1228 (1995)CrossRefGoogle Scholar
  53. Pesaran, M.H., Timmerman, A.: Recursive modelling approach to predicting UK stock returns. Econ. J. 110, 159–191 (2000)CrossRefGoogle Scholar
  54. Pindyck, S.R., Rubinfeld, L.: Econometric Models and Economic Forecasts. McGraw-Hill, New York City (1998)Google Scholar
  55. Phylaktis, K.: Capital markets integration in the pacific-based region: an analysis of real interest rate linkage. Pac. Basin Finance J. 5, 195–213 (1997)CrossRefGoogle Scholar
  56. Ponka, H.: Real oil prices and the international sign predictability of stock returns. Finance Res. Lett. 17, 79–87 (2016)CrossRefGoogle Scholar
  57. Ragunathan, V., Faff, R., Brooks, R.D.: Correlations, business cycles and integration. J. Int. Financ. Mark. Inst. Money 9, 75–95 (1999)CrossRefGoogle Scholar
  58. Rapach, D.E., Strauss, J.K., Zhou, G.: International stock return predictability: what is the role of the United States? J. Finance 68, 1633–1662 (2013)CrossRefGoogle Scholar
  59. Rydberg, T.H., Shephard, N.: Dynamics of trade-by-trade price movements: decomposition and models. J. Financ. Econ. 1, 2–25 (2003)Google Scholar
  60. Shamsuddin, F.M.: Interest rate and exchange rate risk exposure of Australian banks: a note. Int. J. Bank. Finance 6, 128–138 (2009)Google Scholar
  61. Taylor, N.: A note on the importance of overnight information in risk management models. J. Bank. Finance 31, 161–180 (2007)CrossRefGoogle Scholar
  62. Theodossiou, P., Lee, U.: Mean and volatility spillovers across major national stock markets: further empirical evidence. J. Financ. Res. 16, 337–350 (1993)CrossRefGoogle Scholar
  63. Todorova, T., Soucek, M.: Overnight information flow and realized volatility forecasting. Finance Res. Lett. 11, 1–9 (2014)CrossRefGoogle Scholar
  64. Tsiakas, I.: Overnight information and stochastic volatility: a study of European and US stock exchanges. J. Bank. Finance 32, 251–268 (2008)CrossRefGoogle Scholar
  65. Wagner, J., Shellacs, S., Paul, R.: Market timing works where it matters most: in real the world. J. Portf. Manag. 18, 86–90 (1992)CrossRefGoogle Scholar
  66. White, H.: A reality check for data snooping. Econometrica 68, 1097–1126 (2000)CrossRefGoogle Scholar
  67. Womack, K.L.: Do brokerage analysts’ recommendations have investment value? J. Finance 51, 137–167 (1996)CrossRefGoogle Scholar

Copyright information

© Swiss Society for Financial Market Research 2019

Authors and Affiliations

  1. 1.Victoria University Business SchoolVictoria UniversityMelbourneAustralia

Personalised recommendations