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A Comparative Study of Stock Price Forecasting using nonlinear models

Author

Listed:
  • Lawrence Xaba

    (North West University)

  • Ntebogang Moroke

    (North West University)

  • Johnson Arkaah

    (North West University)

  • Charlemagne Pooe

    (South African Reserve Bank)

Abstract

This study compared the in-sample forecasting accuracy of three forecasting nonlinear models namely: the Smooth Transition Regression (STR) model, the Threshold Autoregressive (TAR) model and the Markov-switching Autoregressive (MS-AR) model. Data used was daily close stock prices of five banks in the South African banking sector and was obtained from the Johannesburg Stock Exchange (JSE). It covered the period from 2010 to 2012 with a total of 563 observations. Nonlinearity and nonstationarity tests used confirmed the validity of the assumptions of the study. The study used model selection criteria, SBC to select the optimal lag order and for the selection of appropriate models. The Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) served as the error measures in evaluating the forecasting ability of the models. The MS-AR models proved to perform well with lower error measures as compared to LSTR and TAR models in most cases.

Suggested Citation

  • Lawrence Xaba & Ntebogang Moroke & Johnson Arkaah & Charlemagne Pooe, 2015. "A Comparative Study of Stock Price Forecasting using nonlinear models," Proceedings of International Academic Conferences 2704207, International Institute of Social and Economic Sciences.
    • Handle: RePEc:sek:iacpro:2704207
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    File URL: https://iises.net/proceedings/18th-international-academic-conference-london/table-of-content/detail?cid=27&iid=138&rid=4207
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    References listed on IDEAS

    as
    1. Christopher CRUZ & Claire MAPA, 2013. "An Early Warning System For Inflation In The Philippines Using Markov-Switching And Logistic Regression Models," Theoretical and Practical Research in the Economic Fields, ASERS Publishing, vol. 4(2), pages 136-150.
    2. 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.
    3. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415, November.
    4. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Perez-Rodriguez, Jorge V. & Torra, Salvador & Andrada-Felix, Julian, 2005. "STAR and ANN models: forecasting performance on the Spanish "Ibex-35" stock index," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 490-509, June.
    7. Terence Tai-Leung Chong & Tau-Hing Lam & Melvin J. Hinich, 2009. "Are Nonlinear Trading Rules Profitable In The Chinese Stock Market?," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-20.
    8. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Katleho Daniel Makatjane & Edward Kagiso Molefe, 2020. "Predicting Regime Shifts in Johannesburg Stock Exchange All-Share Index (JSE-ALSI): A Markov-Switching Approach," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 8(2), pages 95-103.

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    More about this item

    Keywords

    Stock price; nonlinear time series models; error metrics;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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